impact of covid 19 on global economy research paper

• Systematic review update
• Open access
• Published: 16 February 2024

The global economic burden of COVID-19 disease: a comprehensive systematic review and meta-analysis

• Ahmad Faramarzi   ORCID: orcid.org/0000-0001-5661-8991 1 ,
• Soheila Norouzi   ORCID: orcid.org/0000-0002-3028-7861 1 ,
• Hossein Dehdarirad 2 ,
• Siamak Aghlmand 1 ,
• Hasan Yusefzadeh 1 &
• Javad Javan-Noughabi 3  

Systematic Reviews volume  13 , Article number:  68 ( 2024 ) Cite this article

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The COVID-19 pandemic has caused a considerable threat to the economics of patients, health systems, and society.

This meta-analysis aims to quantitatively assess the global economic burden of COVID-19.

A comprehensive search was performed in the PubMed, Scopus, and Web of Science databases to identify studies examining the economic impact of COVID-19. The selected studies were classified into two categories based on the cost-of-illness (COI) study approach: top-down and bottom-up studies. The results of top-down COI studies were presented by calculating the average costs as a percentage of gross domestic product (GDP) and health expenditures. Conversely, the findings of bottom-up studies were analyzed through meta-analysis using the standardized mean difference.

The implemented search strategy yielded 3271 records, of which 27 studies met the inclusion criteria, consisting of 7 top-down and 20 bottom-up studies. The included studies were conducted in various countries, including the USA (5), China (5), Spain (2), Brazil (2), South Korea (2), India (2), and one study each in Italy, South Africa, the Philippines, Greece, Iran, Kenya, Nigeria, and the Kingdom of Saudi Arabia. The results of the top-down studies indicated that indirect costs represent 10.53% of GDP, while the total estimated cost accounts for 85.91% of healthcare expenditures and 9.13% of GDP. In contrast, the bottom-up studies revealed that the average direct medical costs ranged from US $1264 to US $79,315. The meta-analysis demonstrated that the medical costs for COVID-19 patients in the intensive care unit (ICU) were approximately twice as high as those for patients in general wards, with a range from 0.05 to 3.48 times higher.

Conclusions

Our study indicates that the COVID-19 pandemic has imposed a significant economic burden worldwide, with varying degrees of impact across countries. The findings of our study, along with those of other research, underscore the vital role of economic consequences in the post-COVID-19 era for communities and families. Therefore, policymakers and health administrators should prioritize economic programs and accord them heightened attention.

Peer Review reports

Coronavirus disease 2019 (COVID-19) is a respiratory infection instigated by the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), first identified in Wuhan, China, in December 2019. The disease has since proliferated globally at an alarming rate, prompting the World Health Organization (WHO) to declare a pandemic on March 11, 2020 [ 1 ]. As of February 21, 2023, the global total of confirmed COVID-19 cases stands at 757,264,511, with a death toll of 6,850,594 [ 2 ].

Patients afflicted with COVID-19 exhibit a range of symptoms, including flu-like manifestations, acute respiratory failure, thromboembolic diseases, and organ dysfunction or failure [ 3 ]. Moreover, these patients have had to adapt to significant changes in their environment, such as relocating for quarantine purposes, remote work or job loss, and air-conditioning [ 4 , 5 ].

The COVID-19 pandemic has imposed substantial direct and indirect costs on patients, families, healthcare systems, and communities. These costs fluctuate significantly based on socioeconomic factors, age, disease severity, and comorbidities [ 6 , 7 ]. For instance, a study conducted in the United States of America (USA) estimated the median direct medical cost of a single symptomatic COVID-19 case to be US $3045 during the infection period alone [ 8 ]. Additionally, indirect costs arising from the pandemic, such as lost productivity due to morbidity and mortality, reduced consumer spending, and supply chain disruptions, could be substantial in certain countries [ 9 ]. Studies by Maltezou et al. and Faramarzi et al. revealed that absenteeism costs accounted for a large proportion (80.4%) of total costs [ 10 ] and estimated an average cost of US $671.4 per patient [ 11 ], respectively. Furthermore, the macroeconomic impact of the COVID-19 pandemic is considerably more significant. Data from Europe indicates that the gross domestic product (GDP) fell by an average of 7.4% in 2020 [ 12 ]. Globally, the economic burden of COVID-19 was estimated to be between US $77 billion and US $2.7 trillion in 2019 [ 13 ]. Another study calculated the quarantine costs of COVID-19 to exceed 9% of the global GDP [ 14 ].

