case study about covid 19 introduction

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An Introduction to COVID-19

Simon james fong.

4 Department of Computer and Information Science, University of Macau, Taipa, Macau, China

Nilanjan Dey

5 Department of Information Technology, Techno International New Town, Kolkata, West Bengal India

Jyotismita Chaki

6 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu India

A novel coronavirus (CoV) named ‘2019-nCoV’ or ‘2019 novel coronavirus’ or ‘COVID-19’ by the World Health Organization (WHO) is in charge of the current outbreak of pneumonia that began at the beginning of December 2019 near in Wuhan City, Hubei Province, China [1–4]. COVID-19 is a pathogenic virus. From the phylogenetic analysis carried out with obtainable full genome sequences, bats occur to be the COVID-19 virus reservoir, but the intermediate host(s) has not been detected till now.

A Brief History of the Coronavirus Outbreak

A novel coronavirus (CoV) named ‘2019-nCoV’ or ‘2019 novel coronavirus’ or ‘COVID-19’ by the World Health Organization (WHO) is in charge of the current outbreak of pneumonia that began at the beginning of December 2019 near in Wuhan City, Hubei Province, China [ 1 – 4 ]. COVID-19 is a pathogenic virus. From the phylogenetic analysis carried out with obtainable full genome sequences, bats occur to be the COVID-19 virus reservoir, but the intermediate host(s) has not been detected till now. Though three major areas of work already are ongoing in China to advise our awareness of the pathogenic origin of the outbreak. These include early inquiries of cases with symptoms occurring near in Wuhan during December 2019, ecological sampling from the Huanan Wholesale Seafood Market as well as other area markets, and the collection of detailed reports of the point of origin and type of wildlife species marketed on the Huanan market and the destination of those animals after the market has been closed [ 5 – 8 ].

Coronaviruses mostly cause gastrointestinal and respiratory tract infections and are inherently categorized into four major types: Gammacoronavirus, Deltacoronavirus, Betacoronavirus and Alphacoronavirus [ 9 – 11 ]. The first two types mainly infect birds, while the last two mostly infect mammals. Six types of human CoVs have been formally recognized. These comprise HCoVHKU1, HCoV-OC43, Middle East Respiratory Syndrome coronavirus (MERS-CoV), Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) which is the type of the Betacoronavirus, HCoV229E and HCoV-NL63, which are the member of the Alphacoronavirus. Coronaviruses did not draw global concern until the 2003 SARS pandemic [ 12 – 14 ], preceded by the 2012 MERS [ 15 – 17 ] and most recently by the COVID-19 outbreaks. SARS-CoV and MERS-CoV are known to be extremely pathogenic and spread from bats to palm civets or dromedary camels and eventually to humans.

COVID-19 is spread by dust particles and fomites while close unsafe touch between the infector and the infected individual. Airborne distribution has not been recorded for COVID-19 and is not known to be a significant transmission engine based on empirical evidence; although it can be imagined if such aerosol-generating practices are carried out in medical facilities. Faecal spreading has been seen in certain patients, and the active virus has been reported in a small number of clinical studies [ 18 – 20 ]. Furthermore, the faecal-oral route does not seem to be a COVID-19 transmission engine; its function and relevance for COVID-19 need to be identified.

For about 18,738,58 laboratory-confirmed cases recorded as of 2nd week of April 2020, the maximum number of cases (77.8%) was between 30 and 69 years of age. Among the recorded cases, 21.6% are farmers or employees by profession, 51.1% are male and 77.0% are Hubei.

However, there are already many concerns regarding the latest coronavirus. Although it seems to be transferred to humans by animals, it is important to recognize individual animals and other sources, the path of transmission, the incubation cycle, and the features of the susceptible community and the survival rate. Nonetheless, very little clinical knowledge on COVID-19 disease is currently accessible and details on age span, the animal origin of the virus, incubation time, outbreak curve, viral spectroscopy, dissemination pathogenesis, autopsy observations, and any clinical responses to antivirals are lacking among the serious cases.

How Different and Deadly COVID-19 is Compared to Plagues in History

COVID-19 has reached to more than 150 nations, including China, and has caused WHO to call the disease a worldwide pandemic. By the time of 2nd week of April 2020, this COVID-19 cases exceeded 18,738,58, although more than 1,160,45 deaths were recorded worldwide and United States of America became the global epicentre of coronavirus. More than one-third of the COVID-19 instances are outside of China. Past pandemics that have existed in the past decade or so, like bird flu, swine flu, and SARS, it is hard to find out the comparison between those pandemics and this coronavirus. Following is a guide to compare coronavirus with such diseases and recent pandemics that have reformed the world community.

Coronavirus Versus Seasonal Influenza

Influenza, or seasonal flu, occurs globally every year–usually between December and February. It is impossible to determine the number of reports per year because it is not a reportable infection (so no need to be recorded to municipality), so often patients with minor symptoms do not go to a physician. Recent figures placed the Rate of Case Fatality at 0.1% [ 21 – 23 ].

There are approximately 3–5 million reports of serious influenza a year, and about 250,000–500,000 deaths globally. In most developed nations, the majority of deaths arise in persons over 65 years of age. Moreover, it is unsafe for pregnant mothers, children under 59 months of age and individuals with serious illnesses.

The annual vaccination eliminates infection and severe risks in most developing countries but is nevertheless a recognized yet uncomfortable aspect of the season.

In contrast to the seasonal influenza, coronavirus is not so common, has led to fewer cases till now, has a higher rate of case fatality and has no antidote.

Coronavirus Versus Bird Flu (H5N1 and H7N9)

Several cases of bird flu have existed over the years, with the most severe in 2013 and 2016. This is usually from two separate strains—H5N1 and H7N9 [ 24 – 26 ].

The H7N9 outbreak in 2016 accounted for one-third of all confirmed human cases but remained confined relative to both coronavirus and other pandemics/outbreak cases. After the first outbreak, about 1,233 laboratory-confirmed reports of bird flu have occurred. The disease has a Rate of Case Fatality of 20–40%.

Although the percentage is very high, the blowout from individual to individual is restricted, which, in effect, has minimized the number of related deaths. It is also impossible to monitor as birds do not necessarily expire from sickness.

In contrast to the bird flu, coronavirus becomes more common, travels more quickly through human to human interaction, has an inferior cardiothoracic ratio, resulting in further total fatalities and spread from the initial source.

Coronavirus Versus Ebola Epidemic

The Ebola epidemic of 2013 was primarily centred in 10 nations, including Sierra Leone, Guinea and Liberia have the greatest effects, but the extremely high Case Fatality Rate of 40% has created this as a significant problem for health professionals nationwide [ 27 – 29 ].

Around 2013 and 2016, there were about 28,646 suspicious incidents and about 11,323 fatalities, although these are expected to be overlooked. Those who survived from the original epidemic may still become sick months or even years later, because the infection may stay inactive for prolonged periods. Thankfully, a vaccination was launched in December 2016 and is perceived to be effective.

In contrast to the Ebola, coronavirus is more common globally, has caused in fewer fatalities, has a lesser case fatality rate, has no reported problems during treatment and after recovery, does not have an appropriate vaccination.

Coronavirus Versus Camel Flu (MERS)

Camel flu is a misnomer–though camels have MERS antibodies and may have been included in the transmission of the disease; it was originally transmitted to humans through bats [ 30 – 32 ]. Like Ebola, it infected only a limited number of nations, i.e. about 27, but about 858 fatalities from about 2,494 laboratory-confirmed reports suggested that it was a significant threat if no steps were taken in place to control it.

In contrast to the camel flu, coronavirus is more common globally, has occurred more fatalities, has a lesser case fatality rate, and spreads more easily among humans.

Coronavirus Versus Swine Flu (H1N1)

Swine flu is the same form of influenza that wiped 1.7% of the world population in 1918. This was deemed a pandemic again in June 2009 an approximately-21% of the global population infected by this [ 33 – 35 ].

Thankfully, the case fatality rate is substantially lower than in the last pandemic, with 0.1%–0.5% of events ending in death. About 18,500 of these fatalities have been laboratory-confirmed, but statistics range as high as 151,700–575,400 worldwide. 50–80% of severe occurrences have been reported in individuals with chronic illnesses like asthma, obesity, cardiovascular diseases and diabetes.

In contrast to the swine flu, coronavirus is not so common, has caused fewer fatalities, has more case fatality rate, has a longer growth time and less impact on young people.

Coronavirus Versus Severe Acute Respiratory Syndrome (SARS)

SARS was discovered in 2003 as it spread from bats to humans resulted in about 774 fatalities. By May there were eventually about 8,100 reports across 17 countries, with a 15% case fatality rate. The number is estimated to be closer to 9.6% as confirmed cases are counted, with 0.9% cardiothoracic ratio for people aged 20–29, rising to 28% for people aged 70–79. Similar to coronavirus, SARS had bad results for males than females in all age categories [ 36 – 38 ].

Coronavirus is more common relative to SARS, which ended in more overall fatalities, lower case fatality rate, the even higher case fatality rate in older ages, and poorer results for males.

Coronavirus Versus Hong Kong Flu (H3N2)

The Hong Kong flu pandemic erupted on 13 July 1968, with 1–4 million deaths globally by 1969. It was one of the greatest flu pandemics of the twentieth century, but thankfully the case fatality rate was smaller than the epidemic of 1918, resulting in fewer fatalities overall. That may have been attributed to the fact that citizens had generated immunity owing to a previous epidemic in 1957 and to better medical treatment [ 39 ].

In contrast to the Hong Kong flu, coronavirus is not so common, has caused in fewer fatalities and has a higher case fatality rate.

Coronavirus Versus Spanish Flu (H1N1)

The 1918 Spanish flu pandemic was one of the greatest occurrences of recorded history. During the first year of the pandemic, lifespan in the US dropped by 12 years, with more civilians killed than HIV/AIDS in 24 h [ 40 – 42 ].

Regardless of the name, the epidemic did not necessarily arise in Spain; wartime censors in Germany, the United States, the United Kingdom and France blocked news of the disease, but Spain did not, creating the misleading perception that more cases and fatalities had occurred relative to its neighbours

This strain of H1N1 eventually affected more than 500 million men, or 27% of the world’s population at the moment, and had deaths of between 40 and 50 million. At the end of 1920, 1.7% of the world’s people had expired of this illness, including an exceptionally high death rate for young adults aged between 20 and 40 years.

In contrast to the Spanish flu, coronavirus is not so common, has caused in fewer fatalities, has a higher case fatality rate, is more harmful to older ages and is less risky for individuals aged 20–40 years.

Coronavirus Versus Common Cold (Typically Rhinovirus)

Common cold is the most common illness impacting people—Typically, a person suffers from 2–3 colds each year and the average kid will catch 6–8 during the similar time span. Although there are more than 200 cold-associated virus types, infections are uncommon and fatalities are very rare and typically arise mainly in extremely old, extremely young or immunosuppressed cases [ 43 , 44 ].

In contrast to the common cold, coronavirus is not so prevalent, causes more fatalities, has more case fatality rate, is less infectious and is less likely to impact small children.

Reviews of Online Portals and Social Media for Epidemic Information Dissemination

As COVID-19 started to propagate across the globe, the outbreak contributed to a significant change in the broad technology platforms. Where they once declined to engage in the affairs of their systems, except though the possible danger to public safety became obvious, the advent of a novel coronavirus placed them in a different interventionist way of thought. Big tech firms and social media are taking concrete steps to guide users to relevant, credible details on the virus [ 45 – 48 ]. And some of the measures they’re doing proactively. Below are a few of them.

Facebook started adding a box in the news feed that led users to the Centers for Disease Control website regarding COVID-19. It reflects a significant departure from the company’s normal strategy of placing items in the News Feed. The purpose of the update, after all, is personalization—Facebook tries to give the posts you’re going to care about, whether it is because you’re connected with a person or like a post. In the virus package, Facebook has placed a remarkable algorithmic thumb on the scale, potentially pushing millions of people to accurate, authenticated knowledge from a reputable source.

Similar initiatives have been adopted by Twitter. Searching for COVID-19 will carry you to a page highlighting the latest reports from public health groups and credible national news outlets. The search also allows for common misspellings. Twitter has stated that although Russian-style initiatives to cause discontent by large-scale intelligence operations have not yet been observed, a zero-tolerance approach to network exploitation and all other attempts to exploit their service at this crucial juncture will be expected. The problem has the attention of the organization. It also offers promotional support to public service agencies and other non-profit groups.

Google has made a step in making it better for those who choose to operate or research from home, offering specialized streaming services to all paying G Suite customers. Google also confirmed that free access to ‘advanced’ Hangouts Meet apps will be rolled out to both G Suite and G Suite for Education clients worldwide through 1st July. It ensures that companies can hold meetings of up to 250 people, broadcast live to up to about 100,000 users within a single network, and archive and export meetings to Google Drive. Usually, Google pays an additional $13 per person per month for these services in comparison to G Suite’s ‘enterprise’ membership, which adds up to a total of about $25 per client each month.

Microsoft took a similar move, introducing the software ‘Chat Device’ to help public health and protection in the coronavirus epidemic, which enables collaborative collaboration via video and text messaging. There’s an aspect of self-interest in this. Tech firms are offering out their goods free of charge during periods of emergency for the same purpose as newspapers are reducing their paywalls: it’s nice to draw more paying consumers.

Pinterest, which has introduced much of the anti-misinformation strategies that Facebook and Twitter are already embracing, is now restricting the search results for ‘coronavirus’, ‘COVID-19’ and similar words for ‘internationally recognized health organizations’.

Google-owned YouTube, traditionally the most conspiratorial website, has recently introduced a connection to the World Health Organization virus epidemic page to the top of the search results. In the early days of the epidemic, BuzzFeed found famous coronavirus conspiratorial videos on YouTube—especially in India, where one ‘explain’ with a false interpretation of the sources of the disease racketeered 13 million views before YouTube deleted it. Yet in the United States, conspiratorial posts regarding the illness have failed to gain only 1 million views.

That’s not to suggest that misinformation doesn’t propagate on digital platforms—just as it travels through the broader Internet, even though interaction with friends and relatives. When there’s a site that appears to be under-performing in the global epidemic, it’s Facebook-owned WhatsApp, where the Washington Post reported ‘a torrent of disinformation’ in places like Nigeria, Indonesia, Peru, Pakistan and Ireland. Given the encrypted existence of the app, it is difficult to measure the severity of the problem. Misinformation is also spread in WhatsApp communities, where participation is restricted to about 250 individuals. Knowledge of one category may be readily exchanged with another; however, there is a considerable amount of complexity of rotating several groups to peddle affected healing remedies or propagate false rumours.

Preventative Measures and Policies Enforced by the World Health Organization (WHO) and Different Countries

Coronavirus is already an ongoing epidemic, so it is necessary to take precautions to minimize both the risk of being sick and the transmission of the disease.

WHO Advice [ 49 ]

• Wash hands regularly with alcohol-based hand wash or soap and water.
• Preserve contact space (at least 1 m/3 feet between you and someone who sneezes or coughs).
• Don’t touch your nose, head and ears.
• Cover your nose and mouth as you sneeze or cough, preferably with your bent elbow or tissue.
• Try to find early medical attention if you have fatigue, cough and trouble breathing.
• Take preventive precautions if you are in or have recently go to places where coronavirus spreads.

The first person believed to have become sick because of the latest virus was near in Wuhan on 1 December 2019. A formal warning of the epidemic was released on 31 December. The World Health Organization was informed of the epidemic on the same day. Through 7 January, the Chinese Government addressed the avoidance and regulation of COVID-19. A curfew was declared on 23 January to prohibit flying in and out of Wuhan. Private usage of cars has been banned in the region. Chinese New Year (25 January) festivities have been cancelled in many locations [ 50 ].

On 26 January, the Communist Party and the Government adopted more steps to contain the COVID-19 epidemic, including safety warnings for travellers and improvements to national holidays. The leading party has agreed to prolong the Spring Festival holiday to control the outbreak. Universities and schools across the world have already been locked down. Many steps have been taken by the Hong Kong and Macau governments, in particular concerning schools and colleges. Remote job initiatives have been placed in effect in many regions of China. Several immigration limits have been enforced.

Certain counties and cities outside Hubei also implemented travel limits. Public transit has been changed and museums in China have been partially removed. Some experts challenged the quality of the number of cases announced by the Chinese Government, which constantly modified the way coronavirus cases were recorded.

Italy, a member state of the European Union and a popular tourist attraction, entered the list of coronavirus-affected nations on 30 January, when two positive cases in COVID-19 were identified among Chinese tourists. Italy has the largest number of coronavirus infections both in Europe and outside of China [ 51 ].

Infections, originally limited to northern Italy, gradually spread to all other areas. Many other nations in Asia, Europe and the Americas have tracked their local cases to Italy. Several Italian travellers were even infected with coronavirus-positive in foreign nations.

Late in Italy, the most impacted coronavirus cities and counties are Lombardia, accompanied by Veneto, Emilia-Romagna, Marche and Piedmonte. Milan, the second most populated city in Italy, is situated in Lombardy. Other regions in Italy with coronavirus comprised Campania, Toscana, Liguria, Lazio, Sicilia, Friuli Venezia Giulia, Umbria, Puglia, Trento, Abruzzo, Calabria, Molise, Valle d’Aosta, Sardegna, Bolzano and Basilicata.

Italy ranks 19th of the top 30 nations getting high-risk coronavirus airline passengers in China, as per WorldPop’s provisional study of the spread of COVID-19.

The Italian State has taken steps like the inspection and termination of large cultural activities during the early days of the coronavirus epidemic and has gradually declared the closing of educational establishments and airport hygiene/disinfection initiatives.

The Italian National Institute of Health suggested social distancing and agreed that the broader community of the country’s elderly is a problem. In the meantime, several other nations, including the US, have recommended that travel to Italy should be avoided temporarily, unless necessary.

The Italian government has declared the closing (quarantine) of the impacted areas in the northern region of the nation so as not to spread to the rest of the world. Italy has declared the immediate suspension of all to-and-fro air travel with China following coronavirus discovery by a Chinese tourist to Italy. Italian airlines, like Ryan Air, have begun introducing protective steps and have begun calling for the declaration forms to be submitted by passengers flying to Poland, Slovakia and Lithuania.

The Italian government first declined to permit fans to compete in sporting activities until early April to prevent the potential transmission of coronavirus. The step ensured players of health and stopped event cancellations because of coronavirus fears. Two days of the declaration, the government cancelled all athletic activities owing to the emergence of the outbreak asking for an emergency. Sports activities in Veneto, Lombardy and Emilia-Romagna, which recorded coronavirus-positive infections, were confirmed to be temporarily suspended. Schools and colleges in Italy have also been forced to shut down.

Iran announced the first recorded cases of SARS-CoV-2 infection on 19 February when, as per the Medical Education and Ministry of Health, two persons died later that day. The Ministry of Islamic Culture and Guidance has declared the cancellation of all concerts and other cultural activities for one week. The Medical Education and Ministry of Health has also declared the closing of universities, higher education colleges and schools in many cities and regions. The Department of Sports and Culture has taken action to suspend athletic activities, including football matches [ 52 ].

On 2 March 2020, the government revealed plans to train about 300,000 troops and volunteers to fight the outbreak of the epidemic, and also send robots and water cannons to clean the cities. The State also developed an initiative and a webpage to counter the epidemic. On 9 March 2020, nearly 70,000 inmates were immediately released from jail owing to the epidemic, presumably to prevent the further dissemination of the disease inside jails. The Revolutionary Guards declared a campaign on 13 March 2020 to clear highways, stores and public areas in Iran. President Hassan Rouhani stated on 26 February 2020 that there were no arrangements to quarantine areas impacted by the epidemic and only persons should be quarantined. The temples of Shia in Qom stayed open to pilgrims.

South Korea

On 20 January, South Korea announced its first occurrence. There was a large rise in cases on 20 February, possibly due to the meeting in Daegu of a progressive faith community recognized as the Shincheonji Church of Christ. Any citizens believed that the hospital was propagating the disease. As of 22 February, 1,261 of the 9,336 members of the church registered symptoms. A petition was distributed calling for the abolition of the church. More than 2,000 verified cases were registered on 28 February, increasing to 3,150 on 29 February [ 53 ].

Several educational establishments have been partially closing down, including hundreds of kindergartens in Daegu and many primary schools in Seoul. As of 18 February, several South Korean colleges had confirmed intentions to delay the launch of the spring semester. That included 155 institutions deciding to postpone the start of the semester by two weeks until 16 March, and 22 institutions deciding to delay the start of the semester by one week until 9 March. Also, on 23 February 2020, all primary schools, kindergartens, middle schools and secondary schools were declared to postpone the start of the semester from 2 March to 9 March.

South Korea’s economy is expected to expand by 1.9%, down from 2.1%. The State has given 136.7 billion won funding to local councils. The State has also coordinated the purchase of masks and other sanitary supplies. Entertainment Company SM Entertainment is confirmed to have contributed five hundred million won in attempts to fight the disease.

In the kpop industry, the widespread dissemination of coronavirus within South Korea has contributed to the cancellation or postponement of concerts and other programmes for kpop activities inside and outside South Korea. For instance, circumstances such as the cancellation of the remaining Asian dates and the European leg for the Seventeen’s Ode To You Tour on 9 February 2020 and the cancellation of all Seoul dates for the BTS Soul Tour Map. As of 15 March, a maximum of 136 countries and regions provided entry restrictions and/or expired visas for passengers from South Korea.

The overall reported cases of coronavirus rose significantly in France on 12 March. The areas with reported cases include Paris, Amiens, Bordeaux and Eastern Haute-Savoie. The first coronaviral death happened in France on 15 February, marking it the first death in Europe. The second death of a 60-year-old French national in Paris was announced on 26 February [ 54 ].

On February 28, fashion designer Agnès B. (not to be mistaken with Agnès Buzyn) cancelled fashion shows at the Paris Fashion Week, expected to continue until 3 March. On a subsequent day, the Paris half-marathon, planned for Sunday 1 March with 44,000 entrants, was postponed as one of a series of steps declared by Health Minister Olivier Véran.

On 13 March, the Ligue de Football Professional disbanded Ligue 1 and Ligue 2 (France’s tier two professional divisions) permanently due to safety threats.

Germany has a popular Regional Pandemic Strategy detailing the roles and activities of the health care system participants in the case of a significant outbreak. Epidemic surveillance is carried out by the federal government, like the Robert Koch Center, and by the German governments. The German States have their preparations for an outbreak. The regional strategy for the treatment of the current coronavirus epidemic was expanded by March 2020. Four primary goals are contained in this plan: (1) to minimize mortality and morbidity; (2) to guarantee the safety of sick persons; (3) to protect vital health services and (4) to offer concise and reliable reports to decision-makers, the media and the public [ 55 ].

The programme has three phases that may potentially overlap: (1) isolation (situation of individual cases and clusters), (2) safety (situation of further dissemination of pathogens and suspected causes of infection), (3) prevention (situation of widespread infection). So far, Germany has not set up border controls or common health condition tests at airports. Instead, while at the isolation stage-health officials are concentrating on recognizing contact individuals that are subject to specific quarantine and are tracked and checked. Specific quarantine is regulated by municipal health authorities. By doing so, the officials are seeking to hold the chains of infection small, contributing to decreased clusters. At the safety stage, the policy should shift to prevent susceptible individuals from being harmed by direct action. By the end of the day, the prevention process should aim to prevent cycles of acute treatment to retain emergency facilities.

United States

The very first case of coronavirus in the United States was identified in Washington on 21 January 2020 by an individual who flew to Wuhan and returned to the United States. The second case was recorded in Illinois by another individual who had travelled to Wuhan. Some of the regions with reported novel coronavirus infections in the US are California, Arizona, Connecticut, Illinois, Texas, Wisconsin and Washington [ 56 ].

As the epidemic increased, requests for domestic air travel decreased dramatically. By 4 March, U.S. carriers, like United Airlines and JetBlue Airways, started growing their domestic flight schedules, providing generous unpaid leave to workers and suspending recruits.

A significant number of universities and colleges cancelled classes and reopened dormitories in response to the epidemic, like Cornell University, Harvard University and the University of South Carolina.

On 3 March 2020, the Federal Reserve reduced its goal interest rate from 1.75% to 1.25%, the biggest emergency rate cut following the 2008 global financial crash, in combat the effect of the recession on the American economy. In February 2020, US businesses, including Apple Inc. and Microsoft, started to reduce sales projections due to supply chain delays in China caused by the COVID-19.

The pandemic, together with the subsequent financial market collapse, also contributed to greater criticism of the crisis in the United States. Researchers disagree about when a recession is likely to take effect, with others suggesting that it is not unavoidable, while some claim that the world might already be in recession. On 3 March, Federal Reserve Chairman Jerome Powell reported a 0.5% (50 basis point) interest rate cut from the coronavirus in the context of the evolving threats to economic growth.

When ‘social distance’ penetrated the national lexicon, disaster response officials promoted the cancellation of broad events to slow down the risk of infection. Technical conferences like E3 2020, Apple Inc.’s Worldwide Developers Conference (WWDC), Google I/O, Facebook F8, and Cloud Next and Microsoft’s MVP Conference have been either having replaced or cancelled in-person events with internet streaming events.

On February 29, the American Physical Society postponed its annual March gathering, planned for March 2–6 in Denver, Colorado, even though most of the more than 11,000 physicist attendees already had arrived and engaged in the pre-conference day activities. On March 6, the annual South to Southwest (SXSW) seminar and festival planned to take place from March 13–22 in Austin, Texas, was postponed after the city council announced a local disaster and forced conferences to be shut down for the first time in 34 years.

Four of North America’s major professional sports leagues—the National Hockey League (NHL), National Basketball Association (NBA), Major League Soccer (MLS) and Major League Baseball (MLB) —jointly declared on March 9 that they would all limit the media access to player accommodations (such as locker rooms) to control probable exposure.

Emergency Funding to Fight the COVID-19

COVID-19 pandemic has become a common international concern. Different countries are donating funds to fight against it [ 57 – 60 ]. Some of them are mentioned here.

China has allocated about 110.48 billion yuan ($15.93 billion) in coronavirus-related funding.

Foreign Minister Mohammad Javad Zarif said that Iran has requested the International Monetary Fund (IMF) of about $5 billion in emergency funding to help to tackle the coronavirus epidemic that has struck the Islamic Republic hard.

President Donald Trump approved the Emergency Supplementary Budget Bill to support the US response to a novel coronavirus epidemic. The budget plan would include about $8.3 billion in discretionary funding to local health authorities to promote vaccine research for production. Trump originally requested just about $2 billion to combat the epidemic, but Congress quadrupled the number in its version of the bill. Mr. Trump formally announced a national emergency that he claimed it will give states and territories access to up to about $50 billion in federal funding to tackle the spread of the coronavirus outbreak.

California politicians approved a plan to donate about $1 billion on the state’s emergency medical responses as it readies hospitals to fight an expected attack of patients because of the COVID-19 pandemic. The plans, drawn up rapidly in reaction to the dramatic rise in reported cases of the virus, would include the requisite funds to establish two new hospitals in California, with the assumption that the state may not have the resources to take care of the rise in patients. The bill calls for an immediate response of about $500 million from the State General Fund, with an additional about $500 million possible if requested.

India committed about $10 million to the COVID-19 Emergency Fund and said it was setting up a rapid response team of physicians for the South Asian Association for Regional Cooperation (Saarc) countries.

South Korea unveiled an economic stimulus package of about 11.7 trillion won ($9.8 billion) to soften the effects of the biggest coronavirus epidemic outside China as attempts to curb the disease exacerbate supply shortages and drain demand. Of the 11,7 trillion won expected, about 3.2 trillion won would cover up the budget shortfall, while an additional fiscal infusion of about 8.5 trillion won. An estimated 10.3 trillion won in government bonds will be sold this year to fund the extra expenditure. About 2.3 trillion won will be distributed to medical establishments and would support quarantine operations, with another 3.0 trillion won heading to small and medium-sized companies unable to pay salaries to their employees and child care supports.

The Swedish Parliament announced a set of initiatives costing more than 300 billion Swedish crowns ($30.94 billion) to help the economy in the view of the coronavirus pandemic. The plan contained steps like the central government paying the entire expense of the company’s sick leave during April and May, and also the high cost of compulsory redundancies owing to the crisis.

In consideration of the developing scenario, an updating of this strategy is planned to take place before the end of March and will recognize considerably greater funding demands for the country response, R&D and WHO itself.

Artificial Intelligence, Data Science and Technological Solutions Against COVID-19

These days, Artificial Intelligence (AI) takes a major role in health care. Throughout a worldwide pandemic such as the COVID-19, technology, artificial intelligence and data analytics have been crucial in helping communities cope successfully with the epidemic [ 61 – 65 ]. Through the aid of data mining and analytical modelling, medical practitioners are willing to learn more about several diseases.

Public Health Surveillance

The biggest risk of coronavirus is the level of spreading. That’s why policymakers are introducing steps like quarantines around the world because they can’t adequately monitor local outbreaks. One of the simplest measures to identify ill patients through the study of CCTV images that are still around us and to locate and separate individuals that have serious signs of the disease and who have touched and disinfected the related surfaces. Smartphone applications are often used to keep a watch on people’s activities and to assess whether or not they have come in touch with an infected human.

Remote Biosignal Measurement

Many of the signs such as temperature or heartbeat are very essential to overlook and rely entirely on the visual image that may be misleading. However, of course, we can’t prevent someone from checking their blood pressure, heart or temperature. Also, several advances in computer vision can predict pulse and blood pressure based on facial skin examination. Besides, there are several advances in computer vision that can predict pulse and blood pressure based on facial skin examination.

Access to public records has contributed to the development of dashboards that constantly track the virus. Several companies are designing large data dashboards. Face recognition and infrared temperature monitoring technologies have been mounted in all major cities. Chinese AI companies including Hanwang Technology and SenseTime have reported having established a special facial recognition system that can correctly identify people even though they are covered.

IoT and Wearables

Measurements like pulse are much more natural and easier to obtain from tracking gadgets like activity trackers and smartwatches that nearly everybody has already. Some work suggests that the study of cardiac activity and its variations from the standard will reveal early signs of influenza and, in this case, coronavirus.

Chatbots and Communication

Apart from public screening, people’s knowledge and self-assessment may also be used to track their health. If you can check your temperature and pulse every day and monitor your coughs time-to-time, you can even submit that to your record. If the symptoms are too serious, either an algorithm or a doctor remotely may prescribe a person to stay home, take several other preventive measures, or recommend a visit from the doctor.

Al Jazeera announced that China Mobile had sent text messages to state media departments, telling them about the citizens who had been affected. The communications contained all the specifics of the person’s travel history.

Tencent runs WeChat, and via it, citizens can use free online health consultation services. Chatbots have already become important connectivity platforms for transport and tourism service providers to keep passengers up-to-date with the current transport protocols and disturbances.

Social Media and Open Data

There are several people who post their health diary with total strangers via Facebook or Twitter. Such data becomes helpful for more general research about how far the epidemic has progressed. For consumer knowledge, we may even evaluate the social network group to attempt to predict what specific networks are at risk of being viral.

Canadian company BlueDot analyses far more than just social network data: for instance, global activities of more than four billion passengers on international flights per year; animal, human and insect population data; satellite environment data and relevant knowledge from health professionals and journalists, across 100,000 news posts per day covering 65 languages. This strategy was so successful that the corporation was able to alert clients about coronavirus until the World Health Organization and the Centers for Disease Control and Prevention notified the public.

Automated Diagnostics

COVID-19 has brought up another healthcare issue today: it will not scale when the number of patients increases exponentially (actually stressed doctors are always doing worse) and the rate of false-negative diagnosis remains very high. Machine learning therapies don’t get bored and scale simply by growing computing forces.

Baidu, the Chinese Internet company, has made the Lineatrfold algorithm accessible to the outbreak-fighting teams, according to the MIT Technology Review. Unlike HIV, Ebola and Influenza, COVID-19 has just one strand of RNA and it can mutate easily. The algorithm is also simpler than other algorithms that help to determine the nature of the virus. Baidu has also developed software to efficiently track large populations. It has also developed an Ai-powered infrared device that can detect a difference in the body temperature of a human. This is currently being used in Beijing’s Qinghe Railway Station to classify possibly contaminated travellers where up to 200 individuals may be checked in one minute without affecting traffic movement, reports the MIT Review.

Singapore-based Veredus Laboratories, a supplier of revolutionary molecular diagnostic tools, has currently announced the launch of the VereCoV detector package, a compact Lab-on-Chip device able to detect MERS-CoV, SARS-CoV and COVID-19, i.e. Wuhan Coronavirus, in a single study.

The VereCoV identification package is focused on VereChip technology, a Lab-on-Chip device that incorporates two important molecular biological systems, Polymerase Chain Reaction (PCR) and a microarray, which will be able to classify and distinguish within 2 h MERS-CoV, SARS-CoV and COVID-19 with high precision and responsiveness.

This is not just the medical activities of healthcare facilities that are being charged, but also the corporate and financial departments when they cope with the increase in patients. Ant Financials’ blockchain technology helps speed-up the collection of reports and decreases the number of face-to-face encounters with patients and medical personnel.

Companies like the Israeli company Sonovia are aiming to provide healthcare systems and others with face masks manufactured from their anti-pathogenic, anti-bacterial cloth that depends on metal-oxide nanoparticles.

Drug Development Research

Aside from identifying and stopping the transmission of pathogens, the need to develop vaccinations on a scale is also needed. One of the crucial things to make that possible is to consider the origin and essence of the virus. Google’s DeepMind, with their expertise in protein folding research, has rendered a jump in identifying the protein structure of the virus and making it open-source.

BenevolentAI uses AI technologies to develop medicines that will combat the most dangerous diseases in the world and is also working to promote attempts to cure coronavirus, the first time the organization has based its product on infectious diseases. Within weeks of the epidemic, it used its analytical capability to recommend new medicines that might be beneficial.

Robots are not vulnerable to the infection, and they are used to conduct other activities, like cooking meals in hospitals, doubling up as waiters in hotels, spraying disinfectants and washing, selling rice and hand sanitizers, robots are on the front lines all over to deter coronavirus spread. Robots also conduct diagnostics and thermal imaging in several hospitals. Shenzhen-based firm Multicopter uses robotics to move surgical samples. UVD robots from Blue Ocean Robotics use ultraviolet light to destroy viruses and bacteria separately. In China, Pudu Technology has introduced its robots, which are usually used in the cooking industry, to more than 40 hospitals throughout the region. According to the Reuters article, a tiny robot named Little Peanut is distributing food to passengers who have been on a flight from Singapore to Hangzhou, China, and are presently being quarantined in a hotel.

Colour Coding

Using its advanced and vast public service monitoring network, the Chinese government has collaborated with software companies Alibaba and Tencent to establish a colour-coded health ranking scheme that monitors millions of citizens every day. The mobile device was first introduced in Hangzhou with the cooperation of Alibaba. This applies three colours to people—red, green or yellow—based on their transportation and medical records. Tencent also developed related applications in the manufacturing centre of Shenzhen.

The decision of whether an individual will be quarantined or permitted in public spaces is dependent on the colour code. Citizens will sign into the system using pay wallet systems such as Alibaba’s Alipay and Ant’s wallet. Just those citizens who have been issued a green colour code will be permitted to use the QR code in public spaces at metro stations, workplaces, and other public areas. Checkpoints are in most public areas where the body temperature and the code of individual are tested. This programme is being used by more than 200 Chinese communities and will eventually be expanded nationwide.

In some of the seriously infected regions where people remain at risk of contracting the infection, drones are used to rescue. One of the easiest and quickest ways to bring emergency supplies where they need to go while on an epidemic of disease is by drone transportation. Drones carry all surgical instruments and patient samples. This saves time, improves the pace of distribution and reduces the chance of contamination of medical samples. Drones often operate QR code placards that can be checked to record health records. There are also agricultural drones distributing disinfectants in the farmland. Drones, operated by facial recognition, are often used to warn people not to leave their homes and to chide them for not using face masks. Terra Drone uses its unmanned drones to move patient samples and vaccination content at reduced risk between the Xinchang County Disease Control Center and the People’s Hospital. Drones are often used to monitor public areas, document non-compliance with quarantine laws and thermal imaging.

Autonomous Vehicles

At a period of considerable uncertainty to medical professionals and the danger to people-to-people communication, automated vehicles are proving to be of tremendous benefit in the transport of vital products, such as medications and foodstuffs. Apollo, the Baidu Autonomous Vehicle Project, has joined hands with the Neolix self-driving company to distribute food and supplies to a big hospital in Beijing. Baidu Apollo has also provided its micro-car packages and automated cloud driving systems accessible free of charge to virus-fighting organizations.

Idriverplus, a Chinese self-driving organization that runs electrical street cleaning vehicles, is also part of the project. The company’s signature trucks are used to clean hospitals.

This chapter provides an introduction to the coronavirus outbreak (COVID-19). A brief history of this virus along with the symptoms are reported in this chapter. Then the comparison between COVID-19 and other plagues like seasonal influenza, bird flu (H5N1 and H7N9), Ebola epidemic, camel flu (MERS), swine flu (H1N1), severe acute respiratory syndrome, Hong Kong flu (H3N2), Spanish flu and the common cold are included in this chapter. Reviews of online portal and social media like Facebook, Twitter, Google, Microsoft, Pinterest, YouTube and WhatsApp concerning COVID-19 are reported in this chapter. Also, the preventive measures and policies enforced by WHO and different countries such as China, Italy, Iran, South Korea, France, Germany and the United States for COVID-19 are included in this chapter. Emergency funding provided by different countries to fight the COVID-19 is mentioned in this chapter. Lastly, artificial intelligence, data science and technological solutions like public health surveillance, remote biosignal measurement, IoT and wearables, chatbots and communication, social media and open data, automated diagnostics, drug development research, robotics, colour coding, drones and autonomous vehicles are included in this chapter.

Contact patterns between index patients and their close contacts and assessing risk for COVID-19 transmission during different exposure time windows: a large retrospective observational study of 450 770 close contacts in Shanghai

Yaxu Zheng ,

Xiaohuan Gong ,

Chenyan Jiang ,

Shenghua Mao ,

Sheng Lin ,

Bihong Jin ,

Dechuan Kong ,

Genming Zhao ,

https://doi.org/ 10.1136/bmjph-2023-000154

Introduction To characterise age-mixing patterns among index cases and contacts of COVID-19, and explore when patients are most infectious during the disease process.

Methods This study examined all initial 90 885 confirmed index cases in Shanghai and their 450 770 close contacts. A generalised additive mixed model was used to analyse the associations of the number of close contacts with different demographic and clinical characteristics. The effect of different exposure time windows on the infection of close contacts was evaluated using a modified mixed-effects Poisson regression.

Results Analysis of contacts indicated that 82 467 (18.29%; 95% CI 18.17%, 18.42%) were second-generation cases. Our result indicated the q-index was 0.300 (95% CI 0.298, 0.302) for overall contact matrix, and that assortativity was greatest for students (q-index=0.377; 95% CI 0.357, 0.396) and weakest for people working age not in the labour force (q-index=0.246; 95% CI 0.240, 0.252). The number of contacts was 4.96 individuals per index case (95% CI 4.86, 5.06). Contacts had a higher risk if they were exposed from 1 day before to 3 days after the onset of symptoms in the index patient, with a maximum at day 0 (adjusted relative risk (aRR)=1.52; 95% CI 1.30, 1.76). Contacts exposed from 3 days before to 3 days after an asymptomatic index case had a positive reverse transcriptase-PCR (RT-PCR) result had a higher risk, with a maximum on day 0 (aRR=1.48; 95% CI 1.37, 1.59).

Conclusions The greatest assortativity was for students and weakest for people working age not in the labour force. Contact in the household was a significant contributor to the infection of close contacts. Contact tracing should focus on individuals who had contact soon before or soon after the onset of symptoms (or positive RT-PCR test) in the index case.

What is already known on this topic

Transmission of respiratory pathogens such as COVID-19 depends on patterns of contact and mixing across populations, and these patterns are crucial to predict pathogen spread and the effectiveness of control efforts.

Although SARS-CoV-2 can be detected one to 3 days before symptoms begin, detection of the virus does not necessarily mean that a person is infectious and able to spread the virus to others.

What this study adds

Contact patterns between index patients and their close contacts were similar to previous human social contact pattern.

Contact in the household increased the risk of infection of a close contact.

Individuals with COVID-19 were most infectious on day of the onset of symptoms (or positive reverse transcriptase-PCR (RT-PCR) test).

How this study might affect research, practice or policy

Tracing of all possible index cases and close contacts is impossible due to limited available resources.

It suggested that surveillance should focus on individuals who had contact before or soon after the onset of symptoms (or positive RT-PCR test) in the index case, especially the symptomatic index case.

• Introduction

The COVID-19 pandemic is a global outbreak of coronavirus and characterised by a high rate of transmission, with a high risk of exposure to and transmission of COVID-19 in indoor congregate settings, 1 2 high transmission mostly among close contacts and spread based on social contacts, 3 and social mixing patterns of host populations. 4 In late February 2022, Shanghai started a new wave of local Omicron epidemic. From that day on, altogether, Shanghai adopted a series of non-pharmaceutical interventions 5 to contain the spread of the SARS-CoV-2 virus. Contact tracing was a key part of the overall strategy of preventing transmission from individuals who are likely to be infectious. 6 In China, a close contact refers to a person who has effective contact with a suspected or confirmed case, typically by inhalation of infectious secretions from coughing, sneezing, laughing, singing, or talking, or by touching contaminated body parts or surfaces followed by ingestion of the pathogen. 7 8

To understand the spread of infectious diseases, many previous studies focused on age-mixing patterns and measured the characteristics of general population and their contacts in different age groups. 9–11 However, there were few studies to report the contact patterns for index cases and close contacts. Data on the interactions of index cases and close contacts can provide early signals related to the identification of close contacts; supply important information regarding the number of close contacts among age cohorts for epidemiological modelling 12 ; and increase awareness of the epidemic so that effective intervention measures can be developed and implemented. 13

Additionally, there is limited data on how long Omicron infections last and the time when an infected individual is most infectious in comparison to other variants. A previous study suggested that in people who develop symptoms, the majority are not infectious before symptoms develop, but two-thirds of cases are still infectious 5 days after their symptoms begin, 14 but this study examined a small sample and had low statistical power. From the inferred distribution of the infectiousness profile, a study in Hong Kong estimated that the infectiousness peaked at 1 before the day of symptom onset. 15 Another study found that close contacts had the highest risk of infection if they were exposed from 2 days before to 3 days after the onset of symptoms in the index patient, with a peak at day 0. 16 Thus, additional epidemiological data are needed to understand the relationship of SARS-CoV-2 transmissibility with the timing of symptoms and diagnosis.

The aim of this study was to use data on index cases and contact tracing to quantify the contact patterns of index cases and contacts, and to investigate the association of the time of exposure with the development of disease in close contacts of index patients.

• Materials and methods

Data sources and study design

Data on contact details for this study were sourced from the provincial Center for Disease Control and Prevention (CDC) in Shanghai, China, identifying patients newly diagnosed with COVID-19 and their close contacts between 1 March and 31 May 2022. Close contacts were quarantined for at least 14 days and received clinical examinations, including at least four times reverse transcriptase-PCR (RT-PCR) test during centralised quarantine period: on days 1, 4, 7 and 14.

This study was divided into two sections. The first section was an analysis of contact patterns. In this section, data were from 90 885 confirmed index cases in Shanghai and their 450 770 close contacts. The second section was an analysis of transmission risk at different exposure time windows. In this section, 409 close contacts were first excluded because of errors in the date of the onset or end of contact. An additional 36 110 contacts were excluded because their exposure times were not between 14 days before and 14 days after the positive RT-PCR result of the index patient. A previous systematic review reported the pooled incubation time of the Omicron variant was 3.42 days (95% CI 2.88, 3.96), slightly less than that of the Alpha variant. 17 Therefore, some contacts whose exposures occurred too early before or too late after the onset of symptoms (or positive RT-PCR result for the asymptomatic) in the symptomatic patient were also excluded. The final analytic sample consisted of 396 482 close contacts ( figure 1 ).

Disposition and inclusion of COVID-19 index patients and close contacts. The number of exposure events is not the same as the number of close contacts because of missing data in some close contacts (date of onset or end of exposure date) or index patients (symptom onset date and diagnosis date). RT-PCR, reverse transcriptase-PCR.

Identification of cases and contact tracing

An index patient was defined as the first eligible patient with a diagnosis of COVID-19 who had one or more contacts, with confirmation by RT-PCR. The clinical severity of disease in each patient was defined as asymptomatic, mild, moderate, severe or critically ill. 16 An asymptomatic infection is defined as a PCR-confirmed individual who entry into hospital or quarantine (i) does not meet any of the following clinical criteria: fever, cough, sore throat, and other self-perceived and clinical-identifiable symptoms or signs; and (ii) has no radiographic evidence of pneumonia. 18 When a patient had a laboratory-confirmed infection, a thorough epidemiological investigation was administered to the index patient and contacts, and all demographic and clinical data (sex, age, occupation, residence, clinical severity, etc) and exposure-related characteristics were recorded by the Shanghai CDC. Health officials defined a close contact as an individual who was not wearing personal protection equipment and had contact with a suspected or confirmed case within 4 days before symptom onset or a positive RT-PCR result. To further prevent transmission, the window of investigation could be extended to 7 days before the date of symptom onset or the first positive RT-PCR result of an asymptomatic infected person to the day of entry into quarantine, especially for index cases who were from densely populated environments, such as factories, construction sites and farmers’ markets.

Different exposure time windows

The exposure window was defined as the time between the first and last day of reported exposure to the index case, based on contact investigation. 19 According to the time from symptoms onset (or positive RT-PCR result for the asymptomatic) to exposure by the first day, contacts of index cases were classified into the following timing groups: more than 4 days before (−4+); 4 days before (−4); 3 days before (−3); 2 days before (−2); 1 day before (−1); 0 days before (0); 1 day after (1); 2 days after (2); and three or more days after (3+). Contacts of symptomatic cases were also divided into groups based on the interval from the onset of symptoms in the index case to diagnosis: day 0; day 1; day 2; day 3; and day 4 or more. Contacts of asymptomatic cases were grouped according to the time from a positive RT-PCR result to exposure and from a positive RT-PCR result to diagnosis, as indicators of different exposure time windows. Cumulative exposure duration was defined as the total number of days that a contact had exposure, from 4 days before to 4 days after the day of symptoms onset (or positive RT-PCR result for the asymptomatic) in the index patient.

Data processing and statistical analysis

Different age classes were used to develop age-specific contact matrices, 11 with stratification by occupation and confirmed disease duration of index cases, to estimate the number of age-specific close contacts per index case. The q-index, which represents departure from proportionate mixing and ranges from 0 (proportionate) to 1 (fully assortative), and the bootstrapped 95% CIs were used to assess the degree of age assortativity. 20

A generalised additive mixed model (GAMM 21 with a negative binomial distribution was used to analyse the association of the number of close contacts with demographic and clinical parameters. A χ 2 test was used to compare the distribution of the clinical severity of index cases and secondary cases, and the duration from symptom onset to a positive RT-PCR result. The interval between a positive RT-PCR result and diagnosis was examined using a two-sample t-test.

The attack rate (AR) was defined as the total number of new cases diagnosed among contacts of index patients divided by the total number of exposed contacts. 22 The AR was estimated for all contacts, and then separately for each exposure-related characteristic. A modified mixed-effects Poisson regression with a robust error variance was used to analyse infection risk for categorical covariates and different exposure time windows. This model has a logarithmic link function that considers clustering of contacts and allows for direct estimation of relative risk in observational studies. 16 Univariate regression was used to analyse factors related to infection of contacts. Then, the significant independent variables in the univariate analysis (age, sex, confirmed time window and occupation of index cases, household contact) were included in a multivariable regression model. P values and 95% CIs were used to assess statistical significance in all models. All statistical analyses were performed using R.4.1.1 software. 23 A two-sided p value less than 0.05 was considered significant.

Patient and public involvement

Neither the patients nor the public were involved in the conceptualisation, design, conduct, reporting or dissemination of this study as it was a large retrospective study of 450 770 close contacts. Data analysis and article writing in our study was designed to be fully anonymous and no private information (eg, name of participant) was recorded.

Demographic characteristics of index cases, close contacts and secondary cases

The index patients consisted of 44 384 women (48.84%) and 46 501 men (51.16%) and the median age was 46.00 years (IQR: 32.00, 59.00 years), compared with 40.81 years (IQR: 28.70, 54.59) among the close contacts ( online supplemental table S1 ). Among the 450 770 close contacts, 82 467 (18.29%, 95% CI 18.17%, 18.42%) received diagnoses of COVID-19. The mean interval from symptom ( online supplemental table S2 ) onset to a positive RT-PCR result was 1.30 days (95% CI 1.26, 1.34) in index cases and 1.50 days (95% CI 1.46, 1.53) in secondary cases (p<0.001). Analysis of the duration between a positive RT-PCR result to diagnosis was 2.30 days (95% CI 2.29, 2.31) in index cases. The mean number of secondary cases per index patient was 0.91 (IQR: 0.89, 0.92) and the median number was 0.0 (IQR: 0.00, 1.00).

The mean number of secondary cases remained stable during April and May ( figure 2 ), and was approximately 1 per index case. Analysis of the index cases indicated that 26.17% were confirmed on day 2 after symptom onset ( online supplemental figure S1 ), but only 1.20% on day 0 or before symptom onset. Most secondary cases were diagnosed 2 days (54.16%) or 1 day (23.12%) after symptom onset.

Changes in close contacts per index patient (A), secondary infections (B) and secondary attack rate (C). Shaded regions: 95% CIs.

Contact patterns of index cases and close contacts

The distribution of the number of close contacts was positively skewed ( online supplemental figure S2 ). Overall, only 1.01% of index cases had more than 50 close contacts, and most of index cases reported 1 close contact (38.14%) or 2 close contacts (21.00%). More than 48% of retired index cases reported 1 close contact, but 30.67% of school student index cases reported 1 close contact ( online supplemental figure S2 ).

Among all 450 770 close contacts, most (50.32%) were at home ( figure 2 ), and fewer were at the workplace (9.65%), farmers’ market (4.17%) and hotels/restaurants (0.51%). As the contact duration increased, the proportion of contacts at home also increased ( figure 3E ). For 81.52% of the index cases with contact durations more than 1 day, the close contact was a daily contact ( figure 3 ). Among daily contacts, 81.30% were at home ( figure 3 ). Individuals who were family members, relatives or colleagues were less likely to be contacts ( figure 3 ).

Patterns of contact of index patients and close contacts according to setting, frequency, relationship and duration. The proportions of contact relationships according to individual contact settings (A), frequencies (B) and durations (C). The proportions of contact settings according to contact frequencies (D) and durations (E). (F) Same as (A-E), but the proportions represent the contact frequencies according to contact durations. Farmers market is a public and recurring assembly of farmers or their representatives selling the food that they produced directly to consumers.

Contact matrix and assortativity of index cases and close contact

Analysis of the overall assortative mixing for the contact matrix indicated the q-index was 0.300 (bootstrapped 95% CI 0.298, 0.302) ( figure 4 ). The epidemic phase with the lowest assortativity was the half-city home quarantine phase to the city-wide home quarantine phase (q-index=0.246; 95% CI 0.242, 0.250). The occupational contact matrix ( figure 4B1,B2 ) showed that the contact matrix of index cases who were students had the greatest assortativity (q-index=0.377; 95% CI 0.357, 0.396), and assortativity was weakest for people working age not in the labour force (q-index=0.246; 95% CI 0.240, 0.252). Index cases who were preschoolers had fewer contacts with individuals aged 10–22 years, and more contacts with individuals who were aged 60 years or more. Students who were index cases tended to have contact with same-age individuals and those who were aged 30–60 years. Analysis of employed people ( figure 4B3 ,) indicated a strong diagonal signal starting at about age 15–70 years old for contacts and index cases.

Contact matrices for index patients and close contacts according to age. Each cell of a matrix represents the mean number of close contacts that an index patient in an age group had with other close contacts of different age groups. Matrices are for close contacts overall (A), different epidemic phases (B1–B5), and different occupations (C1–C6). To construct the matrix, bootstrap sampling with replacement of index patients was performed with weighting by the age distributions of the index patients. Each cell of the matrix represents an average more than 100 bootstrapped results.