Evaluating the cost of COVID-19, encompassing both direct (medical and non-medical) and indirect costs, provides valuable insights for policymakers and healthcare managers to devise effective strategies for resource allocation and cost control, particularly in the post-COVID-19 era. Despite the abundance of literature on COVID-19, only a handful of studies have concentrated on its economic burden. Furthermore, the currency estimates provided in these articles is inconsistent. To address this gap, our study aimed to conduct a systematic review and meta-analysis of the global economic burden of COVID-19. The objectives of this study are twofold: firstly, to estimate the direct and indirect costs of COVID-19 as a percentage of GDP and health expenditure (HE) at the global level, and secondly, to estimate the direct medical costs based on the inpatient ward, which includes both the general ward and the intensive care unit (ICU).

This study was designed according to the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines [ 15 ].

Search strategy and data sources

We performed a comprehensive search in PubMed, Scopus, and Web of Science databases to retrieve studies on the economic burden of COVID-19 disease. To this objective, we conducted a comprehensive search by combining the search terms relating to COVID-19 (coronavirus, 2019-nCoV), as a class, with the terms relating to the economic burden and terms related to it (direct cost, indirect cost, productivity cost, morbidity cost, mortality cost, cost analysis, cost of illness, economic cost, noneconomic cost, financial cost, expenditure, spending). The search was limited to English language publications and human studies that were published before September 19, 2021. The search strategy was validated by a medical information specialist. All search strategies are available in the Additional file 1 .

Screening and selection

Two reviewers independently screened all distinct articles, focusing on the title and abstract and utilizing EndNote software. The reviewers were blinded to each other’s findings during the screening phase. Potential duplicates were identified and scrutinized to exclude identical entries. Any discrepancies between the reviewers were reconciled through consensus or by consulting a third reviewer. The final decision regarding inclusion was determined subsequent to a comprehensive review of the full-text article. The whole process of the study selection was outlined in a flow chart (Fig. 1 ).

Flowchart depicting the selection of research studies

This systematic review included all original studies that addressed the economic burden of COVID-19, provided they (1) estimated all costs associated with COVID-19, including both direct (medical and non-medical) and indirect (morbidity and mortality) costs and (2) were designed as observational studies or controlled clinical trials. Studies were excluded based on the following criteria: (1) they were review articles, commentaries, editorials, protocols, case studies, case series, animal studies, book chapters, or theses, (2) they estimated costs for a specific disease or action during the COVID-19 pandemic, and (3) they were studies assessing budget impact or economic evaluations.

Data extraction

A specific data extraction template was developed to extract relevant information from every study that satisfied our eligibility criteria. The data extracted covered the general study characteristics (authors, study publication, geographical location of data collection), cost-related information (direct medical cost, direct nonmedical cost, indirect cost, total cost, years of costing, and currency), and participants-related data (sample size and population studied for estimation).

Outcome and quality assessment

The primary outcomes were documented as the standardized mean difference (SMD) accompanied by 95% confidence intervals, representing the direct medical costs borne in general wards as compared to ICU for patients diagnosed with COVID-19. Additionally, another outcome was the estimation of these costs as a proportion of the GDP and health expenditure (HE).

A quality assessment was conducted on all the included studies, utilizing the checklist formulated by Larg and Moss [ 16 ]. This checklist comprises three domains: analytic framework, methodology and data, and analysis and reporting. The quality assessment was independently corroborated by two reviewers. In case of any discrepancies in the quality assessment, resolution was ensured through consensus or consultation with a third reviewer.