The pattern of different age groups of index cases tending have assortative mixing by age also occurred in the contact matrices that were stratified by time window and occupation ( online supplemental figures S3-1 and S3-2 ). In particular, male index patients (q-index=0.301; 95% CI 0.298, 0.303) had more assortativity than female index patients (q-index=288; 95% CI 0.285, 0.290) ( online supplemental figure S4 ). For contact matrices of index cases among registered residents in Shanghai or other provinces, the diagonal element was most pronounced in those who were aged 15–35 years ( online supplemental figure S4, B1-B2 ). The diagonal element was evident in the contact matrix that was stratified by symptoms ( online supplemental figure S4, C1-C2 ).

Factors associated with the number of close contacts of index cases

The GAMM regression mode indicated there were 4.96 (95% CI 4.86, 5.06) close contact per index case ( table 1 ). Relative to male index cases, female index cases had fewer close contacts (OR=0.89, 95% CI 0.88, 0.90). There was also a nonlinear association between the number of close contacts and the age of index cases, with the greatest OR in those aged 20–39 years (OR=1.29, 95% CI 1.11, 1.48) and the smallest OR in those aged more than 60 years (OR=1.03, 95% CI 0.89, 1.19). The number of close contacts of employed index patients (6.35, 95% CI 6.16, 6.54) was significantly larger than that for student index patients (5.18, 95% CI 4.63, 5.72).

Compared with index cases who were residents in Shanghai, those with residences in other provinces had more contacts (OR=1.18, 95% CI 1.16, 1.20). The number of daily reported contacts of per case was slightly (although not significantly) greater before onset of the public transportation suspension phase, and declined significantly and progressively during the next four phases of the epidemic (OR=0.39; 95% CI 0.35, 0.44; OR=0.23; 95% CI 0.21, 0.26; OR=0.18; 95% CI 0.16, 0.20; OR=0.62; 95% CI 0.53, 0.73).

Analysis of categorical covariates and risk for different exposure time windows

The AR ( table 2 ) peaked during the half-city home quarantine phase to the onset of city-wide home quarantine phase, and was lowest in the phase since gradual resumption (20.95%; 95% CI 20.62%, 21.29% vs 3.61%; 95% CI 3.09%, 4.19%). The 197 286 close contacts who were Shanghai residents had a higher risk for secondary infection (18.63%; 95% CI 18.46%, 18.80%) than the 25,3075 close contacts who were residents of other provinces (17.87%; 95% CI 17.69%, 18.06%), but this difference was not significant in the multivariate analysis (adjusted relative risk (aRR)=1.01; p=0.082). The multivariate ( online supplemental table S3 ) analysis also indicated there were significant effects of age, occupation, status as a student and phase of the epidemic (all p<0.001). Analysis of contact setting indicated that contact in a household (aRR=1.32, 95% CI 1.29, 1.34) significantly increased the risk of infection in close contacts.

Our univariate analysis ( online supplemental table S4 ) showed that a contact tended to have increased risk of infection with increases in the time from symptom onset to diagnosis of a symptomatic index case and with increases in the time from a positive RT-PCR test to diagnosis of an asymptomatic case ( online supplemental figures S5 and S6 ). A mixed-effects model that adjusted for categorical covariates showed that contacts had a higher risk of COVID-19 if they were exposed from day −1 to day 3 of the index patient’s symptom onset ( figure 5 ), with a maximum at day 0 (aRR: 1.52; 95% CI 1.30, 1.76; AR: 23.37%; 95% CI 21.24%, 25.66%). The time from onset to diagnosis of an index case significantly increased the risk of infection risk for close contacts, and contacts had the highest risk of COVID-19 if the time from an index case’s time from symptom onset to diagnosis was 4 days or more (AR: 21.44%; 95% CI 20.81%, 22.10%; crude risk ratio (CRR): 1.68; 95% CI 1.40, 2.01; aRR: 1.60; 95% CI 1.33, 1.91). For asymptomatic index cases, contacts had a higher risk of COVID-19 if they were exposed between day −3 and day 3 ( figure 5 ) from the index patient’s positive RT-PCR result, with a maximum at day 0 (aRR: 1.48; 95% CI 1.37, 1.59; AR: 22.20%; 95% CI 21.04%, 23.40%). Asymptomatic index cases tended to have an increased risk of infection of close contacts as time from a positive RT-PCR result to diagnosis increased; contacts had the highest risk of COVID-19 if the time of an index case’s positive RT-PCR result to diagnosis was 4 days or more (AR: 23.46%; 95% CI 22.87%, 24.06%; CRR: 1.75; 95% CI 1.63, 1.87; aRR: 1.61; 95% CI 1.50, 1.72). Our results also showed that the cumulative duration of exposure significantly increased the risk of infection of close contacts ( figure 5 and online supplemental figure S7 ). The ARs and infection risk of close contacts tended to increase as the cumulative exposure duration increased (0 days: 17.16%; 95% CI 16.17%, 18.20%; 7+ days: 19.98%–95% CI 19.45%, 20.53%; CRR: 1.16; 95% CI 1.09, 1.24; aRR: 1.13; 95% CI 1.06, 1.21).

Attack rate (AR) for development of COVID-19 in close contacts of index patients according to cumulative exposure duration and time from symptom onset (or positive RT-PCR (reverse transcriptase-PCR) result for the asymptomatic) to exposure. For symptomatic patients, the ‘time from onset to exposure’ is the time from symptom onset in the index patient to exposure of the close contact; the ‘time from onset to diagnosis’ is the time from symptom onset in the index patient to diagnosis of the close contact. For asymptomatic patients, the ‘time from a positive RT-PCR result to exposure’ is the time from a positive RT-PCR result in the index patient to exposure of the close contact; the ‘time from a positive RT-PCR result to diagnosis’ is the time from a positive RT-PCR result in the index patient to diagnosis of the contact. Error bars (95% CIs) and dots represent AR for COVID-19 transmission to contacts. The vertical dotted line at 1.0 indicates no effect on risk. AR for each day was estimated using a multivariable Poisson regression with adjustment for household contacts, phase of the epidemic, and age, sex and occupation of the index patient.

In the present study, analysis of the overall degree of assortative mixing in the contact matrix indicated the q-index was 0.300 (95% CI 0.298, 0.302) and students had the greatest assortativity (q-index=0.377, 95% CI 0.357, 0.396). Several demographic and clinical factors of index cases (gender, age, residence, clinical severity) were associated with the number of close contacts. We also found that contact in the household increased the risk of infection of a close contact. Moreover, our results showed that the risk of COVID-19 transmission to a close contact was greater if the exposure time was from day −1 to day 3 of symptom onset in a symptomatic index case, and from day −3 to day 3 in an asymptomatic case with a positive RT-PCR result, with maximal risk on day 0 in both cases. As expected, the infection risk of close contacts tended to increase as the duration of cumulative exposure increased.

Similar to the findings of Prem et al , 24 we found that high assortativity of contacts was common in students, and less common in index cases working age not in the labour force. Compared with most individuals, those contacted by students and teenagers were of a similar age. 3 25 However, working age not in the labour force were more likely to have contact of diverse ages, which may provide a route for transmission from people working age not in the labour force and the rest of the population, and lead to a greater number of new infections. 26 Previous studies 3 27 28 reported that contact duration, frequency of contact, nature of the relationship and location of contact were associated with one another. For example, the less frequent contacts were less likely to be family members and colleagues. Importantly, intimate contacts had a greater risk of transmission, and these contacts typically occurred at home. 29 For instance, our results suggested that contact within the household (aRR=1.32, 95% CI 1.29, 1.34) was associated with a significantly higher risk of infection of close contacts than contact outside the household. We found that index patients who were residents in other provinces had more close contacts (OR=1.18; 95% CI 1.16, 1.20) than index patients from Shanghai. This might be due to the proximity of places used for gathering, working and living of these no-residents (9.78 million people (40.27% of the total population in Shanghai)). 30 Female index cases had fewer close contacts than male (OR=0.89; 95% CI 0.88, 0.90), possibly because men travel more outside the home with worse awareness of social distancing than female. 31

Previous research reported that COVID-19 was more likely to be transmitted by symptomatic than by asymptomatic infected individuals (AR of contacts: 18% vs 13%). 32 In agreement, we found that symptomatic exposure was associated with a significantly higher AR of close contacts than asymptomatic exposure (aRR=0.93; 95% CI 0.92, 0.95). This result suggests there may be additional secondary benefits of reducing the symptoms or disease severity of infected individuals, 33 such as by vaccination or prompt diagnosis and treatment. Previous reports 16 19 concentrated on the risk of transmission in an earlier SARS-CoV-2 lineage, and only examined a limited number of cases (less than 800) and contacts. In contrast, the outbreak of COVID-19 in Shanghai was caused by the Omicron variant. 34 This led us to perform a large population-based study to investigate the association of the timing of exposure to the Omicron variant with the risk to close contacts. Our findings indicated that the risk of transmission to a close contact was greatest from 1 day before symptom onset to 3 days after symptom onset in the index patient. These results have important implications for understanding the transmission dynamics of COVID-19, and are consistent with other infectivity studies which suggested that viral load may be highest around the time of symptom onset, 35 36 with a gradual decrease in viral shedding at 1 week after symptom onset. 37 Similar to previous research, 38 the transmission risk of asymptomatic index cases was greatest from 3 days before to 3 days after a positive RT-PCR result, with a maximum at day 0 (aRR=1.48; 95% CI 1.37, 1.59). Our observation of a lower risk of transmission from 3 days after symptom onset or 3 days after a positive RT-PCR result during the Shanghai Omicron epidemic has important implications for optimising COVID-19 prevention and control measures, cutting quarantine periods for the close contacts. Our results suggest that contact tracing should focus on individuals who had contact soon before or soon after the onset of symptoms in the index case. This is an important consideration, because surveillance of all possible index cases and close contacts is impossible due to limited available resources.

There were some limitations in our study. First, there may have been some reporting bias if contacts or index patients did not accurately recall the details of their date of onset. Second, the contact times of all contacts were not traced and recorded by health authorities, presumably due to a severe shortage of staff related to the sudden surge of cases. This could have led to bias in the parameter estimates. 39 Third, there may be a proportion of initially diagnosed asymptomatic Omicron infections is in the pre-symptomatic stage. 40 The presence or absence of symptoms depends on self-reporting on admission, which could lead to recall bias, especially in distinguishing between asymptomatic and symptomatic infections. Fourth, we determined the directionality of transmission based on recall by the index patient and the sequence in which the index case and contact first developed symptoms, a widely used procedure. 19 41 42 However, this method could lead to misclassification if an index patient had an unusually long incubation time or the contact had an unusually short incubation time.

• Conclusions

Our study of the relationships of index patients and their close contacts on the risk for COVID-19 transmission indicated that students had the greatest assortativity and people working age not in the labour force had the least assortativity. Individuals with COVID-19 were most infectious a few days before and a few days after symptom onset (or positive RT-PCR result for the asymptomatic), with a maximum at day 0. The duration of cumulative exposure significantly increased the risk of infection of a close contact. Contact in the household was a significant contributor to the infection of close contacts.

• Supplementary files

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• Published: 20 April 2024

Viral decisions: unmasking the impact of COVID-19 info and behavioral quirks on investment choices

• Wasim ul Rehman   ORCID: orcid.org/0000-0002-9927-2780 1 ,
• Omur Saltik 2 ,
• Faryal Jalil 3 &
• Suleyman Degirmen 4  

Humanities and Social Sciences Communications volume  11 , Article number:  524 ( 2024 ) Cite this article

Metrics details

This study aims to investigate the impact of behavioral biases on investment decisions and the moderating role of COVID-19 pandemic information sharing. Furthermore, it highlights the significance of considering cognitive biases and sociodemographic factors in analyzing investor behavior and in designing agent-based models for market simulation. The findings reveal that these behavioral factors significantly positively affect investment decisions, aligning with prior research. The agent-based model’s outcomes indicate that younger, less experienced agents are more prone to herding behavior and perform worse in the simulation compared to their older, higher-income counterparts. In conclusion, the results offer valuable insights into the influence of behavioral biases and the moderating role of COVID-19 pandemic information sharing on investment decisions. Investors can leverage these insights to devise effective strategies that foster rational decision-making during crises, such as the COVID-19 pandemic.

Introduction

Coronavirus (COVID-19) is recognized as a significant health crisis that has adversely affected the well-being of global economies (Baker et al. 2020 ; Smales 2021 ; Debata et al. 2021 ). First identified in December 2019 as a highly fatal and contagious disease, it was declared a public health emergency by the World Health Organization (WHO) (WHO 2020 ; Baker et al. 2020 ; Altig et al. 2020 ; Smales 2021 ; Li et al. 2020 ). The outbreak swiftly spread across 31 provinces, municipalities, and autonomous regions in China, eventually evolving into a severe global pandemic that significantly impacted the global economy, particularly equity markets and social development (WHO 2020 ; Kazmi et al. 2020 ; Li et al. 2020 ). Since the early 2020 emergence of COVID-19 symptoms, the pandemic has caused considerable market decline and volatility in stock returns, significantly impacting the prosperity of world economies (Rahman et al. 2022 ; Soltani et al. 2021 ; Rubesam and Júnior 2022 ; Debata et al. 2021 ; Baker et al. 2020 ; Altig et al. 2020 ). This situation has garnered the attention of many policymakers and economists since its classification as a public health emergency.

Pakistan’s National Command and Operation Centre reported its first two confirmed COVID-19 cases on February 26, 2020. Following this, the Pakistan Stock Exchange experienced a significant downturn, losing 2266 points and erasing Rs. 436 billion in market equity. Foreign investment saw a notable decline, with stocks worth $22.5 million contracting sharply. By the end of February 2020, stock investments totaling $56.40 million had been liquidated. This dramatic drop in equity markets is attributed to the global outbreak of the COVID-19 pandemic (Khan et al. 2020 ). Additionally, for the first time in 75 years, Pakistan’s economy underwent its most substantial contraction in economic growth, recording a GDP growth rate of −0.4% in the first nine months. All three sectors of the economy—agriculture, services, and industry—fell short of their growth targets, culminating in a loss of one-third of their revenue. Exports declined by more than 50% due to the pandemic. Economists have raised concerns about a potential recession as the country grapples with virus containment efforts (Shafi et al. 2020 ; Naqvi 2020 ). Consequently, the rapid spread of COVID-19 has heightened volatility in financial markets, inflicted substantial losses on investors, and caused widespread turmoil in financial and liquidity markets globally (Zhang et al. 2020 ; Goodell 2020 ; Al-Awadhi et al. 2020 ; Ritika et al. 2023 ). This uncertainty has been exacerbated by an increasing number of positive COVID-19 cases.

Since the magnitude of the COVID-19 outbreak became evident, capital markets worldwide have been experiencing significant declines and volatility in stock returns, affected by all new virus variants despite their effective treatments (Hong et al. 2021 ; Rubesam and Júnior 2022 ; Zhang et al. 2020 ). Previous studies have characterized COVID-19 as a particularly devastating and deadly pandemic, severely impacting socio-economic infrastructures globally (Fernandes 2020 ). The pandemic has disrupted trade and investment activities, leading to imbalances in equity market returns (Xu 2021 ; Shehzad et al. 2020 ; Zaremba et al. 2020 ; Baig et al. 2021 ). In response to the COVID-19 outbreak, various governments, including Pakistan’s, have implemented unprecedented and diverse measures. These include restricting the mobility of the general public and commercial operations, and implementing smart or partial lockdowns, all aimed at mitigating the pandemic’s impact on global economic growth (Rubesam and Júnior 2022 ; Zaremba et al. 2020 ).

Investment decisions become notably complex and challenging when influenced by behavioral biases (Pompian 2012 ). In this context, numerous studies have sought to reconcile various behavioral finance theories with the notion of investors as rational decision-makers. One prominent theory is the Efficient Market Hypothesis, which asserts that capital markets are efficient when decisions are informed by symmetrical information among participants (Fama 1991 ). Yet, in reality, individual investors often struggle to make rational investment choices (Kim and Nofsinger 2008 ), as their decisions are significantly swayed by behavioral biases, leading to market inefficiencies. These biases, including investor sentiment, overconfidence, over/underreaction, and herding behavior, are recognized as widespread in human decision-making (Metawa et al. 2018 ). Prior research has identified various behavioral and psychological biases—such as loss aversion, anchoring, heuristic biases, and the disposition effect—that cause investors to stray from rational investment decisions. Moreover, investors’ responses to COVID-19-related news, like infection rates, vaccine developments, lockdowns, or economic forecasts, often reflect behavioral biases such as investor sentiment, overconfidence, over/underreaction, or herding behavior towards short-term events, thereby affecting market volatility (Soltani and Boujelbene 2023 ; Dash and Maitra 2022 ). These biases may have a wide applicability across different markets, regardless of specific cultural or regulatory differences. Consequently, we posit that these four behavioral biases, in the context of COVID-19, are key factors in reducing vulnerability in investment decisions (Dermawan and Trisnawati 2023 ), especially for individual investors who are more susceptible than in a typical investment environment (Botzen et al. 2021 ; Talwar et al. 2021 ). Therefore, understanding these behavioral biases—such as investor sentiment, overconfidence, over/underreaction, or herding behavior—during the COVID-19 pandemic is crucial, as no previous epidemic has demonstrated such profound impacts of behavioral biases on investment decisions (Baker et al. 2020 ; Sattar et al. 2020 ).

Numerous studies have explored the impact of behavioral biases, including investor sentiment, overconfidence, over/under-reaction, and herding behavior, on investment decisions (Metawa et al. 2018 ; Menike et al. 2015 ; Nofsinger and Varma 2014 ; Qadri and Shabbir 2014 ; Asaad 2012 ; Kengatharan and Kengatharan 2014 ). Recent literature has also shed light on the effects of the COVID-19 pandemic on financial and precious commodity markets (Gao et al. 2023 ; Zhang et al. 2020 ; Corbet et al. 2020 ; Baker et al. 2020 ; Mumtaz and Ahmad 2020 ; Ahmed et al. 2022 ; Hamidon and Kehelwalatenna 2020 ). However, academic research specifically addressing the moderating role of COVID-19 pandemic information sharing on behavioral biases remains limited. It has been observed that global pandemics, such as the Ebola Virus Disease (EVD) and Severe Acute Respiratory Syndrome (SARS), significantly influence stock market dynamics, sparking widespread fear among investors and leading to market uncertainty (Del Giudice and Paltrinieri 2017 ; He et al. 2020 ). This study contributes to the field by examining how behavioral biases, such as investor sentiment, overconfidence, over/under-reaction, and herding behavior, are influenced by the unique circumstances of the COVID-19 crisis. Furthermore, this research provides novel insights into real-time investor behavior and policymaking, thus advancing the academic debate on the role of COVID-19 pandemic information sharing within behavioral finance.

The primary goal of this study is to explore the impact of the COVID-19 crisis on behavioral biases and their effect on investment decisions. Additionally, it aims to assess how various socio-demographic factors influence investment decision-making. These factors include age, occupation, gender, educational qualifications, type of investor, investment objectives, reasons for investing, preferred investment duration, and considerations prior to investing, such as the safety of the principal, risk level, expected returns, maturity period, and sources of investment advice. We hypothesize that these factors significantly influence investment decisions, and our analysis endeavors to investigate the relationship between these factors and investment behavior. By thoroughly examining these variables, the study aims to shed light on the role socio-demographic factors play in investment behavior and enhance the understanding of the investment decision-making process. Additionally, the study seeks to conduct a cluster analysis to identify hierarchical relationships and causality, alongside an agent-based learning model that illustrates the susceptibility of low-income and younger age groups to herding behavior. The article provides the codes and outcomes of the model.

The study will commence with an introduction that outlines the scope and significance of the research. Following this, a literature review will be provided, along with the development of hypotheses concerning the behavioral biases affecting investment decisions and the role of socio-demographic factors in shaping investment behavior. The methodology section will detail the research approach, data collection process, variables considered for analysis, and the statistical methods applied. Subsequently, the results section will present findings from the regression and moderating analyses, cluster analysis, and the agent-based learning model. This will include a detailed explanation of the model codes and their interpretations. The discussion section will interpret the study’s results, highlighting their relevance to policymakers, financial advisors, and individual investors. The article will conclude by summarizing the main discoveries and offering suggestions for further inquiry in this domain.

Literature review and development of hypotheses

Invsetor sentiments and investment decisions.

Pandemic-driven sentiments play a crucial role in determining market returns, making it imperative to understand pandemic-related sentiments to predict future investor returns. Consequently, we posit that the sharing of COVID-19 pandemic information is a critical factor influencing investor sentiments towards investment decisions (Li et al. 2021 ; Anusakumar et al. 2017 ; Zhu and Niu 2016 ; Jiang et al. 2021 ). Generally, investors’ sentiments refer to their beliefs, anticipations, and outlooks regarding future cash flows, which are significantly influenced by external factors (Baker and Wurgler 2006 ). Ding et al. ( 2021 ) define investor sentiment as the collective attitude of investors towards a particular market or security, reflected in trading activities and price movements of securities. A trend of rising prices signals bullish sentiments, while decreasing prices indicate bearish investor sentiment. These sentiments, including emotions and beliefs about investment risks, notably affect investors’ behavior and yield (Baker and Wurgler 2006 ; Anusakumar et al. 2017 ; Jansen and Nahuis 2003 ). Sentiment reacts to stock price news (Mian and Sankaraguruswamy 2012 ), with stock prices responding more positively to favorable earnings news during periods of high sentiment than in low sentiment periods, and vice versa. This sentiment-driven reaction to share price movements is observed across all types of stocks (Mian and Sankaraguruswamy 2012 ). Furthermore, research indicates that market responses to earnings announcements are asymmetrical, especially in the context of pessimistic investor sentiments (Jiang et al. 2019 ). Such reactions were notably pronounced during COVID-19 pandemic news, where sentiments such as fear, greed, or optimism significantly influenced market dynamics (Jiang et al. 2021 ). Thus, information related to the COVID-19 pandemic emerges as a valuable resource for forecasting future returns and market volatility, ultimately affecting investment decision-making (Debata et al. 2021 ).

Overconfidence and investment decision

Standard finance theories suggest that investors aim for rational decision-making (Statman et al. 2006 ). However, their judgments are often swayed by personal sentiments or cognitive errors, leading to overconfidence (Apergis and Apergis 2021 ). Overconfidence in investing can be described as an inflated belief in one’s financial insight and decision-making capabilities (Pikulina et al. 2017 ; Lichtenstein and Fischhoff 1977 ), or a tendency to overvalue one’s skills and knowledge (Dittrich et al. 2005 ). This results in investors perceiving themselves as more knowledgeable than they are (Moore and Healy 2008 ; Pikulina et al. 2017 ).

Overconfidence has been categorized into overestimation, where investors believe their abilities and chances of success are higher than actual, and over-placement, where individuals see themselves as superior to others (Moore and Healy 2008 ). Such overconfidence affects investment choices, leading to potentially inappropriate high-risk investments (Pikulina et al. 2017 ). Overconfident investors often attribute success to personal abilities and failures to external factors (Barber and Odean 2000 ; Tariq and Ullah 2013 ). Overconfidence also leads to suboptimal decision-making, especially under uncertainty (Dittrich et al. 2005 ).

Behavioral finance research shows that individual investors tend to overestimate their chances of success and underestimate risks (Wei et al. 2011 ; Dittrich et al. 2005 ). Excessive overconfidence prompts over-investment, whereas insufficient confidence causes under-investment; moderate confidence, however, leads to more prudent investing (Pikulina et al. 2017 ). The lack of market information often triggers this scenario (Wang 2001 ). Amidst recent market anomalies, COVID-19 information has significantly impacted investors’ overconfidence in their investment decisions. Studies have shown that overconfident investors underestimate their personal risk of COVID-19 compared to the general risk perception (Bottemanne et al. 2020 ; Heimer et al. 2020 ; Boruchowicz and Lopez Boo 2022 ; Druica et al. 2020 ; Raude et al. 2020 ). Overconfidence may lead to adverse selection and undervaluing others’ actions, underestimating the likelihood of loss due to inadequate COVID-19 information (Hossain and Siddiqua 2022 ). Consequently, this study hypothesizes that certain exogenous factors, integral to COVID-19 information sharing, may moderate investment decisions in the context of investor overconfidence.

Over/under reaction and investment decision

The Efficient Market Hypothesis (EMH) suggests that investors’ attempts to act rationally are based on the availability of market information (Fama 1998 ; Fama et al. 1969 ; De Bondt 2000 ). However, psychological biases in investors systematically respond to unwelcome news, leading to overreaction and underreaction, thus challenging the notion of market efficiency (Maher and Parikh 2011 ; De Bondt and Thaler 1985 ). Overreaction and underreaction biases refer to exaggerated responses to recent market news, resulting in the overbuying or overselling of securities in financial markets (Durand et al. 2021 ; Spyrou et al. 2007 ). Barberis et al. ( 1998 ) identified both underreaction and overreaction as pervasive anomalies that drive investors toward irrational investment decisions. Similarly, Hirshleifer ( 2001 ) noted that noisy trading contributes to overreaction, which in turn leads to excessive market volatility.

The impact of the COVID-19 outbreak extends far beyond the loss of millions of lives, disrupting financial markets from every angle (Zhang et al. 2020 ; Iqbal and Bilal 2021 ; Tauni et al. 2020 ; Borgards et al. 2021 ). Market reactions have been significantly shaped by COVID-19 pandemic information sharing, affecting investors’ decisions (Kannadas 2021 ). Recent studies have found that investors’ biases in evaluating the precision and predictive accuracy of COVID-19 information can lead to overreactions and underreactions (Borgards et al. 2021 ; Xu et al. 2022 ; Kannadas 2021 ). Furthermore, research documents the growing influence of COVID-19 information sharing on market reactions worldwide, including in the US, Asian, European, and Australian markets (Xu et al. 2022 ; Nguyen et al. 2020 ; Nguyen and Hoang Dinh 2021 ; Naidu and Ranjeeni 2021 ; Heyden and Heyden 2021 ), indicating that market reactions, characterized by non-linear behavior, are driven by investors’ beliefs.

Previous literature has scarcely explored the role of investors’ overreaction and underreaction in decision-making. Recently, emerging research has begun to enrich the literature by examining the moderating role of COVID-19 pandemic information sharing.

Herding behavior and investment decision

According to the assumptions of Efficient Market Hypothesis (EMH), optimal decision-making is facilitated by the availability of market information and stability of stock returns (Fama 1970 ; Raza et al. 2023 ). However, these conditions are seldom met in reality, as decisions are influenced by human behavior shaped by socio-economic norms (Summers 1986 ; Shiller 1989 ). Behavioral finance research suggests that herding behavior plays a significant role in the decline of asset and stock prices, implying that identifying herding can aid investors in making more rational decisions (Bharti and Kumar 2022 ; Jiang et al. 2022 ; Jiang and Verardo 2018 ; Ali 2022 ). Bikhchandani and Sharma ( 2000 ) define herding as investors’ tendency to mimic others’ trading behaviors, often ignoring their own information. It is essentially a group dynamic where decisions are irrationally based on others’ information, overlooking personal insights, experiences, or beliefs (Bikhchandani and Sharma 2000 ; Huang and Wang 2017 ). Echoing this, Hirshleifer and Hong Teoh ( 2003 ) argue that herding is characterized by investment decisions being influenced by the actions of others.

The sharp market declines prompted by events such as the COVID-19 pandemic raise questions about its influence on investors’ herding behaviors (Rubesam and Júnior 2022 ; Mandaci and Cagli 2022 ; Espinosa-Méndez and Arias 2021 ). Christie and Huang ( 1995 ) observed that investor herding becomes more evident during market uncertainties. Hwang and Salmon ( 2004 ) noted that investors are less likely to exhibit herding during crises compared to stable market periods when confidence in future market prospects is higher. The COVID-19 pandemic, as a major market disruptor, necessitates that investors pay close attention to market fundamentals before making investment decisions. Recent studies suggest that an overload of COVID-19 information could lead to irrational decision-making, potentially challenging the EMH by influencing herding behavior (Jiang et al. 2022 ; Mandaci and Cagli 2022 ). This highlights the importance for investors to be aware of market information asymmetry changes, such as those triggered by the COVID-19 outbreak, which could negatively impact their investment portfolios by altering their herding tendencies. This effect may be more pronounced among individual investors than institutional ones (Metawa et al. 2018 ). A yet unexplored area is the extent to which COVID-19 pandemic information sharing amplifies the herding behavior among investors during investment decision-making processes (Mandaci and Cagli 2022 ).

COVID-19 pandemic information sharing moderating the relationship between behavioral biases and investment decisions

Recent research indicates that the COVID-19 pandemic has notably influenced behavioral biases among investors, affecting their decision-making processes (Betthäuser et al. 2023 ; Vasileiou 2020 ). Since the pandemic’s onset, investors have shown increased sensitivity to pandemic-related news or developments, leading to intensified behavioral biases. This heightened sensitivity poses challenges to investors’ abilities to respond effectively. Specifically, information related to economic uncertainty, infection rates, and vaccination progress has shifted investor sentiment regarding risk perception (Gao et al. 2023 ). Additionally, pandemic news has altered the risk perception of overconfident investors, who previously may have underestimated the risks associated with COVID-19 (Bouteska et al. 2023 ). The increased uncertainty and market volatility triggered by COVID-19 news have also prompted investors to adapt their reactions based on new information, potentially fostering more rational decision-making (Jiang et al. 2022 ). The rapid spread of COVID-19-related news has been shown to diminish mimicry in investment decisions (Nguyen et al. 2023 ). This indicates that viral news about the pandemic makes investors more discerning regarding risk perceptions and investment strategies, moving away from mere herd behavior. Based on this discussion, the study proposes that COVID-19 pandemic information sharing acts as a moderating factor in the relationship between behavioral biases and investment decisions.

Sociodemographic factors and investment decision

The influence of demographic factors like gender, age, income, and marital status on investor behavior is well-documented in financial literature. However, examining these relationships within specific geographical contexts—such as countries, regions, states, and provinces—reveals that cultural values, beliefs, and experiences may blur the distinctions between human and cognitive biases in terms of their nuanced impacts. Evidence shows that certain demographic groups, particularly young male investors with lower portfolio values from regions less developed in terms of education and income, are more prone to overconfidence and familiarity bias in their trading activities. Conversely, investors with higher education levels and female investors are inclined to trade less frequently, resulting in better investment returns (Barber and Odean 2000 ; Gervais and Odean 2001 ; Glaser and Weber 2007 ).

This study’s findings further suggest that with increased stock market experience, investors tend to discount emotional factors, leading to more rational investment choices. Nonetheless, experience alone does not appear to markedly influence the decision-making process among investors (Al-Hilu et al. 2017 ; Metawa et al. 2019 ).

In summary, demographic variables such as age, gender, and education significantly impact investment decisions, especially when considered alongside behavioral aspects like investor sentiment, overconfidence, and herd behavior. Gaining insight into these dynamics is crucial for investors, financial advisors, and policymakers to devise effective investment strategies and enhance financial literacy.

Research methodology

Data and sampling.

The research methodology outlines the strategy for achieving the study’s objectives. This research adopted a quantitative approach, utilizing a survey method (questionnaire) to examine the behavioral biases of individual investors in Pakistan during the COVID-19 pandemic. The target population comprised individual investors from Punjab province, specifically those interested in capital investments. Data were collected through convenient sampling techniques. A total of 750 questionnaires were distributed via an online survey (Google Form) to investors in four major cities of Punjab province: Karachi, Lahore, Islamabad, and Faisalabad. Initially, 257 respondents completed the survey following follow-up reminder emails. Out of these, 223 responses were deemed usable, yielding a valid response rate of 29.73% for further analysis (Saunders et al. 2012 ).

To mitigate potential biases during the data collection process, we conducted analyses for non-response and common method biases. Non-response bias, which arises when there is a significant difference between early and late respondents in a survey, was addressed by comparing the mean scores of early and late respondents using the independent samples t -test (Armstrong and Overton 1977 ). Results (see Table 1 ) indicated no statistically significant ( p  > 0.05) difference between early and late responses, suggesting that response bias was not a significant issue in the dataset.

Furthermore, to assess the potential threat of common method variance, we applied Harman’s single-factor test, a widely used method to evaluate common method biases in datasets (Podsakoff et al. 2003 ). This technique is aimed at identifying systematic biases that could compromise the validity of the scale. Through exploratory factor analysis (EFA) conducted without rotation, it was determined that no single factor accounted for a variance greater than the threshold (i.e., 50%). Consequently, common method variance was not considered a problem in the dataset, ensuring the reliability of the findings.

Figure 1 illustrates the framework of the model established for regression and moderating analyses that reveal the interactions between behavioral biases, investment decisions and COVID-19 pandemic information sharing.

Covid-19 pandemic informing sharing.

Measures for behavioral biases

A close-ended questionnaire based on five-point Likert measurement scales was prepared scaling (1= “strongly disagree” to 5= “strongly agree”) to operationalize the behavioral biases of investors. The first predictor is investor sentiments. It refers to investors’ beliefs and perspectives related to future cash flows or discourses of specific assets. It is a crucial behavioral factor that often drives the market movements, especially during pandemic. We used the modified 5-items scale from the study of (Metawa et al. 2018 ; Baker and Wurgler 2006 ). Second important behavioral factor is overconfidence, which measured the tendency of decision-makers to unwittingly give excessive weight to the judgment of knowledge and correctness of information possessed and ignore the public information (Lichtenstein and Fischhoff 1977 ; Metawa et al. 2018 ). This construct was measured by using the 3-items scale developed by Dittrich et al. ( 2005 ). In line with the studies of (see for example (De Bondt and Thaler 1985 ; Metawa et al. 2018 ), we opted the 4-items scale to measure the over/under reactions. It illustrates that investors systematically overreact to unexpected news, and this leads to the violation of market efficiency. They conclude that investors attach great importance to past performance, ignoring trends back to the average of that performance (Boubaker et al. 2014 ). Last, herding behavior effect means theoretical set-up suggesting that investment managers are imitating the strategy of others despite having exclusive information. Such managers prefer to make decisions according to the connected group to avoid the risk of reputational damage (Scharfstein and Stein 1990 ). In sense, a modified scale was anchored to examine the herd behavior of investors from the studies of Bikhchandani and Sharma ( 2000 ) and Metawa et al. ( 2018 ).

Measures for COVID-19 pandemic information sharing

To assess the moderating effect of COVID-19 pandemic information sharing, it was examined in terms of uncertainty, fear, and perceived risk associated with the virus (Kiruba and Vasantha 2021 ). Previous studies indicate that COVID-19 news and developments have markedly affected the behavioral biases of investors (Jiang et al. 2022 ; Nguyen et al. 2023 ). To this end, an initial scale was developed to measure the moderating effect of COVID-19 pandemic information sharing. The primary reason for creating a new scale was that existing scales lacked clarity and were not specifically designed to assess how anchoring behavioral biases affect investment decisions. Subsequently, a self-developed scale was refined with input from a panel of experts, including two academicians specializing in neuro or behavioral finance and two investors with expertise in the capital market, to ensure the scale’s face and content validity regarding COVID-19 pandemic information sharing. They reviewed the scale in terms of format, content, and wording. Based on their comprehensive review, minor modifications were made, particularly aligning the scale with pandemic news and developments to accurately measure the impact of the COVID-19 health crisis on investors’ behavioral biases. Ultimately, a four-item scale, employing a five-point Likert scale (1= “strongly disagree” to 5= “strongly agree”), focusing on COVID-19 related aspects (e.g., infection rates, lockdowns, vaccine development, and government stimulus packages) was utilized to operationalize the construct of COVID-19 pandemic information sharing (Bin-Nashwan and Muneeza 2023 ; Li and Cao 2021 ).

I believe that increasing information about rate of COVID-19 infections influenced my investment decisions.

I believe that increasing information about COVID-19 lockdowns influenced my investment decisions.

I believe that increasing information about COVID-19 vaccinations development, influenced my investment decisions, and

I believe that increasing information about government stimulus packages influenced my investment decisions.

Measures for investment decisions

To measure investment decision, the modified five points Likert scale ranging from (1= “strongly disagree” to 5= “strongly agree”) has been opted from the study of Metawa et al. ( 2018 ).

Hypotheses of study

The hypotheses of the study regarding regression analysis and moderating analyses are as follows in Table 2 :

The hypotheses outlined above were tested using regression analyses and moderating analyses. To reveal the clustering tendencies of investors exhibiting similar behaviors, cognitive biases, and sociodemographic variables, the feature importance values were investigated using K-means clustering analyses. Furthermore, findings and recommendations were provided to policymakers using agent-based models to develop policy suggestions within the scope of these hypotheses, offering insights for academic purposes.

Demographic profile of respondents

Table 3 provides a brief demographic profile of respondents.

Based on the percentages presented in Table 3 , the study primarily focuses on a specific demographic profile. Most participants were 20–30 years old (61.0%) with a higher educational background, particularly a master’s degree (67.3%). They were mostly salaried individuals (56.5%), male (61.0%), and identified as seasonal investors (63.7%). The investment objective of this group was mostly focused on growth and income (37.2%), while wealth creation (41.3%) was their primary purpose for investing. They preferred to invest equally in medium-term (43.5%) and long-term (28.3%) periods and considered high returns (38.6%) as the primary factor before investing. They received investment advice primarily from family and friends (44.8%) and social media (29.6%). Overall, the study indicates that the sample consisted of younger, male, salaried individuals with higher education levels who rely on personal networks and social media for investment advice. Their investment objectives are focused on wealth creation through growth and income, with an equal preference for medium and long-term investments.

Analysis and results

Descriptive summary.

Table 4 outlines the measures used to evaluate the constructs of the study, detailing the number of items for each construct, mean values, standard deviations, zero-order bivariate correlations among the variables, and Cronbach’s Alpha values. The evaluation encompasses a total of 29 items spread across six constructs: investor sentiments (5 items), overconfidence (3 items), over/under reaction (4 items), herding theory (3 items), investment decision (10 items), and COVID-19 information impact (4 items). The mean scores for these items fall between 3.535 and 3.779, with standard deviations ranging from 0.877 to 0.965.

Parallel coordinates (see Figs. 2 – 5 ) visualization is employed as a method to depict high-dimensional data on a two-dimensional plane, proving particularly beneficial for datasets with a large number of features or attributes. This technique involves the use of vertical axes to represent each feature, connected by horizontal lines that represent individual data points. This visualization method facilitates the identification of patterns, detection of clusters or outliers, and discovery of correlations among the features. Therefore, parallel coordinates visualization is instrumental in analyzing complex datasets, aiding in the informed decision-making process based on the insights obtained.

Strongly disagree (CIS1) choice parallel coordinates.

Disagree (CIS2) choice parallel coordinates.

Agree (CIS3) choice parallel coordinates.

Strongly agree (CIS4) choice parallel coordinates.

The analysis of responses to the COVID-19 information sharing questions reveals a significant correlation with the second and fourth-level responses concerning cognitive biases, including investor sentiment, overconfidence, over/under reaction, and herding behavior. This observation leads to two key insights. Firstly, participants demonstrate an ability to perceive, respond to, and comprehend the nuances of their investment decisions as related to investor sentiment, overconfidence, over/under reaction, and herding behavior. Consequently, they show a propensity to make clear decisions, indicating agreement or disagreement in their responses. Secondly, it is noted that individuals who acknowledge being significantly influenced by COVID-19 news tend to adopt more balanced investment strategies concerning these cognitive biases. Additionally, younger individuals, particularly those self-employed or not professionally investing, who show a preference for long-term value investments, are more inclined to exhibit these tendencies.

The value of the Pearson correlation coefficient (r) was calculated to investigate the nature, strength and relationship between variables. The results of correlation analysis reveal that all the constructs positively correlated.

To investigate the interconnections among variables in the dataset, correlations were computed and illustrated through a network graph. The correlation matrix’s values served as the basis for edge weights in the graph, with more robust correlations depicted by thicker lines (see Fig. 6a ). Each variable received a unique color, and connections showcasing higher correlations utilized a distinct color scheme to enhance visual clarity. This method offers a graphical depiction of the intricate relationships among various variables, facilitating the discovery of patterns and insights that might remain obscured within a conventional correlation matrix.

a Correlation diagraphs and matrix. b Correlation diagraphs and matrix.

The correlation analysis revealed a pronounced relationship between cognitive biases (such as investor sentiments, overconfidence, herd behavior, and investment decisions), COVID-19 information sharing, and socio-demographic factors (including age group, occupation, gender, educational qualifications, type of investor, investment objectives, investment purposes, preferred investment duration, factors considered prior to investing, and sources of investment advice). A correlation matrix graph was constructed to further elucidate these correlations, assigning different colors to each variable for visual differentiation (see Fig. 6b ). The thickness of the lines in the graph correlates with the strength of the relationships, indicating variables with high correlation more prominently.

These findings underscore the interconnected nature of the study variables, demonstrating that cognitive biases and socio-demographic factors exert a considerable impact on investment decisions. This analytical approach highlights the complexity of investor behavior and underscores the multifaceted influences on investment choices, providing valuable insights for understanding how various factors interact within the investment decision-making process.

Reliability test

For reliability test, the Cronbach alpha values were examined to check the internal consistency of the measure. The internal consistency of an instrument tends to indicate whether a metric or an indicator measure what it is intended to measure (Creswell 2009 ). The Cronbach’s alpha greater than 0.7 indicates that all the items or the questions regarding the respective variable are good, highly correlated and reliable. The calculated Cronbach coefficient value for Investor sentiments (alpha = 0.888), over confidence (alpha = 0.827), over/under reaction (alpha = 0.858), herding behavior theory (alpha = 0.741), Investment decision (alpha = 0.933) and COVID-19 (alpha = 0.782) indicates that all of the constructs are reliable.

Validity test

Validity refers to the extent to which an instrument accurately measures or performs what it is designed to measure (Kothari 2004 ). To ensure the validity of the questionnaire and its constructs, the researcher engaged in a comprehensive literature review, sought the advice of consultants, and incorporated feedback from other professionals in the field. Additionally, the concepts of convergent validity and discriminant validity were evaluated to further assess the instrument’s validity.

Convergent validity assesses the extent to which items that are theoretically related to a single construct are, in fact, related in practice (Wang et al. 2017 ). To determine convergent validity, factor loading, Average Variance Extracted (AVE), and Composite Reliability (CR) were calculated. According to Hair et al. ( 1998 ), factor loading values should exceed 0.60, composite reliability should be 0.70 or higher, and AVE should surpass 0.50 to confirm adequate convergent validity.

Table 5 demonstrates that all constructs utilized in this study surpass these threshold values, indicating strong convergent validity. This suggests that the items within each construct are consistently measuring the same underlying structure, reinforcing the validity of the questionnaire’s design and the constructs it aims to measure.

Discriminant validity measures the degree that the concepts are distinct from each other (Bagozzi et al. 1991 ) and it is evident that if alpha value of a construct is greater than the average correlation of the construct with other variables in model, the existence of discriminant validity exist (Ghiselli et al. 1981 ).

Hypotheses testing

To examine the conditional moderating effect of COVID-19 on the influence of behavioral factors (investor sentiments, overconfidence, over/under reaction, and herding behavior) on investment decision-making, moderation analysis was conducted using the Process Macro (Model 1) for SPSS, as developed by Hayes, with bootstrapping samples at 95% confidence intervals. According to Hayes ( 2018 ), the analysis first explores the direct impact of the behavioral factors on investment decisions. Subsequently, it assesses the indirect influence exerted by the moderating variable (COVID-19). This two-step approach allows for a comprehensive understanding of how COVID-19 modifies the relationship between investors’ behavioral biases and their decision-making processes, shedding light on the extent to which the pandemic acts as a moderating factor in these dynamics.

For this study the mathematical model to test moderating role of COVID-19 pandemic information sharing can be explained as:

Y = Investment decisions (Dependent variable)

β 0  = Intercept

X 1  = Investment sentiments (Independent variable)

X 2  = Overconfidence (Independent variable)

X 3  = Over/under reaction (Independent variable)

X 4  = Herding behavior (Independent variable)

β 1 X 1  = Intercept of investors sentiments

β 2 X 2  = Intercept of overconfidence

β 3 X 3  = Intercept of over/under reaction

β 4 X 4  = Intercept of herding behavior

(X 1 * COVID-19) = Investors’ sentiments and moderation effect of COVID-19 information

(X 2 * COVID-19) = Overconfidence and moderation effect of COVID-19 information

(X 3 * COVID-19) = Over/under reaction and moderation effect of COVID-19 information

(X 4 * COVID-19) = Herding behavior and moderation effect of COVID-19 information

μ = Residual term.

Direct effect

In Table 6 , the direct effect of the independent variables on the dependent variable demonstrates that the behavioral factors (investor sentiments, overconfidence, over/under reaction, and herding behavior) significantly influence investment decision (ID) with beta values of 0.961, 0.867, 0.884, and 0.698, respectively. The confidence interval (CI) values presented in Table 6 confirm these relationships are statistically significant. The positive and significant outcomes underline that behavioral factors critically impact investors’ decision-making attitudes. Consequently, Hypotheses 1, 2, 3, and 4 (H1, H2, H3, and H4) are accepted, affirming the substantial role of investor sentiments, overconfidence, over/under reaction, and herding behavior in shaping investment decisions.

Indirect moderating effect

In the context of the COVID-19 pandemic and its associated risks, the impact of behavioral factors (investor sentiments, overconfidence, over/under reaction, and herding behavior) on investment decisions tends to diminish. The findings presented in Table 6 and illustrated in Fig. 7 indicate that COVID-19 information sharing significantly and negatively moderates the relationship between these factors and investment decisions, leading to the acceptance of Hypotheses 5, 6, 7, and 8 (H5, H6, H7, and H8). The negative beta values underscore that the presence of COVID-19 adversely influences investors’ behavior, steering them away from rational investment decisions. This demonstrates that the pandemic context acts as a moderating factor, altering how behavioral biases impact investment choices, ultimately guiding investors towards more cautious or altered decision-making processes.

Moderating effect of Covid-19 pandemic information sharing.

K-means clustering analysis

K-means clustering analysis is utilized to uncover natural groupings within datasets by analyzing similarities between observations. This technique is especially beneficial for managing large and complex datasets as it reveals patterns and relationships among variables that may not be immediately evident. In this study, K-means clustering helps identify natural groupings based on socio-demographic factors, cognitive biases regarding investment decisions, and COVID-19 pandemic information sharing, thereby offering insights into the data’s underlying structure and identifying potential patterns or relationships among key variables.

The cluster analysis aims to ascertain the feature importance value of groups with similar investor behaviors, which is crucial for determining agents’ investment functions in subsequent agent-based modeling. Selecting the appropriate number of clusters in the K-means algorithm is essential, yet challenging, as different numbers of clusters can yield varying results (Li and Wu 2012 ).

Two prevalent methods for determining the optimal number of clusters are:

Elbow Method: This approach involves running the K-means algorithm with varying cluster numbers and calculating the total sum of squared errors (SSE) for each. SSE represents the squared distances of each data point from its cluster’s centroid. Plotting the SSE values against the number of clusters reveals a point known as the “elbow,” where the rate of SSE decrease markedly slows, indicating the optimal cluster number (Syakur et al. 2018 ).

Silhouette Analysis: Not mentioned directly in the narrative, but it’s another method that measures how similar an object is to its own cluster compared to other clusters. The silhouette score ranges from −1 to 1, where a high value indicates the object is well matched to its own cluster and poorly matched to neighboring clusters.

The sklearn library provides tools for implementing the elbow method and silhouette analysis. For example, the code snippet described applies the elbow method by varying the number of clusters from 1 to 10 and calculating SSE for each scenario. The optimal number of clusters is identified by selecting a value near the elbow point on the resulting plot.

After clustering, the analysis progresses by using the fit () method from sklearn’s K-Means class to cluster the data, determine each cluster’s center coordinates, and assign each data point to a cluster. Feature importance values can be calculated using the Extra Trees Classifier class from sklearn, and these values can be visualized through a line graph.

Finally, to illustrate the clusters’ membership to the CIS1, CIS2, CIS3, and CIS4 inputs as a color scale bar, the seaborn library is used (see Fig. 8 (top) and Fig. 8 (bottom)). This involves calculating the average membership values for each cluster and visualizing these averages, providing a clear depiction of how each cluster associates with the different inputs, enriching the analysis of investor behaviors and their responses to COVID-19 information sharing.

Elbow method sum of squared error class determination (top) and clustering analysis results (bottom).

After employing a network diagram constructed from a correlation matrix to elucidate the interrelationships among variables, and utilizing the Elbow method to ascertain the optimal number of clusters, the K-means clustering algorithm was applied (see Fig. 9 ). This approach successfully identified three distinct clusters, highlighting the variables that exerted a significant influence on these clusters. Notably, the COVID-19 pandemic information sharing variable, along with its corresponding CIS1, CIS2, CIS3, and CIS4 values, emerged as significant factors. The analysis indicated that overconfidence and overreaction were the predominant factors in crucial clustering, alongside cognitive biases and investment strategies that lead to similar behaviors among investors and varying levels of impact from COVID-19.

Cluster analysis feature importance value results.

Furthermore, sociodemographic factors such as age, occupation, and investor type were also identified as influential determinants. Leveraging these insights, policymakers and researchers can develop an agent-based model that incorporates herd behavior, along with age and income levels categorized by occupation, to effectively simulate market dynamics. This approach facilitates a comprehensive understanding of how different factors, particularly those related to the COVID-19 pandemic, influence investor behavior and market movements, thereby enabling the formulation of more informed strategies and policies.

An ingenious agent-based simulation for herding behavior

In this study, the findings of behavioral economics and finance research may contain results that are easy to interpret for policymakers but may involve certain difficulties in practical implementation. Specifically, for policymakers, an agent-based model has been created (see Appendix 1 for pseudo codes. In case, requested python codes are available). In a model consisting of 223 agents who trade on a single stock, prototypes of investors have been created based on the analysis presented here, and characteristics such as age group and income status, which are relatively easy to access or predict regarding their socio-demographic profiles, have been taken into account in the herd behavior function, considering the decision to follow the group or make independent decisions. Younger and lower-income agents were allowed to exhibit a greater tendency to follow the group, while 50 successful transactions were monitored to determine in which trend of stock price increase or decrease the balance of the most successful agent was increased or decreased (Gervais and Odean 2001 ).

In addressing the influence of age and income status on herding behavior, it is imperative to underscore the nuanced interplay between various socio-economic and psychological factors within our agent-based model framework. The model’s robustness stems from its capacity to simulate a range of investor behaviors by integrating key determinants such as investor sentiment, overconfidence, reaction to market events, and socio-demographic characteristics. Herein we expound on the contributory elements:

Investor Sentiment (IS1–IS5)

The model encapsulates the variability of investor sentiment, which oscillates with age and income, influencing individuals’ financial perspectives and risk propensities. Younger investors’ sentiment may tilt towards optimism driven by a more extensive investment horizon, while lower-income investors’ sentiment could lean towards caution, primarily driven by the pressing requirement for financial dsecurity (Baker and Wurgler 2007 ).

Overconfidence (OF1–OF5)

The tendency towards overconfidence is dynamically modeled, particularly among younger investors who may overrate their market acumen and predictive capabilities. This overconfidence may also manifest among lower-income investors as a psychological compensatory mechanism for resource inadequacy (Malmendier and Tate 2005 ).

Over/Under Reaction (OUR1–OUR5)

The model accounts for the influence of age and income on the velocity and extent of response to market stimuli. Inexperienced or financially restricted investors may be prone to overreactions due to a lack of market exposure or intensified economic strain (Daniel et al. 1998 ).

Herding Behavior (HB1–HB4)

Within the simulated environment, herding is more pronounced among younger investors, possibly due to peer influence, and among lower-income investors who may seek safety in conformity (Bikhchandani et al. 1992 ).

Investment Decision (ID1–ID10)

The model intricately reflects the complexities of investment decisions influenced by age-specific factors such as projected earnings and lifecycle influences. Investors with limited income may exhibit a predilection for security, swaying their investment choices (Yao and Curl 2011 ).

COVID-19 Information Sharing (CIS1–CIS4)

The pandemic era’s nuances are integrated into the model, acknowledging that younger investors could be more susceptible to digitally disseminated information, which, in turn, impacts their investment decisions. The credibility and source of information are also calibrated based on income levels (Shiller 2020 ).