Statistical analysis

To analyze the data, we utilized the cost-of-illness (COI) study approach, which involved categorizing the studies into two groups: top-down studies and bottom-up studies. Top-down studies were defined as population-based methods that estimated costs for a specific country or group of countries, while bottom-up studies were defined as person methods that estimated costs per person [ 16 ].

In our methodological approach to the top-down studies, we initially categorized the costs into direct and indirect types. The direct costs comprised both medical and nonmedical expenses, while the indirect costs were related to potential productivity losses stemming from mortality and morbidity. Subsequently, we undertook the adjustment of all costs to the 2020 US dollar value. This was achieved based on the principle of purchasing power parity (PPP), and we utilized the currency conversion factor as recommended by the World Bank for this purpose. We employed the method proposed by Konnopka and König to present the COVID-19 cost to top-down studies. This method, which expresses the costs as a proportion of the gross domestic product (GDP) and health expenditure (HE), eliminates the need for adjustments for inflation or differences in purchasing power [ 17 ]. Moreover, we computed the costs using both an unweighted mean and a population-weighted mean.

In the bottom-up studies, a random-effects model was employed for the meta-analysis, with the SMD serving as the measure of effect size. To mitigate the influence of heterogeneity, all costs were converted to 2020 US dollars based on PPP, utilizing the currency conversion factor suggested by the World Bank. The focus of our analysis was a comparison of the direct medical costs of patients admitted to the general ward versus those in ICU. The SMD was calculated as the measure of effect size, with the sample size acting as the weighting factor. Heterogeneity was assessed through Cochran’s Q test and the I 2 statistic. The Q -test, a classical measure with a chi-square distribution, is calculated as the weighted sum of squared differences between individual study effects and the pooled effects across studies. The I 2 statistic represents the percentage of variation across studies, with threshold values of 25%, 50%, and 75% indicating low, moderate, and high levels of heterogeneity, respectively. To assess possible publication or disclosure bias, we used funnel plots, the Begg-adjusted rank correlation test, and Egger’s test. All statistical analyses were performed using STATA version 14 (Stata Corp, College Station, TX, USA), and P -values less than 0.05 were considered as statistically significant.

The study selection process is illustrated in Figure 1 . The search strategy produced 3271 records (Scopus, 1450; PubMed, 1144; Web of Science, 677), from which 1358 duplicates were eliminated. Out of the remaining 1913 articles, a mere 101 satisfied the inclusion criteria and underwent a full-text review. During this full-text screening, 74 articles were excluded for various reasons, resulting in a final selection of 27 studies included in the systematic review. Among these, 20 were bottom-up studies [ 7 , 10 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ], and 7 were top-down studies [ 36 , 37 , 38 , 39 , 40 , 41 , 42 ].

Characteristics of included studies

Table 1 presents the general characteristics of the included studies. Out of the 27 studies, 5 were conducted in the USA; 5 in China; 2 each in Spain, Brazil, South Korea, and India; and 1 each in Italy, South Africa, the Philippines, Greece, Iran, Kenya, Nigeria, and the Kingdom of Saudi Arabia. Based on the methodology employed, 20 studies were categorized as bottom-up studies and seven as top-down studies.

Among the seven top-down studies, only three calculated direct medical costs [ 37 , 38 , 41 ], two studies examined the direct nonmedical costs [ 38 , 41 ], and all but Santos et al. [ 37 ], who did not report these costs, calculated indirect costs. Of the 20 bottom-up studies, all but 1 study [ 31 ] assessed the direct medical costs. Only four studies calculated the direct nonmedical costs [ 10 , 19 , 29 , 34 ], and seven studies reported the indirect costs [ 7 , 10 , 19 , 26 , 29 , 31 , 34 ].

Table 2 presents the specific characteristics of the top-down studies. These studies indicate that the direct costs of COVID-19 span from US $860 million to US $8,657 million, while indirect costs range from US $610 million to US $5,500,000 million. On average, top-down studies estimate the direct costs associated with COVID-19 to constitute 2.73% and 0.39% of healthcare expenditures, based on unweighted and weighted means, respectively. The results also reveal that, on average, indirect costs account for 10.53% of GDP, with a range of 0.02 to 30.90%. Furthermore, the total cost estimated by top-down studies comprises 85.91% of healthcare expenditure and 9.13% of GDP.