Socio-demographic factors

Age: The model simulates younger investors’ reliance on the conduct of others, utilizing it as a heuristic substitute for experience (Dobni and Racine 2016 ).

Occupation: It captures how occupational background can broaden or restrict access to information and influence herding tendencies (Hong et al. 2000 ).

Gender: Gender disparities are incorporated, reflecting on investment styles where men may be more disposed to herding due to overconfidence (Barber and Odean 2001 ).

Qualification (Qualif.): The model acknowledges that higher education and financial literacy levels can curtail herding by fostering self-reliant decision-making (Lusardi and Mitchell 2007 ).

Investor Type (InvTyp): It differentiates between retail and institutional investors, noting that limited resources might push retail investors towards herding (Nofsinger and Sias 1999 ).

Investment Objective (InvObj): The model recognizes that short-term objectives might amplify herding as investors chase swift gains (Odean 1998 ).

Purpose: It contemplates the conservative herding behavior that is aligned with goals like retirement savings (Yao and Curl 2011 ).

Investment Horizon (Horizon): A lengthier investment horizon is modeled to potentially dampen herding tendencies (Kaustia and Knüpfer 2008 ).

Factors Considered Before Investing (factors): The model simulates a range of investment considerations, including risk tolerance and expected returns, which influence herding propensities (Shefrin and Statman 2000 ).

Source of Investment Advice (source): The influence of advice sources, such as analysts or financial media, on herding is also captured within the model (Tetlock 2007 ).

In conclusion, the agent-based model we present is meticulously designed to reflect the intricate fabric of financial market behavior. It is particularly attuned to the multi-layered aspects that drive herding, informed by empirical evidence and theoretical underpinnings that rigorously define the interrelations between investor demographics and market behavior. The aforementioned socio-economic and psychological facets provide a comprehensive backdrop against which the validity and consistency of the model are substantiated.

The following code has been prepared using Python programming language with the Mesa, Pandas, SciPy, NumPy, Random and Matplotlib libraries. This code simulates a herd behavior of stock traders in a simple market (Hunt and Thomas 2010 ; McKinney 2010 ; Harris et al. 2020 ; Virtanen et al. 2020 ; Van Rossum 2020 ; Hunter 2007 ). The simulation runs for 50-time steps, with the stock price and balance of each agent printed at each step. The decision-making process of agents in the simulation is stochastic, with agents randomly choosing to buy, sell, or follow the market trend based on their characteristics and decision-making strategy.

The Stock Trader class in the model symbolizes individual agents, each characterized by a unique ID, balance, and a stock price. These agents are equipped with a method to compute the current stock price. The step() function within each agent embodies their decision-making process, which is influenced by their current balance and the prevailing stock price. Agents have the option to buy, sell, or align with the market trend, reflecting various investment strategies.

The Herding Model class encapsulates the entire simulation framework. It generates a population of Stock Trader agents and progresses the simulation over a designated number of time steps. Within this class, the agent_decision() method orchestrates each agent’s decision-making, factoring in individual characteristics and strategies. The step() method, in turn, adjusts the stock price based on the aggregate current stock prices of all agents before executing the step() method for each agent, thereby simulating the dynamic nature of the stock market.

Socio-demographic factors, specifically age and income status, are integrated into the agent-based model simulations, drawing upon insights from Parallel Coordinates and Cluster Analysis as well as relevant literature. The simulation posits that agents of younger age and lower income are predisposed to mimicking the market trend, whereas other agents exhibit a propensity for independent decision-making. Given the stochastic nature of the decision-making process, the behavior of agents varies across different runs of the simulation, introducing an element of unpredictability.

At each time step, the simulation outputs the stock price and balance of each agent, offering a snapshot of the market dynamics at that moment. Figure 10 provides a flow diagram elucidating the operational framework of the model’s code, presenting a visual representation of how the simulation unfolds over time.

Flowchart of agent-based model.

This model architecture allows for the exploration of how socio-demographic characteristics influence investment behaviors within a simulated market environment, offering valuable insights into the mechanisms driving market trends and individual investor decisions.

Within our agent-based model (ABM), “performance” embodies multiple dimensions reflective of the agents’ investment outcomes, influenced by socio-demographic factors and behavioral biases. The provided pseudo-code conceptualizes the implementation of these facets in the model.

Metrics used to quantify agent performance

Balance trajectory.

This primary indicator tracks the evolution of each agent’s financial balance over time, reflecting the impact of their buy, sell, or market trend-following decisions (Arthur 1991 ).

Decision strategy efficacy

Evaluates the effectiveness of an agent’s decision-making strategy (‘buy’, ‘sell’, or ‘follow’), influenced by socio-demographic variables such as age and income, as delineated in the agent_decision method (Tesfatsion and Judd 2006 ).

Market trend alignment

Assesses the correlation between an agent’s balance trajectory and overall market trends, indicating successful performance if an agent’s balance increases with market prices (Shiller 2003 ).

Risk management

Infers risk management skill from the volatility of balance changes, with less volatility indicating stable and potentially successful investment strategies (Markowitz 1952 ).

Wealth accumulation

Agents are ranked by their final balance at the simulation’s end to identify the most financially successful outcomes (De Long et al. 1990 ).

Adaptive behavior

The model evaluates agents’ adaptability to market price changes, revealing their capacity to capitalize on market movements (Gode and Sunder 1993 ).

Herding influence

Considers how herding behavior impacts financial outcomes, especially for younger and lower-income agents as programmed in the Herding Model class (Bikhchandani et al. 1992 ).

These performance metrics are quantified through agents’ balance and stock price histories, updated at each simulation step. These histories offer a time series analysis of financial trajectories, enabling pattern identification such as herding tendencies or the effects of overconfidence.

The model’s realism is enhanced by parameters like young_follow_factor and low_income_follow_factor, adjusting the propensity for herding among different socio-demographic groups. This inclusion allows the model to reflect real-world dynamics where age and income significantly impact investment performance.

In conclusion, our ABM presents a detailed framework for examining investment performance’s complex nature. It integrates behavioral economics and socio-demographic data, providing insights into investor behavior under simulated market conditions.

Characteristics of agents in the agent-based model

Demographics (age and income): Consistent with the focus of our study on socio-demographic factors, each agent is characterized by age and income parameters, which influence their investment behavior, particularly their propensity towards herding. Age and income are randomly assigned within realistic bounds reflecting the demographic distribution of typical investor populations.

Cognitive biases: Agents are imbued with behavioral attributes such as overconfidence, herding instinct, and over/under-reaction tendencies to market news, reflecting the psychological dimensions of real-world investors.

Investment strategy: Each agent follows a distinct investment strategy categorized broadly as ‘buy’, ‘sell’, or ‘follow’ (herding). The strategy is influenced by the agent’s demographic characteristics and cognitive biases.

Adaptability: Agents are capable of learning and adapting to market changes over time, simulating the dynamic and evolving nature of real-world investor behavior.

Social influence: Agents are influenced by other agents’ behaviors, especially under conditions conducive to herding, modeling the social dynamics of investment communities.

Wealth and portfolio: Agents have a variable representing their wealth, which fluctuates based on investment decisions and market performance. Their portfolio composition and changes therein are also tracked, offering insights into their risk-taking and diversification behaviors.

Significance of agent-based modeling

Agent-based modeling is a powerful tool that allows researchers to simulate and analyze complex systems composed of interacting agents. Its significance and utility in various fields, including economics and finance are profound:

Complexity and emergence: ABM can capture the emergent phenomena that arise from the interactions of many individual agents, providing insights into complex market dynamics that are not apparent at the individual level (Epstein and Axtell 1996 ).

Customizability and scalability: ABMs can be tailored to include various levels of detail and complexity, allowing for the simulation of systems ranging from small groups to entire markets (Tesfatsion and Judd 2006 ).

Experimental flexibility: ABMs facilitate virtual experiments that would be impractical or impossible in the real world, enabling researchers to explore hypothetical scenarios and policy implications (Gilbert and Troitzsch 2005 ).

Realism in behavioral representation: By incorporating cognitive biases and decision-making rules, ABMs can realistically represent human behavior, providing deeper behavioral insights than models assuming perfect rationality (Hommes 2006 ).

Policy analysis and forecasting: In economics and finance, ABMs are particularly useful for policy analysis, risk assessment, and forecasting, as they can incorporate a wide range of real-world factors and individual behaviors (LeBaron and Tesfatsion 2008 ).

By integrating these agent characteristics into our ABM and considering the broader implications of agent-based modeling, our study aims to provide nuanced insights into herding behavior among investors. We believe that our approach not only aligns with best practices in the field but also significantly contributes to the understanding of complex investment behaviors and market dynamics. We trust that this expanded description addresses the reviewer’s comment and underscores the robustness and relevance of our agent-based simulation approach.

Figure 11a, b panels display the balance changes of agents with respect to stock prices, age, and income status. By coding the balance increases and decreases as +1 and −1, respectively, and employing a line graph that matches the changes in stock prices, it has become possible to provide information about the agents’ performance. In panels a and b, it is observed that agents created after the age of 37.5 have been included in the higher income group on average, and during transitions of stock prices below 12.75 units, between 17 and 20 units, and between 26 and 27.50 units, the agents’ responses to price state changes are accompanied by noticeable transitions (increases and decreases) in their portfolio states, depending on age and income status.

a Agents’ performance. b Agents’ responses.

In Fig. 12 , in the agent-based model’s 50 repeated simulations, at the 45th simulation, the stock price is 20.03 units, and the balance of agent number 74 reaches 911 units. The price-income-balance change graph for the agent throughout the 50 transactions is presented below.

Balance change according to stock price for agent 74.

Upon examining the descriptive statistics of the income for agent number 74, who diverges from the herding tendency profile of the model and is in the higher income group aged 40 and above, the highest balance value is 911 units, the lowest balance level is 732 units, the average is 799 units, and the standard deviation is 41 units. When the overall balance of the agents is investigated, it is observed that the average balance of the agents is around 84 units. Considering the existence of an agent with the lowest balance of −670 units, it can be concluded that agent number 74 has demonstrated a significantly superior performance.

Discussion and conclusion

The influence of behavioral biases on investors’ decision-making has yielded mixed findings in literature. Wan ( 2018 ) observed a positive impact of behavioral biases, considered forward-looking factors, on investment decisions. Conversely, Zulfiqar et al. ( 2018 ) noted a markedly negative impact of overconfidence on investment decisions. Similarly, Aziz and Khan ( 2016 ) explored the role of heuristic factors (representative, anchoring, overconfidence, and availability bases) and found them significantly influencing investment decision and performance. However, they reported that prospect factors (loss aversion, regret aversion, and mental accounting biases) had an insignificant impact on these outcomes.

These varied results may stem from a complex interplay of factors such as cultural differences, pandemic-related information, economic conditions, regulatory environments, historical context, and investors’ financial literacy levels, contributing to differences in how behavioral biases influence investment decisions across regions (Metawa et al. 2018 ).

This study contributes to the field of behavioral finance by revealing the moderating role of COVID-19 pandemic information sharing on the relationship between behavioral quirks and investment choices, specifically in the context of Pakistan. Key contributions include:

Investors’ sentiments

This study shows that COVID-19 pandemic information sharing significantly moderates the relationship between investors’ sentiments and their investment decisions, validating that pandemic-related information, such as infection rates and economic downturns, heavily influences investors’ sentiments and alters their risk perceptions (Anastasiou et al. 2022 ; Hsu and Tang 2022 ; Bin-Nashwan and Muneeza 2023 ; Gao et al. 2023 ; Sohail et al. 2020 ).

Overconfidence

It reveals how COVID-19 information reshapes overconfident investors’ risk perceptions, urging them to reassess their investment portfolios in light of the pandemic’s uncertainties and economic implications (Bouteska et al. 2023 ; Li and Cao 2021 ).

Over/under reaction

The study uncovers that the pandemic information moderates the relationship between over-under reaction and investment decisions, suggesting that investors adjust their reactions based on evolving pandemic information, leading to more informed and rational investment choices (Jiang et al. 2022 ).

Herd behavior

It finds that COVID-19 pandemic information significantly reduces herd behavior among investors, encouraging them to make rational decisions rather than blindly following the majority (Nguyen et al. 2023 ).

In conclusion, this study illustrates that the COVID-19 pandemic has significantly moderated the relationship between behavioral biases and investment decisions. Furthermore, clustering analyses and agent-based outcomes suggest that younger, less experienced agents prone to herding behavior exhibit a higher propensity for such behavior and demonstrate lower performance in agent-based models. These findings pave the way for further research into additional cognitive biases and socio-demographic variables’ effects on investment decisions.

Implications

This study contributes to the field of behavioral finance that COVID-19 pandemic information sharing significantly moderates the relationship between behavioral biases (e.g., investors’ sentiments, overconfidence, over/under reaction, and herd behavior) and investment decisions. Therefore, policy implications stem from findings are substantial, and thus addressing behavioral biases during COVID-19 pandemic to mitigate the market inefficiencies and promote better decision-making. First, this study suggests that investing in comprehensive financial education plans will enhance the financial literacy of investors and enable them to better recognize the behavioral biases during times of uncertainty and crises. Second, findings imply that accurate and transparent information sharing about COVID-19 pandemic can better mitigate the behavioral biases, especially government interventions (e.g., National Command and Coordination Centre) ensuring reliable information can lead the investors to make more rational and informed investment decisions during the time of uncertainty and crises. Last, findings provide insights to policy makers that pandemic news and developments significantly influenced behavioral biases of investment decisions (Khurshid et al. 2021 ). For example, news about number of causalities, infection rates, vaccine progress, government stimulus packages, or stock market downturns had immediate effects on behavioral biases especially when an investor is overconfidence, over/under reaction, and herd behavior. In this sense, enhancing information transparency about COVID-19 news in media can reduce the influence of sensationalized news on investor decisions.

Limitations and call for future research

This study significantly enhances the understanding of behavioral factors’ impact on investors’ decision-making processes, presenting important findings within the context of the COVID-19 pandemic. While these contributions are notable, the research is subject to certain limitations that pave the way for future exploration and deeper investigation into this complex field.

Firstly, the study underscores the necessity for further research to validate its results through larger sample sizes and a more diverse array of respondents. Adopting a longitudinal design could prove particularly insightful, enabling an analysis of behavioral biases across different stages of the pandemic and providing a dynamic perspective on how investor behaviors evolve over time.

In addition, there’s a highlighted opportunity for future studies to delve into the behaviors influencing institutional investor decisions within Pakistan. The complex decision-making processes and investment portfolios of institutional investors, coupled with challenges like data availability and the heterogeneity among institutions, present a fertile ground for investigation. Such research could unravel how various factors, including market conditions and macroeconomic assessments, impact institutional investment strategies.

The study also points out the need to broaden the investigation to include other potential behavioral factors beyond those focused on in the current research, such as loss aversion, personality traits, anchoring, and recency biases. Expanding the scope of behavioral factors examined could significantly enrich the behavioral finance field by offering a more comprehensive view of the influences on investment decisions.

Moreover, while the insights gained from a Pakistani context during the COVID-19 pandemic are invaluable, extending the research to include global (e.g., China, Japan, USA) and other emerging markets (e.g., BRICS) would enhance understanding of the universality or specificity of behavioral biases in investment decisions across various economic, cultural, and regulatory environments.

Lastly, the study’s reliance on quantitative data points to the potential benefits of incorporating qualitative data into future research. Undertaking case studies within specific securities brokerages or investment banks could provide an in-depth investigation of investor behavior, generating new insights that could inspire further research.

To support the development of more sophisticated agent-based models and to foster collaborative research efforts, the study makes its source code available to other researchers. This openness to collaboration promises to stimulate innovative approaches to understanding and modeling investor behavior across diverse contexts, contributing to the advancement of the behavioral finance field.

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Department of Business Administration, University of the Punjab, Gujranwala Campus, Gujranwala, Pakistan

Wasim ul Rehman

Manager of Economics Research Department, Marbas Securities Co., Istanbul, Turkey

Omur Saltik

Institute of Quality and Technology Management, University of the Punjab, Lahore, Pakistan

Faryal Jalil

Department of Economics, Mersin University, Mersin, Turkey

Suleyman Degirmen

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All authors contributed equally to this research work.

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Correspondence to Wasim ul Rehman .

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Ethical approval

The data was collated through an online survey approach (questionnaire) during the last variant of COVID-19 where anonymity of the respondents is meticulously preserved. The respondents were not asked to provide their names, identification, address, or any other identifying elements. The authors minutely observed the ethical guidelines of the Declaration of Helsinki. In addition, we hereby certify that this study was conducted under the ethical approval guidelines of Office of Research Innovation and Commercialization, University of the Punjab granted under the office order No. D/ 409/ORIC dated 31-12-2021.

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The consent of participants was obtained through consent form during the last variant of COVID-19. The consent form contains the title of study, intent of study, procedure to participate, confidentiality, voluntary participation of respondents, questions/query and consent of the respondents. The respondents were requested to provide their willingness to participate in survey on consent form via email before filling the online-surveyed (questionnaire). Further, participants were also assured that their anonymity would be maintained and that no personal information or identifying element would be disclosed. The consent form is in the supplementary files.

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Rehman, W.u., Saltik, O., Jalil, F. et al. Viral decisions: unmasking the impact of COVID-19 info and behavioral quirks on investment choices. Humanit Soc Sci Commun 11 , 524 (2024). https://doi.org/10.1057/s41599-024-03011-7

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Received : 17 June 2023

Accepted : 28 March 2024

Published : 20 April 2024

DOI : https://doi.org/10.1057/s41599-024-03011-7

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Evaluation of angiotensin converting enzyme 2 (ACE2), angiotensin II (Ang II), miR-141-3p, and miR-421 levels in SARS-CoV-2 patients: a case-control study

• Ehsan Kakavandi 1 ,
• Kaveh Sadeghi 1 ,
• Mohammad Shayestehpour 2 ,
• Hossein Mirhendi 3 ,
• Abbas Rahimi Foroushani 4 ,
• Talat Mokhtari-Azad 1 ,
• Nazanin Zahra Shafiei Jandaghi 1 &
• Jila Yavarian 1 , 5  

BMC Infectious Diseases volume  24 , Article number:  429 ( 2024 ) Cite this article

Metrics details

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious virus that uses angiotensin converting enzyme 2 (ACE2), a pivotal member of the renin–angiotensin system (RAS), as its cell-entry receptor. Another member of the RAS, angiotensin II (Ang II), is the major biologically active component in this system. There is growing evidence suggesting that serum miRNAs could serve as prognostic biomarkers for SARS-CoV-2 infection and regulate ACE2 expression. Therefore, the aim of this study is to evaluate the changes in the serum levels of sACE2 and Ang II, as well as the expression level of miR-141-3p and miR-421 in SARS-CoV-2 positive and negative subjects.

In the present study, the serum levels of sACE2 and Ang II were measured in 94 SARS-CoV-2 positive patients and 94 SARS-CoV-2 negative subjects with some symptoms similar to those of SARS-CoV-2 positive patients using the ELISA method. In addition, the expression level of miR-141-3p and miR-421 as ACE2 regulators and biomarkers was evaluated using quantitative real-time PCR (qRT-PCR) method.

The mean serum sACE2 concentration in the SARS-CoV-2-positive group was 3.268 ± 0.410 ng/ml, whereas in the SARS-CoV-2 negative group, it was 3.564 ± 0.437 ng/ml. Additionally, the mean serum Ang II level in the SARS-CoV-2 positive and negative groups were 60.67 ± 6.192 ng/L and 67.97 ± 6.837 ng/L, respectively. However, there was no significant difference in the serum levels of sACE2 ( P value: 0.516) and Ang II ( P value: 0.134) between the SARS-CoV-2 positive and negative groups. Meanwhile, our findings indicated that the expression levels of miR-141-3p and miR-421 in SARS-CoV-2 positive group were significantly lower and higher than SARS-CoV-2 negative group, respectively ( P value < 0.001).

Conclusions

Taken together, the results of this study showed that the serum levels of sACE2 and Ang II in SARS-CoV-2 positive and negative subjects were not significantly different, but the expression levels of miR-141-3p and miR-421 were altered in SARS-CoV-2 positive patients which need more investigation to be used as biomarkers for COVID-19 diagnosis.

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Introduction

At the end of 2019, a novel coronavirus named severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) was first officially reported in Wuhan, the capital of Hubei Province in China. However, a number of studies have indicated that the virus was already prevalent in some parts of the world prior to this report [ 1 , 2 ]. SARS-CoV-2 was highly transmissible which spread all over the world, resulting in a serious pandemic. The clinical symptoms of COVID-19 (Coronavirus disease 2019) are fever, fatigue, myalgia, cough (most common), headache, sore throat, diarrhea, and loss of smell and taste (less common). A better understanding of SARS-CoV-2 pathogenesis may lead to the identification of more effective therapeutic and preventive strategies. Current studies suggest that the pathogenesis of SARS-CoV-2 pneumonia comprises of two phases. The first phase is viral replication and subsequent direct tissue damage by the replicating virus. The second phase includes immune hyperactivity (the recruitment of immune cells that result in local/systemic inflammatory responses) [ 3 , 4 ].

The SARS-CoV-2 receptor is angiotensin-converting enzyme 2 (ACE2), which plays a critical role in the pathogenesis of COVID-19 [ 5 ]. ACE2 is a transmembrane glycoprotein that is located on the cell membrane (known as the membrane-bound form or mACE2), but does not always remain on the cell surface. For instance, it can be detached through an event called shedding induced by disintegrin and metalloprotease 17 (ADAM17), which produces the soluble form of ACE2 (sACE2), resulting in the loss of mACE2 [ 6 ]. ACE2 has important functions in the renin–angiotensin system (RAS): blood pressure maintenance and electrolyte homeostasis [ 7 , 8 , 9 ]. In the RAS, after releasing renin into the blood, it cleaves angiotensinogen to angiotensin I (Ang I), and then ACE catalyzes the conversion of Ang I to Ang II. Ang II, the major biologically active component of the RAS, has several effects, including vasoconstriction, hypertension, thrombosis, and inflammation. The final consequence of the RAS axis depends on the balance between ACE and ACE2 [ 10 ]. Additionally, previous studies have shown that there are possible correlations between RAS and the clinical outcomes/pathogenesis of acute respiratory distress syndrome (ARDS) [ 11 , 12 , 13 , 14 ]. The binding of the SARS-CoV-2 spike protein to the cell surface of ACE2 results in mACE2 cleavage at the enodomain and ectodomain sites. This process triggers the shedding of cellular mACE2 receptors, which results in their systemic release into the blood circulation, which is associated with the severity of COVID-19 [ 15 , 16 ]. On the other hand, several studies have demonstrated that Ang II levels are correlated with the severity and mortality of respiratory virus-infected patients [ 17 ].

However, despite rapid progress in understanding of the pathophysiological roles of ACE2, little is known about the mechanisms that regulate its expression. Numerous investigations have documented that microRNAs (miRNAs) possess the ability to regulate ACE2 expression in various diseases [ 18 , 19 , 20 ]. miRNAs are small non-coding RNAs (with an average of 22 nucleotides) capable of negatively regulating gene expression [ 21 ]. There is growing evidence suggesting that serum miRNAs could serve as prognostic biomarkers for SARS-CoV-2 infection. Various studies have shown the regulation of ACE2 expression by different miRNAs, including miR-17-5p, miR-5197-3p, miR-212-p, miR-20b-5p, miR-4677-3p, and miR-3909 which have been demonstrated to have a direct impact on the SARS-CoV-2 genome, thereby inhibiting its post-transcriptional expression [ 18 , 22 ]. ACE2 is also post-transcriptionally regulated by miR-141-3p and miR-421 [ 18 , 19 , 23 ]. However, there are few studies which have investigated the expression of miR-141-3p and miR-421 in COVID-19 patients, in the present study, we aimed to investigate the serum levels of sACE2 and Ang II, as well as the expression levels of miR-141-3p and miR-421 in SARS-CoV-2-positive and negative subjects.

Materials and methods

Patient sample collection.

One hundred eighty-eight outpatients were enrolled in the present study between June and August 2022, which divided into case and control groups. The inclusion criteria for the case group were having symptoms related to COVID-19 and a positive result of the SARS-CoV-2 quantitative real-time polymerase chain reaction (qRT‒PCR) test. For the control group, the inclusion criteria were having symptoms related to COVID-19 and a negative result of the SARS-CoV-2 qRT-PCR test.

These patients were referred to a private medical laboratory for the SARS-CoV-2 PCR diagnostic test (Vahid Laboratory, Isfahan, Iran) according to the decision of their physicians. The study was approved by the medical ethics committee of Tehran University of Medical Sciences, and written informed consent was obtained from all study subjects before enrollment (ethical approval number: IR.TUMS.SPH.REC.1400.145). The swab specimens for detection of SARS-CoV-2 were obtained from both the oropharynx and nasopharynx and placed in viral transport media (VTM). Simultaneously, five milliliters of blood was obtained from all study participants in clot activator-containing tubes for the determination of Ang II, sACE2 and miRNAs levels. Participants who used any drug that might affect Ang II or ACE2 (Ang II receptor blockers and ACE blockers) or who had any kidney/heart disease were excluded. The blood samples were allowed to coagulate for 30 to 60 min at room temperature (22–25 °C) and then centrifuged at 1200 relative centrifugal force (RCF) for 15 min. The sera were separated, transferred to new tubes, and stored at -70 °C until analysis without freeze‒thaw cycles. The Fig.  1 shows the study flow diagram.

Study flow diagram

Viral nucleic acid extraction and SARS-CoV-2 detection by qRT‒PCR

SARS-CoV-2 nucleic acid was extracted from 200 µL of each swab specimen using a commercially available viral RNA/DNA extraction kit in a silica column (Payesh Gene Rasti, Iran) according to the manufacturer’s instructions. The extraction product was stored in -70 °C until the SARS-CoV-2 detection step. SARS-CoV-2 genome detection was performed by a one-step TaqMan probe-based RT‒PCR method using a commercial kit targeting the RNA-dependent RNA polymerase (RdRp) and nucleocapsid (N) genes (Pishtaz Teb, Tehran, Iran). All RT‒PCR protocols were run based on the manufacturer’s instructions on an AusDiagnostics real-time PCR system (AusDiagnostics Pty. Ltd., Sydney, Australia).

Analysis of serum sACE2 and Ang II levels

Serum sACE2 and Ang II levels were measured using a commercially available sandwich enzyme-linked immunosorbent assay (ELISA) kit (Bioassay Technology Laboratory, Shanghai, China) according to the manufacturer’s protocol. The assay ranges for Ang II and sACE2 were 1 to 350 ng/L and 0.05 to 20 ng/ml, respectively. The sensitivity of the assay was 0.52 ng/L for Ang II and 0.021 ng/ml for sACE2. The mean intra- and interassay coefficients of variation (CVs) for sACE2 and Ang II were < 8% and < 10%, respectively. ELISA standard curves were drawn, and the corresponding Ang II and sACE2 concentrations were subsequently determined according to the optical density (OD) data.

Evaluation of mir-141-3p and miR-421 expression

Total RNA was extracted from all serum samples using a total RNA isolation kit (Bon Yakhteh, Tehran, Iran) according to the manufacturer’s instructions. The RNA purity and concentrations were determined using a NanoDrop ND-1000 spectrometer (Thermo Fischer Scientific, USA). Then, reverse transcription of miR-141-3p and miR-421 was carried out using the BONmiR 1st-strand cDNA synthesis kit (Bon Yakhteh, Tehran, Iran). Subsequently, qRT-PCR was performed using BON- High-Specificity microRNA qPCR Master mix (Bon Yakhteh, Tehran, Iran) and Rotor-Gene Q system (Qiagen, Hilden, Germany). The U6 snRNA was used as an internal reference gene and fold-change expression of miRNAs was evaluated using 2 −ΔΔCt method [ 24 ].

Statistical analysis

The data analysis was performed using SPSS software version 18.0 (SPSS, Inc. Chicago, USA) and GraphPad Prism program 8.0 (GraphPad Software, Inc.). A nonparametric Mann‒Whitney U test was used for variables that were not normally distributed. Normality was determined by the Kolmogorov–Smirnov test. Fisher’s exact test was used to calculate the male/female ratio. In addition, the correlation between miR-141-3p and miR-421expression with sACE2 and Ang II levels was analyzed using Pearson test. P value < 0.05 was considered statistically significant.

Demographic characteristics

Demographic characteristics of the patients and controls are shown in Table  1 . The gender distribution in the two studied groups were not significantly different ( P value: 0.372). Most of the patients in the case and controls were in the age group of 25–34 years.

Serum sACE2 and Ang II levels

Based on the ELISA protocol, standard curves were established for sACE2 and Ang II (Fig.  2 A and B). Then, according to the standard curves, the serum Ang II and sACE2 levels in SARS-CoV-2 positive and negative subjects were calculated.

Standard curve of ELISA for sACE2 ( A ) and Ang II ( B ) quantitation. OD; optical density

The mean serum sACE2 concentration in the SARS-CoV-2-positive group was 3.268 ± 0.410 ng/ml, whereas in the SARS-CoV-2 negative group, it was 3.564 ± 0.437 ng/ml. Additionally, the mean serum Ang II concentration in the SARS-CoV-2-positive and -negative groups were 60.67 ± 6.192 ng/L and 67.97 ± 6.837 ng/L, respectively. Statistical analysis revealed that there were no significant differences in the mean serum concentrations of sACE2 ( P value: 0.516) and Ang II ( P value: 0.134) between the SARS-CoV-2 positive and negative groups (Fig.  3 A and B). Additionally, serum sACE2 and Ang II levels were analyzed according to the gender. Neither females nor males had significantly different serum levels of sACE2 ( P value: 0.903) and Ang II ( P value: 0.384).

Serum sACE2 ( A ) and Ang II ( B ) levels in the SARS-CoV-2-positive (case) and -negative (control) groups. The data are presented as the mean ± SEM. The Mann-Whitney U test was used for comparisons between study groups

Expression of mir-141-3p and miR-421

The expression of miR-141-3p and miR-421 as regulators of ACE2 in the serum of SARS-CoV-2 negative and positive subjects was investigated using qRT-PCR method. Our findings indicated that the expression levels of miR-141-3p in SARS-CoV-2 positive group were significantly lower than SARS-CoV-2 negative group ( P value < 0.001) (Fig.  4 A). Meanwhile, the expression levels of miR-421 were significantly increased in the SARS-CoV-2 positive group compared to the SARS-CoV-2 negative group ( P value < 0.001) (Fig.  4 B). We next analyzed the correlations of miR-141-3p and miR-421 with sACE2 and Ang II levels in SARS-CoV-2-positive and negative population by Pearson correlation analysis. As shown in Fig.  5 , no significant correlation was observed between the expression of miR-141-3p and miR-421 with the sACE2 and Ang II levels, but there was a significant positive correlation between sACE2 and Ang II levels ( P value < 0.001).

Expression of miR-141-3p ( A ) and miR-421 ( B ) levels in the SARS-CoV-2-positive (case) and -negative (control) groups. The data are presented as the mean ± SEM. The Mann-Whitney U test was used for comparisons between study groups

Scatter plots representing the correlation between the expression of miR-141-3p and miR-421 with sACE2 and Ang II levels in SARS-CoV-2-positive and negative subjects

After COVID-19 was declared by the World Health Organization (WHO) as a pandemic in March 2020, extensive global efforts have been made to control the pandemic and mitigate the spread of SARS-CoV-2 via mass vaccination, face mask wearing, isolation, and the use of several therapeutic agents [ 25 , 26 ]. The pandemic caused by SARS-CoV-2 has highlighted the indispensable role of the RAS in the virus’s pathogenesis. The virus utilizes ACE2, an important component of RAS, as its host cell-entry receptor, and Ang II, another member of RAS, is the major biologically active component in this system. The interplay between these two elements and their potential impact on COVID-19 has been a subject of intense research [ 27 , 28 , 29 ]. Emerging evidence suggests that serum miRNAs, specifically miR-141-3p and miR-421, could serve as prognostic biomarkers for SARS-CoV-2 infection and may regulate sACE2 expression [ 19 , 30 ]. In this study the changes in the serum levels of sACE2 and Ang II, as well as the expression level of miR-141-3p and miR-421 in SARS-CoV-2 positive and negative subjects were evaluated.

Serum sACE2 and Ang II levels in SARS-CoV-2 positive patients compared to the controls

The results of this study showed that there was no significant differences in the mean serum concentrations of sACE2 and Ang II between the SARS-CoV-2 positive and negative groups. These results were consistent with the findings of several previous studies. Rieder et al. [ 31 ] analyzed the serum levels of sACE2 in 24 COVID-19-positive and 61 COVID-19-negative patients with similar symptoms admitted to the emergency unit. They reported no changes in serum sACE2 and Ang II levels between SARS-CoV-2 positive and the control groups. A meta-analysis study conducted by Naderi et al. found that the levels of ACE-2 were not significantly different when comparing severe COVID-19 patients with healthy controls or mild COVID-19 patients. Thus the authors proposed that sACE2 serum levels cannot be used as a biomarker to evaluate disease severity in COVID-19 patients [ 32 ]. Also, Hani et al. carried out a cross-sectional observational study on the association between serum sACE2 levels with the severity of COVID-19. They demonstrated that serum sACE2 levels did not change among SARS-CoV-2-positive and negative groups [ 33 ]. In addition, another study by Kintscher et al. reported that sACE2 levels and the Ang II/Ang I peptide ratio did not change in COVID-19 patients [ 34 ].

In contrast to these findings, several reports have shown that serum sACE2 levels were increased in COVID-19 patients and elevated sACE2 levels were associated with the severity of COVID-19 [ 35 , 36 , 37 , 38 , 39 ]. A study published in the scientific reports has compared the protein expression status of sACE2 in post-mortem lung specimens obtained from severe COVID-19 and non-COVID-19 patients, using immunohistochemistry (IHC). Their results showed markedly raised sACE2 protein expression in severe COVID-19 disease correlated with increased macrophage infiltration and microthrombi. The authors proposed that sACE2 might have a pathobiological role in the severity of COVID-19 [ 40 ]. In a large longitudinal 28-day study of 306 COVID-19 patients and 78 individuals without COVID-19, sACE2 plasma levels were measured, and the results showed that higher baseline sACE2 plasma levels in COVID-19 patients were significantly correlated with increased severity of the disease [ 41 ]. Increased serum sACE2 levels could be related to the spread of SARS-CoV-2 through the body and disease burden [ 42 ]. Increased lysis of mACE2-expressing cells as a result of severe infection could explain the possible causes of increased serum sACE2 levels in critically ill/deceased patients compared to mild or non-severe patients. Measurement of serum sACE2 in COVID-19 patients admitted to the hospital and control subjects showed that sACE2 serum levels were markedly greater in more severe and non-survivor cases of COVID-19 than in the milder patients [ 43 ]. Therefore, some studies have suggested that sACE2 can be used as a possible predictor marker of COVID-19 severity [ 33 ]. Contrary to these reports, several studies have demonstrated that serum/plasma sACE2 levels were significantly lower in severe COVID-19 patients than in the mild and healthy control subjects [ 44 ]. There is evidence that SARS-CoV-2 can downregulate sACE2 expression, which exacerbates disease symptoms [ 45 , 46 ]. A study showed that sACE2 serum levels were significantly lower in SARS-CoV-2 patients than in SARS-CoV-2 unexposed patients and also in hospitalized SARS-CoV-2 infected patients compared to discharged SARS-CoV-2 infected patients [ 47 ]. Although many studies have been conducted on the association of serum sACE2 and COVID-19, the obtained results were controversial. There are several possible reasons for this discrepancy: (I) geographic/ancestry-related differences in sACE2 and Ang II gene expression [ 48 , 49 ]. (II) disease severity, it is likely expected that if the serum levels of sACE2 and Ang II are measured in SARS-CoV-2-positive and -negative individuals, a significant difference may not be observed. However, if their levels are measured in more severe or non-survivor cases of COVID-19 compared to the milder patients, a statistically significant difference is more likely to occur. (III) The timing of the serum sample collection following a positive SARS-CoV-2 PCR test [ 47 ].

Mir-141-3p and miR-421 expression level in SARS-CoV-2 positive patients compared to the controls

MicroRNAs, particularly those found in circulation (circulating miRNAs), have been identified as key factors in COVID-19 infection [ 22 ]. Nevertheless, there is limited understanding regarding the roles of miRNAs in COVID-19. In the present study, we evaluated the expression of miR-141-3p and miR-421 as regulators of sACE2 in the serum of SARS-CoV-2 positive and negative population. Previous studies and bioinformatics analyses have shown that miR-141-3p and miR-421 can regulate the sACE2 transcript by binding to its 3’-UTR region [ 19 , 23 ]. According to Giannella et al., low serum levels of miR-141-3p, miR-4433b-5p, miR-23b-3p, miR-1-3p, and miR-155-5p were associated with increased mortality after hospitalization [ 50 ]. In a study conducted by Elemam et al. they evaluated the serum levels of sACE2, miR-421, miR-3909, miR-212-5p, and miR-4677-3p in COVID-19 patients. Their results revealed that serum levels of sACE2 and the four examined miRNAs were significantly increased in COVID-19 patients, indicating their promising role as biomarkers in COVID-19 diagnosis [ 18 ]. In line with these studies, our results also revealed that the expression level of miR-141-3p was significantly decreased in the SARS-CoV-2 positive compared to the SARS-CoV-2-negative groups. Meanwhile, the expression level of miR-421 was significantly increased in the SARS-CoV-2-positive compared to the SARS-CoV-2-negative groups.

Limitations of the study

There were several limitations in our study. The selection of patients with mild COVID-19 infection was one of the limitations. Indeed, we did not include the factor of COVID-19 severity in our study and did not quantify serum sACE2 and Ang II in more severe and milder COVID-19 patients. Additionally, the sample size in the case and control groups was small. Moreover, we did not follow up the patients and monitor changes in serum levels of sACE2 and Ang II at different time points. However, one of the strengths of our study was the selection of the control group as SARS-CoV-2-negative subjects with some symptoms similar to those of COVID-19 patients rather than healthy individuals without symptoms.

Taken together, the results of this study showed that the serum levels of sACE2 and Ang II in SARS-CoV-2 positive and negative subjects were not significantly different, but the expression of miR-141-3p and miR-421 was altered in SARS-CoV-2 and may serve as biomarkers for COVID-19 diagnosis. We suggest that future studies should investigate the serum levels of sACE2 and Ang II in SARS-CoV-2-positive and negative patients, simultaneously in more severe and milder cases of COVID-19, as well as at different time points. Additionally, we suggest that further clinical investigations with a large sample size are needed to evaluate miR-141-3p and miR-421 in SARS-CoV-2 patients to confirm the biomarker roles of these miRNAs.

Data availability

All data generated or analyzed during this study are included in this published article. Raw data is available from the corresponding author upon request.

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Acknowledgements

We would like to acknowledge all study participants and our colleagues at the National Influenza Centre of the Islamic Republic of Iran.

This study was conducted as part of the PhD thesis of the first author and was funded by the Tehran University of Medical Sciences (Grant No. 1400-3-99-55268). The authors would like to thank Dr. Hossein Mirhendi for his helpful financial support.

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Ehsan Kakavandi, Kaveh Sadeghi, Talat Mokhtari-Azad, Nazanin Zahra Shafiei Jandaghi & Jila Yavarian

Department of Bacteriology and Virology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Mohammad Shayestehpour

Department of Medical Parasitology and Mycology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Hossein Mirhendi

Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

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JY and EK designed the study. EK, KS, MSH, HM, TM, NSH, and JY performed the laboratory analysis. EK, ARF, and JY analyzed and interpreted the patient data. EK and JY prepared the draft. All authors read and approved the final manuscript.

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Kakavandi, E., Sadeghi, K., Shayestehpour, M. et al. Evaluation of angiotensin converting enzyme 2 (ACE2), angiotensin II (Ang II), miR-141-3p, and miR-421 levels in SARS-CoV-2 patients: a case-control study. BMC Infect Dis 24 , 429 (2024). https://doi.org/10.1186/s12879-024-09310-3

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Relying on the French territorial offer of thermal spa therapies to build a care pathway for long COVID-19 patients

Roles Conceptualization, Formal analysis, Methodology, Software, Validation, Visualization, Writing – original draft

Affiliation University of Clermont Auvergne, “Santé et Territoires” Resarch Chair, CleRMa, Clermont-Ferrand, France

Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Visualization

* E-mail: [email protected]

Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft

Roles Conceptualization, Funding acquisition, Project administration

Affiliation University of Clermont Auvergne, CRNH, AME2P, Clermont-Ferrand, France

Affiliations CHU Clermont-Ferrand, Service d’Immunologie, CHU Gabriel-Montpied, Clermont-Ferrand, France, University of Clermont Auvergne, INRA, UMR 1019, Clermont-Ferrand, France

Affiliation Centre Imagerie Cellulaire Santé, University of Clermont Auvergne, Clermont-Ferrand, France

Affiliations University of Clermont Auvergne, INRA, UMR 1019, Clermont-Ferrand, France, Service de Médecine du Sport et des Explorations Fonctionnelles, CHU de Clermont-Ferrand, Université Clermont Auvergne, INRA, UNH, Unité de Nutrition Humaine, CRNH Auvergne, Clermont-Ferrand, France

Affiliation Cluster Auvergne-Rhône-Alpes Innovation Innovatherm, Clermont-Ferrand, France

Roles Conceptualization, Data curation, Funding acquisition, Project administration

¶ Membership of the CAUVIM-19 research program is indicated in the "Acknowledgments" section.

• Milhan Chaze, 
• Laurent Mériade, 
• Corinne Rochette, 
• Mélina Bailly, 
• Rea Bingula, 
• Christelle Blavignac, 
• Martine Duclos, 
• Bertrand Evrard, 
• Anne Cécile Fournier, 

• Published: April 19, 2024
• https://doi.org/10.1371/journal.pone.0302392
• Peer Review
• Reader Comments

Work on long COVID-19 has mainly focused on clinical care in hospitals. Thermal spa therapies represent a therapeutic offer outside of health care institutions that are nationally or even internationally attractive. Unlike local care (hospital care, general medicine, para-medical care), their integration in the care pathways of long COVID-19 patients seems little studied. The aim of this article is to determine what place french thermal spa therapies can take in the care pathway of long COVID-19 patients.

Based on the case of France, we carry out a geographic mapping analysis of the potential care pathways for long COVID-19 patients by cross-referencing, over the period 2020–2022, the available official data on COVID-19 contamination, hospitalisations in intensive care units and the national offer of spa treatments. This first analysis allows us, by using the method for evaluating the attractiveness of an area defined by David Huff, to evaluate the accessibility of each French department to thermal spas.

Using dynamic geographical mapping, this study describes two essential criteria for the integration of the thermal spa therapies offer in the care pathways of long COVID-19 patients (attractiveness of spa areas and accessibility to thermal spas) and three fundamental elements for the success of these pathways (continuity of the care pathways; clinical collaborations; adaptation of the financing modalities to each patient). Using a spatial attractiveness method, we make this type of geographical analysis more dynamic by showing the extent to which a thermal spa is accessible to long COVID-19 patients.

Based on the example of the French spa offer, this study makes it possible to place the care pathways of long COVID-19 patients in a wider area (at least national), rather than limiting them to clinical and local management in a hospital setting. The identification and operationalization of two geographical criteria for integrating a type of treatment such as a spa cure into a care pathway contributes to a finer conceptualization of the construction of healthcare pathways.

Citation: Chaze M, Mériade L, Rochette C, Bailly M, Bingula R, Blavignac C, et al. (2024) Relying on the French territorial offer of thermal spa therapies to build a care pathway for long COVID-19 patients. PLoS ONE 19(4): e0302392. https://doi.org/10.1371/journal.pone.0302392

Editor: Angela Mendes Freitas, University of Coimbra: Universidade de Coimbra, PORTUGAL

Received: June 20, 2023; Accepted: March 26, 2024; Published: April 19, 2024

Copyright: © 2024 Chaze et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: This publication is related to the project named “CAUVIM-19 – Immuno-Metabolic (IM) Phenotyping and management of COVID-19: Specificity of actors and of the Auvergne territory”. The “CAUVIM-19” project is co-funded by the FEDER (European Fund for Regional Development) as part of the European Union’s response to the COVID-19 pandemic. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In November 2019, one of the largest pandemics of modern times began. Its global and rapid spread resulted a massive contamination of the world’s population. According to official data from the American Johns-Hopkins University , the COVID-19 pandemic affected 559.5 million people worldwide and killed at least 6.4 million people by July 12, 2022. In France, the figures given by World Health Organization (WHO) reported 39,86 million cases of COVID-19 since the beginning of the pandemic (as of April 12, 2023) with a death toll of 162,176.

Far from the pandemic being over, contaminations are on the rise again. If the forms observed in the last few months seem less dangerous, the intensive research of the last two years has allowed us to highlight and document the numerous deleterious effects of the virus and in particular to focus on its long forms. For example, recent data have shown that a series of persistent symptoms can continue long after acute infection with SARS-CoV-2 has taken place. This condition is now referred to as long COVID-19 by scientists [ 1 ]. The National Institute for Health and Care Excellence (NICE) defines long COVID-19 as symptoms that persist or develop after acute COVID-19 infection and cannot be explained by any other diagnosis. In France, according to a study conducted by the national public health agency ( Santé Publique France) last July 2022, the number of people who have been affected by a long COVID-19 is 2.06 million [ 2 ].

Knowledge of the many symptoms of long COVID-19 [ 3 , 4 ] and the mechanisms that may explain them has improved significantly over the past two years. It must be noted, however, that the pathway of long COVID-19 patients after the acute phase has not been the subject of specific research on the type of care services likely to constitute a long COVID-19 patient pathway beyond strictly clinical treatment [ 5 ]. Indeed, the work on long COVID-19 patients has mainly focused on clinical treatments, ignoring the potential for innovation in the supply of care and the structuring of a pathway that this situation could allow to emerge at the territorial level [ 6 ]. The territorial level refers to a geographical area composed of health actors and structures with specific mobilizable resources. Because they can constitute major barriers to the follow-up of specific treatments, geography and space are essential determinants of the quality of care for long COVID-19 patients [ 6 , 7 ].

Among specific treatments, thermal spa therapies represent a therapy offer located outside health care institutions (hospitals, clinics, medical centers) and whose integration in the care pathways of long COVID-19 patients seems to be little studied by researchers and considered by the health authorities. Important works have demonstrated, in different countries, that the medical management of COVID-19 patients favors the use of thermalism for its curative effects related to post-disease pulmonary problems [ 8 , 9 ] and for post-treatment rehabilitation [ 10 – 12 ]. Other studies remain more cautious about the benefits of spa treatments not combined with other treatments [ 13 – 15 ], particularly for the treatment of COVID-19 [ 16 ]. The medical benefits of spa treatments for long-standing COVID-19 also appear to be the subject of intense academic debate. However, irrespective of the outcome of this medical controversy, few studies have focused in parallel on an important practical and organizational issue: the potential access of patients to these spa treatments. Indeed, in contrast, we do not find any work analyzing the ways in which it is possible to integrate these thermal therapies into the care pathway of long COVID-19 patients. Indeed, beyond the health effects of thermal spa therapy on COVID-19 patients, it seems essential, in parallel, to put in perspective the possibilities of thermal spa therapy offered by the territories and their integration in an adapted care pathway. This is why our research focuses on patients’ access to this resource (thermal treatments) and the possibility of integrating them into a post-treatment pathway of the acute phase of the disease if the results of ongoing studies (such as CauvimTherm in France but not yet published) show that there are beneficial conclusive effects.

What place can thermal spa therapies take in the care pathway of long COVID-19 patients?

This is the question that has guided our reflection and has led us, in the case of France, to explore the place that thermal spa therapy could take in the care pathway of long COVID-19 patients. To carry out this reflection, we mobilized geographic mapping in health care. In health care pathway management studies, geographic mapping is still under-exploited [ 17 ], probably because of the limitations associated with it: idealized [ 18 ] and reductive [ 19 ] representations, the ideological imprint of its sponsors and/or designers [ 20 ], and the impossibility of representing on the scale of a geographical space in all its completeness [ 21 ]. So far, attempts to geographically conceptualize care pathways [ 22 – 24 ] have used the map as a descriptive and static tool at the service of public health policies and less at the service of patients and health professionals [ 13 , 25 ]. Indeed, geographical mapping is mainly used to visualize national and sub-national health data [ 26 ]. In public health, geographical mapping is sometimes used collaboratively [ 27 – 29 ] or thematically [ 30 ], but without really superimposing healthcare needs and resources integrating a care pathway. On the one hand, cartographic interaction and collaboration, representing the dialogue between a human and a map, provides auxiliary background knowledge and auxiliary map-reading tools to facilitate the transfer of geographical knowledge to the public [ 29 ]. On the other hand, these approaches to geographical analysis remain less dynamic and prescriptive in terms of care pathway management.

We propose here to use geographical cartography to carry out an interactive and cross reading of the possible care trajectories of long COVID-19 patients and of accessibility to the French territorial offer of thermal spa therapies. The objective of this reading is, from the geographical cartography, to determine what place French thermal spa therapies can take in the care pathway of long COVID-19 patients. Indeed, unlike proximity care (hospital care, general medicine, para-medical care), the thermal cures present national or even international accessibility. The models of spatial accessibility to the care based on the proximity between patients and care only partially answer the question of the integration of the thermal treatments in a care pathway. Also, in order to answer our research objective, we build a geographical cartographic analysis in two stages by mobilizing in particular the method of evaluation of the attractiveness of an area defined by David Huff [ 31 ] which allows measures of accessibility to care to be widened to a national or even international patient base.

To report on this study, our article is structured as follows. Firstly, we review the main studies on long COVID-19 and its consequences on the care pathways of affected patients. Secondly, we present our geographical mapping methodology. Thirdly, we present the main maps illustrating the application of geographical mapping analysis integrating Huff’s attractiveness model. Finally, we discuss these results and highlight their main contributions to the management of care pathways in general, and more specifically to the management of long COVID-19 patient pathways.

Knowledge about the persistent symptoms and rehabilitation needs of long COVID-19 patients has begun to emerge [ 32 – 34 ]. The most common persistent symptom was fatigue (53% to 64%), followed by dyspnea (42% to 50%) [ 34 – 36 ]. Other symptoms were psychological distress, joint pain, chest pain, cough, sleep disturbance, and functional disability. Patients also reported a decrease in quality of life [ 32 – 34 ]. The management of these symptoms requires mobilizing a wide range of resources to manage the sequelae of long COVID-19 patients [ 37 ]. The National Institute for Health and Care Excellence (NICE) guidance in the United Kingdom highlights the value of multidisciplinary rehabilitation in managing the post-COVID-19 patient pathway [ 37 ]. There is an interest for more relevance in setting up individualized and adapted rehabilitation programs to meet patients’ needs [ 38 ].

Therapeutic effects of spa treatments on chronic pathologies through the reduction of pain, the improvement of patients’ comfort and the reduction of medication have been demonstrated [ 39 ]. Twelve therapeutic orientations of spa medicine are recognized in France by the health insurance system and entitle patients to reimbursement for a treatment. These orientations and the resulting treatments help to alleviate the effects of a chronic pathology. They concern, among other pathologies, rheumatological affections, respiratory tracts, psychosomatic affections, which are also pathologies identified within the framework of the symptoms reported by long COVID-19 patients.

Research into the manifestations of infection and their effects points to symptoms that are in part, symptoms that spa treatments can act upon. Indeed, recent evidence shows that various treatments using thermal water are effective for several diseases of the respiratory tract [ 9 ]. Consequently, the spa environment could represent an appropriate out-of-hospital setting for respiratory rehabilitation in post-COVID-19 subjects. However, further studies are needed to test the efficacy of spa respiratory rehabilitation protocols for these patients [ 9 ] to support the initial results of the studies [ 39 ].

For example, respiratory treatments (based on inhalations or gargles, prescribed and supervised by specialized health personnel) stimulate the immune system and clean the respiratory system, preparing it to face a possible episode of COVID-19 [ 35 ]. Several studies [ 9 – 11 , 40 ] underline the interest of offering rehabilitation interventions based on the thermal spa therapy offer for COVID-19 patients suffering from musculoskeletal and neurological disabilities characteristic of long COVID-19 patients. Recently, the study conducted by Gvozdjáková et al. [ 41 ] would tend to demonstrate that a high-altitude environment accompanied by spa rehabilitation can be recommended to accelerate the recovery of patients with long COVID-19 syndrome.