Table 3 outlines the specific characteristics of the bottom-up studies. Excluding two studies [ 23 , 27 ], all reported their sample sizes, which varied from 9 to 1,470,721. The mean estimate of direct medical costs ranged from US $1264 to US $79,315. Two studies reported values for direct nonmedical costs [ 19 , 29 ], with means of US $25 and US $71. The mean estimate of indirect costs ranged from US $187 to US $689,556.

Meta-analysis results

The results of the meta-analysis for the direct medical costs are shown in Figure 2 . The results indicate a significant association between the mean cost of direct medical services and the inpatient ward. Specifically, the analysis yielded a standardized mean difference (SMD) of 1.62 ( CI : 0.9–2.35) with a substantial degree of heterogeneity ( Q = 26170, p < 0.0001; I 2 = 100%).

Mean direct medical cost for patient with COVID-19 based on disease severity

Assessment of publication bias

Figure 3 presents the information related to publication bias. The funnel plot, constructed from the studies included, does not suggest the presence of potential publication bias. Moreover, the application of Begg’s and Egger’s tests in the statistical analysis resulted in P-values of 0.788 and 0.789, respectively, indicating an absence of significant bias.

The funnel plots, Begg’s test, and Egger’s test to assessment of publication bias for included studies that assessed the direct medical costs of patients hospitalized in the general ward versus those in the intensive care unit (ICU)

This investigation represents the initial systematic review and meta-analysis conducted on the topic of the global economic impact of COVID-19. Furthermore, it is the first study to evaluate economic burden research related to COVID-19 using both top-down and bottom-up approaches, and it has conducted a meta-analysis of medical direct expenses based on hospitalization wards. In general, studies examining the economic impact of COVID-19 are scarce, with a greater proportion of studies employing a bottom-up approach. More than 30% of these studies were conducted in the USA and China. Patients admitted to the ICU ward exhibited higher costs than those admitted to the general ward.

Admission to the ICU significantly escalated the medical expenditure associated with COVID-19 treatment. This study discovered that the medical costs for COVID-19 patients in the ICU were approximately twice as high as those for patients in general wards, with a range from 0.05 to 3.48 times higher. This finding aligns with existing literature, which suggests that ICU patients with COVID-19 are more likely to require expensive treatments such as mechanical ventilation and extracorporeal membrane oxygenation, compared to those in general wards [ 44 , 45 ]. Consistent with this, other studies have reported an increase in medical expenditures with the hospitalization of COVID-19 patients in the ICU. For instance, a study conducted in the USA found a fivefold increase in costs for patients in the ICU who required invasive mechanical ventilation (IMV), compared to those not in the ICU or without IMV [ 22 ]. Similarly, a study in China reported a 2.5-fold increase in costs for severe COVID-19 patients compared to mild cases [ 30 ]. Given the elevated medical costs associated with treating COVID-19 patients in the ICU or those with severe symptoms, health policymakers must concentrate on implementing programs that promote early diagnosis. Consequently, healthcare providers could initiate treatment at an earlier stage, potentially reducing the severity of the disease and associated costs.

Our research indicates that significant variations in estimated costs would be observed if these costs were reported in PPP, particularly in relation to direct medical expenses. The lowest value was calculated in India, amounting to US $1264, while the highest value was observed in the USA, reaching US $54,165. Furthermore, the calculated medical costs varied across countries. For example, in the USA, direct medical expenditures ranged from US $1701 to US $54,156 [ 21 , 35 ]. In contrast, in China, the reported costs fluctuated between US $5264 and US $79,315 [ 7 , 25 ]. Several factors contribute to this variation in the estimation of direct medical costs. Primarily, direct medical costs cover a spectrum of services, including diagnosis, medication, consumables, inpatient care, and consultation services. Consequently, each study may have estimated the direct medical costs for a subset or the entirety of these services, leading to differences in the estimated costs. For instance, Nguyen et al. demonstrated a nearly threefold increase in direct costs for COVID-19 patients managed with extracorporeal membrane oxygenation (ECMO) compared to patients not receiving ECMO [ 35 ]. This highlights the impact of specific treatments on the overall cost. Secondly, the sample size may vary between studies, resulting in different cost estimates. Larger sample sizes typically provide more accurate and reliable estimates, but they also require more resources to collect and analyze. Lastly, the studies may have estimated costs for patients with varying conditions, such as those in acute status, patients hospitalized in general wards, or those admitted to ICU wards.