A thermal spa therapy is an ideal space to recharge one’s batteries between primary care and Physical Medicine and Rehabilitation centers [ 42 ]. It allows the patient to leave their normal situation and habits, offers essential rest time during the convalescence phase, allows the re-appropriation of chronobiological rhythms including sleep which is the pillar of recovery, the resumption of an adapted physical activity and resocialization [ 43 ]. Other studies also show that balneotherapy has thermal effects on the body and may present certain dangers for patients [ 13 , 14 , 16 ]. Other studies show a potential effect of spa treatment in the medium term rather than the short term, but always in combination with other therapies [ 15 , 35 , 40 , 41 ]. Consequently, there is no total consensus on the benefit/risk ratio of spa treatments for long COVID-19 patients.

Thermal spa therapy constitutes a unit of time and place conducive to learning self-management of the disease [ 43 ]. It is characterized by a holistic and person-centered approach, led by a multi-professional team (doctor, physiotherapist, nurse, dietician, psychologist, hydro therapists) experienced in the practice of thermal spa therapies. Thermal spas can also sometimes offer the advantage of being inexpensive therapies for health insurance compared to medical drug management.

Antonelli & Donelli [ 12 ] propose a standard/core care model of COVID-19 patient management (Mechanical Pulmonary Ventilation for Rehabilitation, Mineral Water Inhalation Therapies, Physical Activity, Psychological Support) that refers to already existing rehabilitation plans with a long tradition, such as those prescribed for work-related respiratory diseases such as pneumoconiosis, whose long-term outcomes share some clinical features with post-infectious pulmonary fibrosis.

The need to start respiratory therapy as soon as possible, while hospital resources are over mobilized by acute management [ 44 ], and the facilities available to treat patients in the post-acute period being limited [ 17 ] local thermal spa facilities constitute an appropriate setting for respiratory rehabilitation interventions in post-COVID-19 subjects [ 45 ]. If thermal spa therapies can bring benefits on the physiological level (respiratory discomfort, musculoskeletal pains) they also present an interest at the psychological level (anxiety, sleep disorders) by acting on well-being and can be part of a global approach to the treatment of long COVID-19 symptoms. Thus, for example, a broad review of the literature on the therapeutic effects of thermal spa therapies allows Castelli et al [ 46 ] to hypothesize the mutual and reciprocal effects of these therapies on pain reduction and sleep quality.

It is in this perspective of global treatment that thermal spas have developed a long COVID-19 offer but the care pathways in a patient-centered territorial proximity offer remain to be thought out [ 17 ]. A geographical mapping of the thermal spa offer and an analysis of its attractiveness/accessibility to potential patients can help to develop care pathways for post-acute COVID-19 patients. Indeed, maps are regularly used in public health as secondary methodologies for representing primary location data cartographically [ 47 ]. However, they are rarely used to dynamically read aggregated geographical data in order to manage care pathways.

Materials and methods

Our research is non-interventional and therefore not subject to the rules of the Jardé Law (Law no. 2012–300 of March 5, 2012) on biomedical research carried out in France, since it does not use any personal data relating to the health of a specific cohort of patients. Regarding personal data protection, in the European Union and in France, the General Data Protection Regulation (GDPR) (Regulation 2016/679 of the European Parliament and of the Council of April 27, 2016) came into force on May 25, 2018. The French Data Protection Act (Law no. 78–17 of January 6, 1978 on data processing, data files and individual liberties) came into force on May 25, 2018.

These two texts now form the basis of the new regulations on personal data protection. The GDPR defines personal data as "any information relating to an identified or identifiable natural person", i.e. a natural person who can be identified, directly or indirectly. In practice, this can mean identifying data such as surname, first name, address or telephone number, information relating to the patient’s personal life (e.g. number of children), social security coverage (e.g. compulsory health insurance, supplementary health insurance, etc.) and, above all, information relating to the patient’s health (pathology, diagnosis, prescriptions, care, etc.), as well as to the professionals involved in his or her care.

In our study, we do not use any of these data, as they are only statistical data from open national databases (INSEE, Santé Publique France, Conseil National des Etablissements Thermaux) concerning, on the one hand, the proportion of patients affected by a long COVID-19 and their geographical locations and, on the other hand, the socio-economic characteristics of French thermal spas as well as their geographical locations (see S1 Appendix ).

Seven hundred and seventy natural mineral water springs are listed in France, i.e. 20% of the European thermal capital, which gives it first place in terms of European hydromineral heritage. Ninety thermal areas are in operation in France (see S2 Appendix )., for 110 thermal spas (cf. French thermal spas, National Council of Thermal Establishments, Edition 2022). Three major regions (Occitanie, Auvergne-Rhône-Alpes, New Aquitaine) account for 80% of French thermal spa therapy centers (Curist survey 2006, National Council of Thermal Establishments).

In order to put into perspective, the opportunities offered by the French thermal spa therapy offer for the management of long COVID-19 patients, we proceeded to a geographic mapping analysis of the available official data on COVID-19 contaminations, hospital admissions of COVID-19 patients in intensive care and available data on the national offer of thermal spa therapies by relying on an analysis of the offer of all thermal spas. This analysis was carried out in two successive and cumulative steps.

Data for the geographic mapping analysis

In order to carry out our geographical cartography, we used the geographical cartography software Chronomap ® .

Firstly, to build this geographic mapping, statistics on the French territorial organization and population of each region are taken from official data from the National Institute of Statistics and Economic Studies (INSEE) [ 48 ]. Statistics on COVID-19 positive cases and COVID-19 cases requiring admission to an intensive care unit are based on official French data published by Santé Publique France (national public health agency) [ 49 ] They cover all COVID-19 cases and admissions to intensive care units between March 19, 2020 (start of the census of cases in France) and May 30, 2022.

Regarding attendance at thermal spas, the data we have processed are those of the National Council of Thermal Establishments (CNETh) [ 50 ] which includes all French thermal spas whether they are public, private, for profit or not for profit. The choice of the year 2019 to measure the number of thermal spa patients is justified by its anteriority to the COVID-19 epidemic which strongly disrupted thermal spa therapies during the year 2020. We also used the data of specialization of thermal spas ( cf . location-cure.net, guide to French thermal spas from the National Council of Thermal Establishments) as well as the geocoding in two points (longitude/latitude) of the whole of the thermal spas counted.

Analysis method of attractiveness and accessibility to thermal care

Secondly, we applied an analysis of the attractiveness and spatial accessibility to thermal spas for the potential patient concerned by this type of care. We chose to apply our method on spas whose specialties are likely to enter the care protocol of long COVID-19, in order to get a global view point on the offer. However, we distinguished the medical specialities in order to show their location in France. Various measures of spatial accessibility are proposed, including regional availability [ 47 ], the gravity model [ 51 , 52 ] and the 2 Steps Floating Catchment Area (2SFCA) method [ 53 ].

The regional availability method performs a simple relationship between supply (physicians) and demand (population) within a predefined area (usually the “Department”—in France the “Department” is one of the three levels of government under the national level, between the administrative regions and the communes. 96 departments are in metropolitan France, and 5 are overseas departments). However, this method does not reveal spatial variation within the boundary and does not account for the interaction between supply and demand across the boundary [ 53 ]. The gravity model is theoretically more robust, but it requires more computation and the result is not intuitive to interpret [ 54 ]. In this model, the interaction data between the place of care and the patient are often specific to a region [ 55 ], which is relatively unsuitable for a thermal spa whose area of attraction goes far beyond the borders of a region. Among the gravity methods, the 2SFCA method retains most of the advantages of a gravity model [ 56 ] and generates a physician/population ratio that is determined primarily at the local catchment or regional level [ 51 ]. Despite its limitations, the 2SFCA method is the most widely used to measure spatial accessibility to health care [ 49 ]. However, it is a dichotomous measure where all locations outside the catchment area are assumed to have no access [ 56 ]. This mode of calculation is not well adapted to the thermal spas which, unlike proximity care (local doctors, pharmacies, etc.) have a national or even international attractiveness and not only local.

The method of evaluation of the attractiveness of an area defined by Huff (also known as Modified Huff Model three-step floating catchment area—MH3SFCA) [ 31 , 56 , 57 ] is also a gravity model but it allows us to consider the distance between the supply and the demand whatever the zone of influence of the care place. It is a spatial interaction model based on the gravitational principle, i.e. that the attractiveness of an area is proportional to the offer we are talking about (the capacity of the thermal spas in this study) and inversely proportional to the distance which separates it from the patient base. The application of this model allows to define the degree of attractiveness of a place by another without being limited to a local health basin. This is why it is well suited to the particularities of our study. In the MH3SFCA method, a population’s demand only decreases with increasing distance from a service site if other service sites are available. The MH3SFCA method is therefore highly sophisticated, combining many of the advantages of previous traditional methods (regional availability and gravity model) with the benefits of more advanced methods (2SFCA) [ 58 ].

Pij: Probability that patients from department j will take their treatment in thermal spa i.

Wi: Capacity of the thermal spa i.

Dij: Distance between the centroid of department i and thermal spa j.

a: Exponent applied to reduce the probability of distant sites.

The capacity of the thermal spas was determined by the number of patients in 2019, in order to avoid the year 2020, marked by the epidemic of COVID-19 which could have disrupted the thermal frequentation. The choice of spa attendance in a typical year rather than the capacity of the thermal spa allows us to overcome the bias of the variable duration of the cures, which also influences the actual attendance of the spas.

The distance between the patients and the thermal spas was calculated from the address of each spa and the centroid of the departments. The choice of the department centroid is explained by the departmental scale at which the data on patients treated for COVID-19 were obtained. The distance between the two points was calculated as the crow flies, and not by road, in order to take into account, the insular nature of Corsica, where access to the island is by boat, but also by plane. The choice of the bird’s eye view, even if it is less precise than road accessibility, allows us to measure accessibility in the same way for each area. The application of this formula has thus allowed, in a first instance, to calculate the attractiveness of each department to each thermal spa.

In a second step, in order to determine the attractiveness of each area for all thermal spas, we have added the previously calculated indices, obtaining an accessibility indicator of each area for all thermal spas. We have carried out the same operation for the thermal spas in order to obtain an attractiveness indicator considering not only their capacity of reception, but also their geographical situation. The formula of the second step is the following one.

Aj: Accessibility of the department j to all the thermal spas.

Aj: Attractiveness capacity of the thermal spa i for the whole departments.

The result of this analysis of accessibility has finally been represented on a map with double reading: by the attractiveness of spas areas, and by the accessibility to the thermal spas.

To produce the maps presented in this article, we used a background map of the French Departments from Global Administrative Areas Data and Maps (GADM: https://gadm.org/ ). These backgrounds are royalty-free and compatible with the CC-BY-4.0 license used by PLOS, as indicated on the GADM website ( https://gadm.org/license.html ). We then imported this base map into the GIS software QGIS (CC-BY-3.0 license: https://www.qgis.org/fr/site/ ). We then attached our statistical data to the base map to map the INSEE and Santé Publique France data. Spa centers were geolocated using their addresses, enabling us to create the second layer of our GIS. We then joined the spa data to the points representing them on the map and were thus able to map the spa data. Finally, we calculated each departments’ potential access to spa centers, as described in the article’s methodology.

The thermal spa therapy offers likely to intervene in the care pathway of long COVID-19 patients

The French thermal spa therapy offer presents, for natural and historical reasons, a very unequal distribution. Four main areas appear: the Pyrenees and their foothills, the Northern Alps, the Massif Central and the Vosges. In addition to these areas, there are scattered centers (Balaruc in the Hérault province, Amnéville in Moselle province, Niederbronn in Alsace province, Saint-Amand-les-Eaux in the North, Bagnoles-de-l’Orne…). The Pyrenees and their foothills are the main focus, but the Auvergne-Rhône-Alpes region, with the Auvergne and North Alpine thermal spas, have the second largest spa therapy offer in France ( Fig 1 ).

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https://doi.org/10.1371/journal.pone.0302392.g001

The activity of thermal spas is also very uneven. Through the analysis of the number of spa patients received by each of them in 2019 (the year preceding the COVID-19 epidemic), some large centers emerge: Balaruc, Aix-les-Bains and Dax clearly stand out. Dax and its suburbs (Saubusse-les-Bains, Préchacq-les-Bains, Tercy-les-Bains) also has the originality of having 22 thermal spas, 16 of which are located in Dax alone (a record in France). That being said, the majority of thermal spas are small entities, more than half of which do not welcome more than 5,000 patients per year.

In terms of specialization, most French spas are specialized in rheumatology and a good half of them combine this specialty with ENT (Ear-Nose-Throat) and respiratory problems (see S1 Appendix ). In contrast, very few French spas are specialized in psychosomatic pathologies ( S1 Appendix ), but, as previous studies have revealed [ 38 , 43 ], the treatments of these pathologies undoubtedly benefit from rheumatological or respiratory treatments.

On the one hand, thermal spas with the highest capacity (Dax in the South-West of France, Balaruc-les-Bains in the South or Aix-les-Bains in the Alps) are rather specialized in rheumatology ( Fig 1 ). On the other hand, the thermal spas specialized in ENT and respiratory affections, very concerned by the treatment of the symptoms of the long COVID-19, are not big thermal spas in terms of capacity of reception ( Fig 1 ). Moreover, most of the thermal spas specialized in these pathologies are located in the Pyrenees and their foothills and in the region Auvergne-Rhône-Alpes (Alps and Massif Central—La Bourboule, Le Mont-Dore, Allevard, Saint-Martin-d’Uriage, Saint-Gervais-les-Bains…). Only a few spas are more isolated (Amnéville, Saint-Amand-les-Eaux, Jonzac).

Thermal spa therapy and COVID-19 contamination sites

If we compare the thermal spa therapy offer with the geographical distribution of COVID-19 cases, some major points appear. First of all, several important foci of COVID-19 contamination appear ( Fig 1 ), in particular Ile-de-France, Auvergne-Rhône-Alpes, Provence-Alpes-Côte d’Azur, Toulouse Region, Nord-Pas-de-Calais. The location of the main sources of contamination corresponds to the main urban concentrations (Paris Region, Lyon Region, Marseille, Toulouse, Nord-Pas-de-Calais), as well as to the initial sources of contamination (Val-d’Oise in the Paris Region, Rhône Region, Northern Alps, Alsace).

However, this geography of infections must be complemented by the geography of severe cases that required admission to intensive care, which includes the majority of long COVID-19 cases ( Fig 2 ).

https://doi.org/10.1371/journal.pone.0302392.g002

Admissions to intensive care are mainly in regions with large urban areas (Paris, Lyon, Marseille), both because of the concentration of population and the presence of major hospital infrastructures. In addition, there is a clear contrast between a north-east-south arc where contamination and admissions to intensive care are significant, and a more spared west-central area.

The interactive reading of the geography of the French thermal spa therapy offer and of the COVID-19 contaminations/severe cases (Figs 1 and 2 ) allows us to enrich very appreciably our way of analyzing and managing the care pathway of long COVID-19 patients. Indeed, on the one hand, this cartographic analysis indicates that the thermal spas located in the heart of, or near the main areas of contamination, and in particular the resuscitation admission centers, appear to be adapted structures in terms of accessibility, for patients suffering from long COVID-19 and are therefore favored. Indeed, the Alpine thermal spas (Aix-les-Bains, Allevard, Saint-Gervais-les-Bains…), located in the heart of the Rhone-Alpine contamination area, are particularly well placed to receive higher numbers of patients. This is also the case, to a lesser extent, of the Pyrenean thermal spas in relation to the Toulouse region.

On the other hand, some regions, such as Ile-de-France and Nord-Pas-de-Calais, with few thermal spas but strongly affected by COVID-19, constitute important areas of potential patients for thermal spas located relatively far away. The economic support of the long COVID-19 patients of these distant regions, the conception of thermal stays adapted to their needs and more important collaborations between the public administrations of these regions and the thermal spas already appear as levers of reduction of these inequalities of access very adapted to the French socio-economic specificities.

However, this cartographic analysis also identifies the need, as far as thermal cures in particular are concerned, to measure the level of accessibility of potential patients to these cures which constitute care whose attractiveness largely exceeds the borders of a medical area of proximity or of a region.

Attractiveness and accessibility to the thermal spas

The departments with the highest accessibility to the thermal spas are those which are the closest to the thermal spas. However, thanks to the method used, the proximity can be relativized. Some departments like Gironde (Nouvelle Aquitaine region) or Lozère (Occitanie region) or Meurthe-et-Moselle (Grand Est region), which don’t have a thermal spa on their territory, or only a small center like Lozère, benefit from a very good accessibility to the thermal offer because of the presence of a highway which connects them correctly to the stations ( Fig 3 ).

https://doi.org/10.1371/journal.pone.0302392.g003

In fact, we find areas of high accessibility in the South-West of France, in the North-East, as well as in the heart of the Massif Central and in the Northern Alps. On the other hand, the heart of the Paris Basin, Brittany, Provence-Alpes-Côte d’Azur and Corsica present the weakest potential of access to thermal spas. The main explanation lies in the unequal geographical distribution of the thermal spas. But to this, we have to add the unequal capacity of attraction of thermal spas which reinforces the potential of access of some departments like Hérault (Occitanie region) thanks to the presence of Balaruc (the most important French thermal spa), Savoie (Auvergne-Rhône-Alpes) with Aix-les-Bains, and of course the departments of the South-West around Dax and the more modest thermal spas of the Pyrenees.

In addition, the geographical location of a thermal spa within a department also influences its accessibility. This is the case for Saint-Amand-les-Eaux in the Hauts-de-France, whose relatively central geographical position allows it to be easily accessible from the entire department ( Fig 3 ).

If we relate the accessibility of the territories to the thermal offer with the geographical distribution of the patients having had long COVID-19 (Figs 1 and 2 ), a real disparity between the foci of COVID-19 and long COVID appears as for their accessibility to the thermal spas. Indeed, Ile-de-France, and to a lesser extent the South-East of France, which constitute important foci of COVID-19, are relatively far from the thermal spas, whereas other important foci such as the Lyon region and the Northern Alps (Auvergne-Rhône-Alpes) are well served by the thermal spas.

These inequalities of accessibility risk adding an additional cost to the expenses of the patients for their travel to the thermal spas. In fact, some thermal spas located near theirs home locations could be preferred by the patients living nearby, and this, in spite of a sometimes limited capacity of reception. This could be the case of the thermal spas located at the limits of the Paris Basin, namely the most accessible ones of the Paris Region where we find an important concentration of patients treated for a COVID-19.

The case of Ile-de-France (Paris) contrasts with that of the other large French conurbations (Lyon, Marseille, Toulouse, Bordeaux), which constitute other major foci of COVID-19, but whose accessibility to the thermal spas appears to be better even though they do not have thermal spas immediately nearby ( Fig 3 ).

Huff’s model [ 31 , 56 ], which combines the location of potential patients with the attractiveness of spa areas, significantly strengthens the analysis of accessibility to thermal spas, thus promoting the integration of thermal treatments into the care pathway of long COVID-19 patients.

Analysis of thermal spas accessibility reinforced by the Huff Model

Several ideas emerge from the cartographic analysis of the accessibility to the thermal spas realized from the method of Huff [ 31 , 56 ]. Our results show that, when it comes to integrating spa therapies into patient care pathway, the application of Huff’s method offers important theoretical and managerial perspectives. Indeed, as mentioned above, the regional availability method [ 47 ], the gravity model [ 51 , 52 ], and the 2 Steps Floating Catchment Area (2SFCA) method [ 53 ] mainly allow the location of a local patient to be considered. For spa centers, however, the patient base is national or even international. Our results therefore show that Huff’s method offers the possibility of taking this national patient base into account and therefore facilitates the integration of spa therapies into the care pathway of patients with long COVID-19.

Thanks to Huff’s method, it is possible to distinguish thermal spas according to their respective levels of attractiveness and accessibility. In the first place, the spa areas which present the highest potential of attraction are the ones which have the most important capacity of reception, and this, independently of the distance the geographical situation of the spas, because of a scale effect. As the study concerns all the thermal spas of a same department, the longest distances are always compensated by the shortest ones for each thermal spa.

Indeed, whereas the cartographic analysis would tend to show a relative distance of the COVID-19 foci from the thermal spas (Figs 1 and 2 ), the Huff’s model relativizes appreciably this distance by demonstrating that the accessibility to the thermal spas is good for a good part of the south and the east of France. Only the center of Northern France (Regions, Ile-de-France, Hauts-de-France, and Centre) presents a distance that we can qualify as important. This analysis therefore sheds light on the question of patient accessibility to care that is attractive nationally and even internationally. It allows a much finer reading of the accessibility to care (here thermal) which considers at the same time, the care offer of a territory and the location of the patients presenting a single and same pathology. Therefore, this cartographic and accessibility analysis makes it possible to adapt the care pathway of the COVID-19 patients according to the potential accessibility of the patients to the thermal care and not only according to their geographical proximity to the thermal spas.

Adapting care pathways to the location of patients and the thermal spa therapy offer

In France, thermal spa therapies prescribed by a physician are reimbursed at 65% by the health insurance (the rest is covered by the patient’s private health insurance) and at 100% for patients suffering from an occupational disease (for a maximum duration of 18 days) (cf. French thermal spas, National Council of Thermal Establishments, Edition 2022). The thermal spa is chosen by physicians with the patient’s agreement (or on the patient’s proposal in 50% of the case) according to the patient’s pathology and is not necessarily the one closest to the insured person’s home for the prescribed orientation. Accommodation and travel costs are the responsibility of the patient. They can also be covered only if they have insufficient resources. In fact, transportation and accommodation are only covered for patients whose annual income does not exceed 14,664 euros per year for a single insured person. For a married person without children, this ceiling rises to 21,996 euros [ 59 ]. However, if the insured person is eligible for the supplementary transport benefit, the reimbursement will be based on the distance separating the patient’s home from the nearest thermal spa suitable for the therapy. Also, in practice, the conditions of coverage of the spa therapy by the health insurance and especially the necessity for some patients to finance their accommodation and their transport lead doctors to choose the thermal spa closest to the patient’s home. From then on, as the geographical cartography suggests, the proximity between the territorial offer of thermal spa therapies and long COVID-19 patients constitutes an essential determinant of the integration of thermal spa therapies in the care pathway of these patients. The South of France (Auvergne-Rhône-Alpes Region, Provence-Alpes-Côte d’Azur Region, Occitanie Region, New Aquitaine Region) represents both 80% of the French spa therapy offer and the French regions with the highest COVID-19 contaminations between 2020 and 2022 ( Fig 1 ). Among the thirteen French regions, these four regions represent geographical areas in which thermal spa therapy can be integrated with ease into the care pathway of COVID-19 patients. This result is particularly striking in the two regions of the South-East of France (Auvergne-Rhône-Alpes Region, Provence-Alpes-Côte d’Azur Region) where the importance of severe cases of COVID-19 which geographical location is very similar with the thermal spa therapy offer ( Fig 2 ). In these last two regions, the thermal spa therapy can be integrated in the care pathway of the long COVID-19 patients without any important geographical obstacles.

Examining the collaboration between healthcare stakeholders for patient’s health

In these regions, the proximity between the territorial offer of thermal spa therapies and the location of the patients authorizes a closer collaboration between hospital physicians, general practitioners and thermal spas. The development of this collaboration is an even more interesting prospect as thermal spas can, in parallel, increase the number of rehabilitation places for long COVID-19 patients and, consequently, reduce the workload of hospital rehabilitation units [ 60 ].

On the other hand, in the other French regions where the thermal spa therapy offer is reduced but in which COVID-19 contaminations or the number of serious cases are high (Paris Region and North-East of France) (Figs 1 and 2 ), the integration of thermal spa therapy in the patients’ care pathway seems more constrained at the geographical level. This integration is due to very unequal care pathways on the socio-economic level between high income patients able to finance all or part of their treatment, accommodation and transport and those only able to finance a part of this treatment. Also, it is undoubtedly necessary to think of a mode of financing and the reimbursement of thermal spa therapy by the national health insurance and the private health insurance companies of long COVID-19 patients that can consider, in a more refined way, the localization of patients and the thermal spa therapy offer. In this way, the cartographic study carried out here justifies the necessity to integrate geographical analysis in the modes of financing of the patients’ care pathway in order to reduce the socio-economic inequalities of access to thermal spa therapy and to make this care a therapy accessible to the greatest number possible of patients.

Contributions

The use of geographic mapping, allows the analysis of the integration of spa therapies in the care pathway of patients presenting symptoms of long COVID-19. The opening of and the integration of patient care pathways outside hospital establishments represents essential health stakes because they allow the reinforcement of care continuity [ 61 ] in a much more extended geographical perimeter. Taking the example of spa therapy, we present here the perspectives that geographic mapping offers to integrate spa therapies in the care pathway of long COVID-19 patients.

Firstly, on a theoretical level, this geographical mapping study shows that the potential attractiveness of spas areas and accessibility to thermal spas can determine the ways in which spa therapies can be integrated into the construction of territorialized, person-centered care pathways. The identification of these two geographical criteria represents a first conceptualization of the integration of a type of treatment such as spa therapy into a care pathway. This contribution deserves to be replicated for other types of treatment and other types of care pathways, but it already proposes a robust methodology to help researchers and practitioners measure the ways in which they can integrate territorial health services into a care pathway. Thus, in the case of spa therapies, "territorialized care pathways" [ 13 ] could emerge and constitute an opportunity to integrate the spa therapy offer in the downstream pathway of patients and, by the same token, contribute to a decompartmentalization of these two types of structures (hospitals and thermal spas). Indeed, the mobilization of Huff’s method for evaluating the attractiveness of an area [ 31 ] makes it possible to define, on a practical level, in which French regions it is possible to implement these “territorialized care pathways” and in which regions the latter require the implementation of support mechanisms. Thus, using this spatial attractiveness method, we make this type of geographical analysis more dynamic by showing the extent to which a thermal spa is accessible to long COVID-19 patients.

Secondly, geographical mapping based on attractiveness of spa areas and accessibility of thermal spas is a managerial tool for reflection and dialogue between hospitals, doctors, spas, health and social protection authorities, with the aim of integrating a health service (in this case, spa therapy) into the patient’s care pathway. In the case of the care of long COVID-19 patients, this mapping highlights the main criteria for reflection and discussion to integrate thermal care in territorialized care pathways for patients:

• continuity of the patient’s care pathway and the perspectives offered by the patient’s medical territory;
• possible clinical collaborations between hospitals and thermal spas located in the same geographical area;
• adapting of the methods of financing patients’ care pathways according to their geographical location, mainly for patients who are farthest from thermal spas.

At a time when it has been demonstrated that thermal spas have a strong social and economic value for the region in which they are located [ 62 ], it is also important to show how these spas and their regions can be both accessible and attractive. The main criteria for integrating spa therapies into the care pathways of long COVID-19 patients, derived from our geographical mapping, are useful recommendations for improving this accessibility and attractiveness.

Limitations

This mapping analysis of the management of long COVID-19 patients thus shows that the geographic approach makes it possible to inscribe patient care pathways in their territories for care that is distant from the patient’s location instead of limiting them to local clinical care. However, this is only a first analysis and, in future, it will be necessary to reinforce these results by mobilizing larger data concerning long COVID-19 patients treated in French thermal spas in order to verify the relevance of the managerial and health perspectives presented in this article.

Conclusions and perspectives

From the example of the French thermal spa therapy offer, this article proposes to inscribe the care pathways of long COVID-19 patients in their territories instead of limiting them to clinical care in hospitals. Our research objective, which is to determine what place French thermal spa therapies can take in the care pathway of long COVID-19 patients, falls within the field of management and organization of care pathways for long COVID-19 patients, and in no way within the medical field. Through this study, geographic mapping analysis has proven to be a powerful analytical tool to reconcile the design of these pathways with the French thermal spa therapy offer. Firstly, the mapping analysis associated with the one of the attractiveness and accessibility to the thermal spas, which the Huff attractiveness method allows, makes it possible to describe the care pathways of the patients suffering from long COVID-19 in a dynamic way and not centered on a limited geographical area.

Secondly, these analyses then allow the identification of success and integration criteria of these care pathways in their territories adapted to the specificities of the produced care as well as to the geographical location of the care centers (here thermal) and of the potential patient base. This result then begins to demonstrate the original prospects offered by geographical attractiveness models (for example, Huff, [ 27 , 56 ] to help managers manage and steer care pathways beyond the boundaries of their local areas. Of course, this type of spa treatment can only be implemented on a larger scale if the clinical benefits are further demonstrated by randomized trials.

Supporting information

S1 appendix. links to the public data used to produce figs 1 to 3 ..

https://doi.org/10.1371/journal.pone.0302392.s001

S2 Appendix. Characteristics of the waters of spas specialized in ENT and respiratory, rheumatological and psychosomatic pathologies.

https://doi.org/10.1371/journal.pone.0302392.s002

Acknowledgments

Milhan Chaze, Laurent Mériade, Corinne Rochette, Mélina Bailly, Rea Bingula, Christelle Blavignac, Martine Duclos, Bertrand Evrard, Anne Cécile Fournier, Lena Pelissier and David Thivel form the complete membership of the “Immuno-Metabolic (IM) Phenotyping and management of COVID-19: Specificity of actors and of the Auvergne territory” (CAUVIM-19 research program). We would like to thank the InnovaTherm Cluster for its support.

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• Volume 14, Issue 4
• The impact of delayed diagnosis and treatment due to COVID-19 on Australian thyroid cancer patients: a qualitative interview study
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• http://orcid.org/0000-0002-6372-4896 Bianka D'souza 1 ,
• Anthony Glover 2 , 3 , 4 ,
• Claire Bavor 5 ,
• Benjamin Brown 5 ,
• http://orcid.org/0000-0002-8080-6359 Rachael H Dodd 6 , 7 ,
• http://orcid.org/0000-0001-6679-274X James C Lee 8 , 9 ,
• http://orcid.org/0000-0001-8202-8602 Jeremy Millar 5 , 10 ,
• Julie A Miller 11 , 12 , 13 ,
• John R Zalcberg 5 ,
• Jonathan Serpell 8 , 14 ,
• Liane J Ioannou 5 ,
• http://orcid.org/0000-0002-8100-4278 Brooke Nickel 6
• 1 School of Public Health and Preventive Medicine , Monash University , Clayton , Victoria , Australia
• 2 The University of Sydney Faculty of Medicine and Health , Sydney , New South Wales , Australia
• 3 University of New South Wales Faculty of Medicine , Sydney , New South Wales , Australia
• 4 Australian and New Zealand Endocrine Surgeons , St Leonards , New South Wales , Australia
• 5 Monash University School of Public Health and Preventive Medicine , Melbourne , Victoria , Australia
• 6 The University of Sydney School of Public Health , Sydney , New South Wales , Australia
• 7 The Daffodil Centre , Kings Cross , New South Wales , Australia
• 8 Department of Surgery , Monash University , Clayton , Victoria , Australia
• 9 Monash University Endocrine Unit , The Alfred Hospital & Monash Health , Melbourne , Victoria , Australia
• 10 Radiation Oncology , Alfred Hospital , Melbourne , Victoria , Australia
• 11 Endocrine Surgery Unit , Royal Melbourne Hospital , Melbourne , Victoria , Australia
• 12 Department of Surgery , The University of Melbourne , Melbourne , Victoria , Australia
• 13 Epworth Hospital Network , Melbourne , Victoria , Australia
• 14 Surgery , The Alfred Hospital , Melbourne , Victoria , Australia
• Correspondence to Dr Brooke Nickel; brooke.nickel{at}sydney.edu.au

Objectives The study aims to investigate the perceptions of patients with thyroid cancer on the potential impact of diagnosis and treatment delays during the COVID-19 pandemic.

Design This study involved qualitative semi-structured telephone interviews. The interviews were transcribed verbatim, analysed using the thematic framework analysis method and reported using the Consolidated Criteria for Reporting Qualitative Research.

Setting Participants in the study were treated and/or managed at hospital sites across New South Wales and Victoria, Australia.

Participants 17 patients with thyroid cancer were interviewed and included in the analysis (14 females and 3 males).

Results The delays experienced by patients ranged from <3 months to >12 months. The patients reported about delays to diagnostic tests, delays to surgery and radioactive iodine treatment, perceived disease progression and, for some, the financial burden of choosing to go through private treatment to minimise the delay. Most patients also reported not wanting to experience delays any longer than they did, due to unease and anxiety.

Conclusions This study highlights an increased psychological burden in patients with thyroid cancer who experienced delayed diagnosis and/or treatment during COVID-19. The impacts experienced by patients during this time may be similar in the case of other unexpected delays and highlight the need for regular clinical review during delays to diagnosis or treatment.

• Otolaryngology
• Thyroid disease

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjopen-2022-069236

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STRENGTHS AND LIMITATIONS OF THIS STUDY

First qualitative study to explore the impact of delayed diagnosis and treatment on patients with thyroid cancer during the COVID-19 pandemic.

The semi-structured nature of the interviews allowed patients to discuss niche experiences.

The interview schedule was developed by a multidisciplinary team.

Retrospective nature of the interviews may lead to a recall bias.

The cohort is limited by only including a state-based subset of the Australian population.

Introduction

The ongoing coronavirus (COVID-19) pandemic has led to a global public health response. 1 In Australia, an increased burden on the healthcare system and resource needs resulted in non-urgent elective surgery being postponed from March 2020. 2 In response to COVID-19 case numbers, there was varying easing and tightening of restrictions across the states and territories throughout the pandemic. These restrictions had an important impact on patients with thyroid cancer—the standard treatment for thyroid cancer is the surgical removal of part (hemithyroidectomy) or all (total thyroidectomy) of the thyroid, depending on the type of thyroid cancer and/or size of the tumour(s). Some surgeries for small thyroid cancers or diagnosis of thyroid cancer, such as hemithyroidectomy for atypical nodules, were delayed due to restrictions to operating capacity. 3 While these restrictions did not affect the treatment of clinically urgent thyroid cancer cases, they potentially led to delays in diagnosis due to difficulty with healthcare access and restricted access to imaging procedures. 4

Though most types of thyroid cancer have a high survival rate, the quality of life for these patients is of interest. One systematic review suggested that survivors of thyroid cancer tend to have similar or slightly poorer health-related quality of life than the normative population. 5 Another study found that patients with differentiated thyroid cancer experience anxiousness related to the risk of recurrence and the risk of developing other types of cancer. 6

While this is known about patients with thyroid cancer, our understanding of the impact of delays in treatment on patients with thyroid cancer is limited. Despite this, research has emerged on delays in other cancers, such as breast and prostate cancers during the COVID-19 pandemic. 7 8 A recent study suggests that there is no significant association between surgical delays and adverse oncological outcomes or disease progression in medium-risk to high-risk patients with prostate cancer. 7 However, another study looking at the psychosocial impact of delayed treatment on patients with breast or prostate cancer reported high levels of distress due to the combination of cancer-related and COVID-related worries. 8 It is important to understand whether there are similar clinical and psychological outcomes for patients with thyroid cancer who experience unexpected delays to diagnosis and/or treatment. The findings of this study may also improve our understanding of how delayed thyroid cancer treatments can be clinically managed in the future.

Study design

This study followed a thematic analysis research design, allowing for a descriptive and nuanced interpretation of the themes that emerged. 9 Semi-structured qualitative telephone interviews were undertaken to understand the impact of delayed diagnosis and/or treatment of patients with thyroid cancer during COVID-19.

Sample and recruitment

Patients aged 18 years or older who were diagnosed with primary thyroid cancer and had delayed diagnosis and/or treatment due to the COVID-19 pandemic were eligible. Patients were eligible for the study if they had a diagnosis of thyroid cancer and any delay to their treatment, which could include a delay in diagnosis, delay in surgery or further treatment such as radioactive iodine treatment. Patients were excluded from the study if they were non-English speaking, or could not provide informed consent. Patients were recruited through key Australian and New Zealand Endocrine Surgeons members and clinicians, and through the Australia and New Zealand Thyroid Cancer Registry. Eligible patients were purposively identified and notified of the study by the clinicians and given an invitation letter, the Participant Information Statement and Expression of Interest form to return to the clinician or directly to the research team via email. Registry participants who were diagnosed with thyroid cancer from April to June 2020 from across Victoria and New South Wales were sent a mail-out invitation. Participants returned their expressions of interest and were screened via phone call to ascertain suitability for the study. Eligible patients were interviewed after providing written or oral consent. The participants did not receive a stipend for participation in this study. Recruitment continued until no new themes emerged.

Data collection

A semi-structured interview schedule (see online supplemental appendix A ) was developed using an iterative process by a multidisciplinary team, including thyroid clinicians, public health researchers and health psychologists. The interview schedule included—pathway to diagnosis, diagnosis specifics, delay in treatment, initial views and attitudes towards delayed treatment, actual experiences of delayed treatment and retrospective reflection on the delayed treatment experience. The interviews were conducted via telephone by two researchers with training and experience in qualitative methods (BD and BN), audio-recorded and transcribed verbatim by an external transcription company. BD is a research student and BN is a public health researcher working in the area of cancer communication and decision-making. The interviews were conducted between June 2020 and October 2021 and ranged from 15 min to 43 min in duration, with a median interview duration of 23 min.

Supplemental material

Patient and public involvement.

The study was piloted and conducted in patients with thyroid cancer. The findings of this study have been summarised and disseminated to the participants.

Data analysis

The study methodology used thematic analysis, and the transcribed interviews were analysed using thematic framework analysis. 10 The transcripts were analysed independently by the three researchers (BD, CB and BN) to identify codes that could be linked together by related concepts. The agreed-upon initial themes and concepts were then grouped into broader themes and subthemes. Following this, RHD read a subset of the transcripts to inform the final version of the coding framework. Two researchers (BD and BN) were involved in the coding process, where BD coded all the transcripts and BN double coded 20% of the transcripts in Microsoft Excel. On completion of the coding and double-coding, a table of quotes listed under themes and subthemes was circulated to the research team (AG, BN, LI) to finalise for inclusion. After this, the final results were reported using the Consolidated Criteria for Reporting Qualitative Research Publication Guidelines. 11

A total of 20 patients were interviewed (16 females and 4 males). Three patients were excluded from the analysis after being interviewed. These patients were unsure if they had experienced a delay in the screening process, and on completing the interviews it was determined that the patients had not experienced any delays. The 17 participants included in this study (14 females and 3 males) had a median age of 48 (range 35–77) years old ( table 1 ). Most patients interviewed were treated in a private healthcare setting ( table 2 ). The key emerging themes with supporting quotes from the interviews are detailed below, and a comprehensive overview of additional supporting quotes for each theme can be found in online supplemental appendix B .

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Demographics of study participants

Treatment characteristics of study participants

Delays in diagnosis pathway

Some patients initially presented to their general practitioner for non-thyroid problems, while others presented with physical symptoms (e.g., sore throat, enlarged lymph node). Most patients were then diagnosed by a thyroid clinician ( table 2 ). Many patients noted feeling worried about their diagnosis; however, patients who felt that they better understood their diagnosis expressed less concern.

Patients noted a variety of reasons for delays to diagnosis, including delays in fine needle aspiration cytology, as well as delays between diagnostic testing and seeing a thyroid clinician. One patient noted that their diagnosis was delayed due to difficulties getting a flight back to Australia during the pandemic. Another patient noted they put off their ultrasound due to COVID-19 and conflicting personal demands during the pandemic.

I didn’t go because it wasn’t anything that seemed to have any urgency about it and, because in that early stage in 2020, it was only to go and see the doctor or whatever for things that were serious or urgent. (3–6 months delay)

Initial management

Most patients were recommended to undergo surgery by their thyroid clinician, although one patient noted that active surveillance was discussed. Most patients trusted the management advice provided by their clinician. A sense of urgency was conveyed to some patients regarding their treatment.

What I was anxious about was that COVID was going to slow and delay things. I was reading articles about delaying elective surgery and because I’m not medical, I didn’t know the difference. (<3 months delay)

One patient noted that they were treated in a private setting as their treating clinician was concerned about the delays COVID-19 could have in the public sector.

…any other year, [the surgeon] would have said just wait and go through the public system but [the surgeon] does [their] surgery through the public system and it wouldn’t have been that long, but because [they] could see the pandemic coming, [they] said that [they] recommended I went private. (3–6 months delay)

Delay in management

Delays were primarily communicated to patients by a thyroid clinician, with most delays being communicated via telehealth, including telephone or video conferencing. While some patients accepted the news of the delay, others expressed worry towards disease progression and going to the hospital during the pandemic.

Initial views and experiences of delay

The patients’ feelings towards the delay initially included disappointment, anxiousness, anger and fear, though most patients understood that the delay was out of their control. Many of the patients interviewed experienced heightened emotional responses during the pandemic.

…I don’t how much my feeling is different to just anybody that’s been through the pandemic. But I really have felt a lot more fragile than previous years. (3–6 months delay)

One patient noted that they chose to delay their radioactive iodine treatment due to fears of being in a hospital setting at the height of the pandemic. Numerous patients expressed concerns about going to hospitals or health settings during the pandemic. Notably, most patients were treated through the private healthcare system ( table 2 ), with some choosing private treatment over public in light of increased waiting times anticipated by the clinicians in the public sector.

During COVID like you don’t feel safe to go anywhere. (>6 months delay)

Others avoided further follow-up due to COVID-19 restrictions.

I was very aware at different times of worrying about it, and worrying about whether or not I really needed to do something or go and have this investigatory scan in the middle of a nation-wide lockdown, when everyone’s been told ‘unless it’s urgent, stay away’… (3–6 months delay)

Some patients were advised that treatment would recommence in 1 to 3 months, but this was mostly an estimate of what was ultimately unknown to healthcare providers. Most patients were supported by immediate family during this time, although COVID-19 restrictions did impact hospital visitation and the ability to access some healthcare facilities.

…you didn’t necessarily have the support of your family there. You know, my husband wasn’t able to attend the appointment with me, initially. (<3 months delay)

Views and experiences during delay

The patients reported numerous perceived impacts of COVID-19 on the care they received. Most patients noted slowed management and reduced communication with treating clinicians, as well as barriers to communication with the healthcare professionals. Some patients also reported the inability to access a nearby clinician in person and a lack of understanding of the information conveyed by the thyroid clinicians.

[The surgeon] just told me, ‘You have thyroid cancer’ but [the surgeon] didn’t explain. (<3 months delay)

One patient noted that they were regularly followed up and kept informed by their clinician. The majority of patients reported anxiousness towards their delayed treatment, with one patient noting that they felt they received little reassurance from their clinician during this time. Almost half of the patients interviewed expressed concerns regarding disease progression or tumour growth during their delay.

I always wonder[ed] whether the cancer grew in that time, the two-month delay. (<3 months delay) You do wonder whether the delay has caused that, you know, that things have grown while I’ve been waiting. (<3 months delay)

Views and experiences after delay

Most patients accepted the need to have their treatment rescheduled. Though most understood the barriers to treatment due to the pandemic, some wondered if their disease would have been as invasive without the aforementioned delays.

…you know, if we had taken more action in February, would I be in the position I’m in now? Probably still would have been thyroid cancer but would it have been as invasive? (<3 months delay)

Some patients reported a significant financial burden as a result of being treated through the private healthcare system.

…I decided to go through private because COVID was hitting and I just felt very anxious. …I think it ended up costing about $20 000 through private. (<3 months delay)

…it did cost us a lot of money to have it done because I’d only just cancelled my private health insurance, you know, “I don’t need this anymore” and I couldn’t go public because the hospital, they had a lot of COVID patients in there so they weren’t accepting any surgeries in there. So yes, we were out of pocket a bit of money having to go with that private option but it was worth it. (<3 months delay)

The majority of patients were satisfied with their treating teams; however, a few noted that improved communication with their clinicians would have been appreciated. Almost all patients expressed that they did not want to wait any longer than they did if they had to experience similar delays again, with anxiousness and unease being the main influencing factors for their opinions.

…I was glad that it was done within that 2 months. I think it was because I didn’t know what it was. That was really concerning. So dragging that on would have increased my anxiety. (<3 months delay)

Although concerns about cancer treatment are not unique to COVID-19, the findings of this study suggest that the pandemic may have increased the fragility and unease experienced by patients with thyroid cancer, including hesitancy about going to the hospital during this time. 12 The interviews highlighted patients’ worries and concerns around the delayed treatment during COVID-19, especially uncertainty surrounding when surgery and other treatments would resume. Although this may be evident without the pandemic, it is clear that patients with thyroid cancer may not be comfortable delaying treatment. 13 However, it was also found that some patients were accepting the delays as they felt it was largely out of their control.

There were notable changes in the management of patients during the COVID-19 pandemic. A higher proportion of the patients interviewed were treated through the private healthcare system compared with those treated through the public system ( table 2 ), with one patient reporting a greater financial burden due to being treated privately, rather than in the public healthcare system. 14 Telehealth became the primary means of communication between patients and clinicians and provided flexible approaches to providing patient care throughout the pandemic. 15 16 However, its limitations include an inability to have physical examinations and a potential impact on communication. 17 Barriers to seeing doctors in person throughout the pandemic, due to limited in-person consultations and inaccessibility to the healthcare providers nearby, meant that physical examinations and thyroid biopsies may have been hindered during this time. 18

The idea of ‘fear of the unknown’ emerged from the interviews, with many patients expressing concerns about not knowing when their treatment would recommence. Not only did the pandemic impact public health services at an operational level but patients also expressed unease towards being treated—whether diagnostic tests or surgery—in a public setting during this time. Consequently, these factors may have elevated patients’ concerns regarding self-perceived risk of disease progression which has also been observed in other cancer types during the pandemic. 19 In a recent study of thyroid clinicians’ views towards delayed treatment during the COVID-19 pandemic, most clinicians suggested that while they often did not have concerns regarding disease progression, they worried about the anxiousness patients may face during their delays. 4 In other words, clinicians were more worried about the patient’s perception and anxiousness than the actual disease progression.

Active surveillance is a type of management for low-risk thyroid cancers that are now emerging in countries outside Japan; however, this management strategy has differing levels of uptake due to the clinician and patient preferences. 20–24 While patients in this study did not undergo active surveillance, it is of interest to consider if a similar management strategy could be used for patients undergoing delays in treatment with regular and planned clinical review. The findings of this study may suggest that discussing the perceived risk of disease and feasibility of active surveillance to eligible patients could alleviate unnecessary concerns surrounding not receiving immediate surgical treatment. However, it is important to note that many of the patients in this study would not have been eligible for active surveillance due to the nature of their tumour sizes and types.

A similar study using semi-structured interviews was conducted in North America, involving patients with breast and prostate cancer. The findings of this study highlighted the importance of ongoing communication between clinicians and patients, as well as patients experiencing anxiousness associated with delays—similar to the findings of this study. 8

To our knowledge, this is the first qualitative study to explore the impact of delayed diagnosis and treatment on patients with thyroid cancer during the COVID-19 pandemic. It was important to keep the scope of enquiry broad so as not to restrict the contents of the interviews. The semi-structured nature of the interviews allowed the patients to discuss additional ideas and subthemes for researchers to analyse—niche findings that may not have been captured in a more structured interview format. Additionally, the interview schedule was developed by a multidisciplinary team, which ensured that relevant information was captured.

However, the study is limited by the retrospective nature of the interviews, which may lead to recall bias; interviewing patients during or immediately after their delayed experience may have provided a more robust account of their experience. However, registry-based recruitment was significantly impacted by the COVID-19 pandemic, including the limited capacity to invite participants to studies such as this. Another limitation of this study was that the identification of participants through their treating clinician may have introduced unintended sampling bias to the study. However, the initial stages of this study occurred during the height of the COVID-19 pandemic, and recruitment via the key endocrine surgeons was the most appropriate means of recruitment. While the project also recruited participants via the Australia and New Zealand Thyroid Cancer Registry, the pandemic significantly impacted the core registry operations, including mailing out invitations to potential study participants. The sample was not truly reflective of the population, as this was only a state-based subset of the Australian-based population, and patients from other countries with differing COVID-19 restrictions and healthcare systems may have had different experiences. However, this study was undertaken in Australia when the pandemic was relatively well-controlled, and healthcare restrictions were in place that offered a different insight into the experience of treatment delay. Additionally, non-English-speaking participants were excluded—potentially impacting the generalisability of these findings to a broader population. 25 The differences in the length and circumstances surrounding delays varied between patients, which is expected given the variability in restrictions across Australia during this time period, as well as the underlying themes and subthemes we reported. One of the issues faced in this study is potential missing data (i.e., who communicated the treatment delay; method of communication). In future research, it may be beneficial to conduct a supporting quantitative survey to maximise the data completeness, as well as adding the appropriate psychological measures by using validated scales for outcomes like anxiety. It would be of interest to also interview patients who have not experienced delays in their treatment during unexpected delays (i.e., a pandemic), which may allow for comparisons to be made between patients who experience delays versus those who do not.

The COVID-19 pandemic has sharpened the focus on the importance of healthcare communication. During times of unexpected delays, it is vital for clinicians to communicate the perceived risk of disease to patients with thyroid cancer. Providing patients with more accessible information surrounding disease management may ease some of the anxiousness experienced by patients. Additionally, the feasibility of active surveillance should be regularly discussed with patients with low-risk thyroid cancer. Research now suggests that patients can be more accepting of active surveillance when given informed and balanced options for treatment, 20 21 26 particularly when clinicians promote this option. 20 21 It is also important for clinicians to encourage patients to express their concerns if they are unclear about the information provided to them. Finally, it is imperative that delays are followed up by a medical review to improve overall communication and understanding.

Conclusions

This qualitative study indicates an increased burden on patients with thyroid cancer who experienced delayed diagnosis and/or treatment during COVID-19. The findings presented provide important insights which may be similar in the case of any unexpected delay to diagnosis or treatment and can help to improve future cancer communication and management. Further research is required to assess the clinical impact of possible disease progression on patients with cancer impacted by COVID-19 delays.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and ethical approval for the study was provided by the University of Sydney Human Research and Ethics Committee (HREC) (project no. 2020/350), and the study was registered with the Monash University HREC (project no. 28781). Participants gave informed consent to participate in the study before taking part.

Acknowledgments

We would like to thank the patients and referring clinicians involved in this study.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1
• Data supplement 2

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Contributors BD: formal analysis (lead), investigation (equal), writing—original draft, writing—review and editing (equal); AG: resources, supervision (equal), writing—review and editing (equal); CB: formal analysis (supporting), writing—review and editing (equal); BB: resources, writing—review and editing (equal); RHD: formal analysis (supporting), writing—review and editing (equal); JCL: resources, writing—review and editing (equal); JM: writing—review and editing (equal); JAM: resources, writing—review and editing (equal); JRZ: writing—review and editing (equal); JS: writing—review and editing (equal); LI: supervision (equal), writing—review and editing (equal); BN: conceptualisation, formal analysis (supporting), investigation (equal), methodology, supervision (equal), writing—original draft (supporting), writing—review and editing (equal). BN is the guarantor.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient and public involvement Patients and/or the public were involved in the design, conduct, reporting or dissemination plans of this research. Refer to the Methods section for further details.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Evidence Review of the Adverse Effects of COVID-19 Vaccination and Intramuscular Vaccine Administration

Vaccines are a public health success story, as they have prevented or lessened the effects of many infectious diseases. To address concerns around potential vaccine injuries, the Health Resources and Services Administration (HRSA) administers the Vaccine Injury Compensation Program (VICP) and the Countermeasures Injury Compensation Program (CICP), which provide compensation to those who assert that they were injured by routine vaccines or medical countermeasures, respectively. The National Academies of Sciences, Engineering, and Medicine have contributed to the scientific basis for VICP compensation decisions for decades.

HRSA asked the National Academies to convene an expert committee to review the epidemiological, clinical, and biological evidence about the relationship between COVID-19 vaccines and specific adverse events, as well as intramuscular administration of vaccines and shoulder injuries. This report outlines the committee findings and conclusions.

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• Digital Resource: Evidence Review of the Adverse Effects of COVID-19 Vaccination
• Digital Resource: Evidence Review of Shoulder Injuries from Intramuscular Administration of Vaccines
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