In addition to direct medical expenditures, the indirect costs arising from productivity losses due to COVID-19 have substantial societal implications. This study discovered that direct medical expenses attributable to COVID-19 varied from US $860 million (representing 0.11% of China’s healthcare expenditure) as reported by Zhao et al. [ 38 ] in China to US $8657 million (equivalent to 7.4% of Spanish healthcare expenditure) as reported by Gonzalez Lopez et al. [ 41 ] in Spain. On a global scale, direct medical costs due to COVID-19 constituted 2.73% of healthcare expenditure and 0.25% of GDP. The results also unveiled that the indirect costs of the COVID-19 pandemic impacted different countries to varying extents. The minimum value of indirect costs was estimated in Italy [ 40 ] and India [ 39 ] at US $610 million and US $658 million, respectively. Interestingly, when reported as a percentage of GDP, India had a lower cost (0.02% of GDP) compared to China (0.03% of GDP). The maximum value of indirect costs was calculated in the USA at US $5,500,000 million, which accounted for approximately 26.32% of the USA’s GDP [ 36 ]. Despite the numerical value of indirect costs being lower in Spain than in the USA and China, it represented a higher percentage of GDP (30.90%). The resulting pooled estimate indicated that the indirect costs due to COVID-19 were responsible for 10.53% of global GDP. The review underscores the significant economic repercussions of COVID-19. The total costs in the USA accounted for about 157% of healthcare expenditure and 26% of GDP, in China for 80% of healthcare expenditure and 4.28% of GDP, and in Spain for approximately 345% of healthcare expenditure and 32% of GDP. Globally, the total costs of COVID-19 accounted for about 86% of healthcare expenditure and 9.13% of GDP. This highlights the profound economic impact of the pandemic on both healthcare systems and economies worldwide.

Strengths and limitation

Our study possesses several significant strengths. It is the inaugural meta-analysis of the worldwide costs associated with COVID-19, supplementing a systematic review conducted by Richards et al. on the economic burden studies of COVID-19 [ 12 ]. A considerable number of studies was conducted in the USA and China, but our analysis also incorporated studies from other high- and low-income countries, potentially enhancing the generalizability of our findings. Recognizing that economic burden studies often display significant heterogeneity, we endeavored to minimize this by distinguishing between bottom-up and top-down studies and standardizing currencies to US dollars in terms of PPP.

However, our study is not without limitations. As is typical with all meta-analyses of economic burden studies, the most substantial limitation is heterogeneity. This heterogeneity can originate from various factors, including differences in study design, the range of services included in individual studies, the year of estimation, the currencies used for estimation, the study population, among other factors. Our systematic review only incorporated studies that estimated costs for an actual population, thereby excluding a wide array of studies on the economic burden of COVID-19 that employed modeling techniques. Future research could potentially conduct systematic reviews and meta-analyses on cost estimation modeling studies for COVID-19. Lastly, while no publication bias was detected through statistical analysis, our study was limited to papers written in English. As a result, numerous papers published in other languages were inevitably excluded.

Our research indicates that the COVID-19 pandemic has imposed a substantial economic strain worldwide, with the degree of impact varying across nations. The quantity of studies examining the economic repercussions of COVID-19 is limited, with a majority employing a bottom-up methodology. The indirect costs ascribed to COVID-19 constituted 10.53% of the global GDP. In total, the costs linked to COVID-19 represented 9.13% of GDP and 86% of healthcare spending. Moreover, our meta-analysis disclosed that the direct medical expenses for COVID-19 patients in the ICU were almost twice those of patients in general wards. The results of our research, along with those of others, underscore the pivotal role of economic outcomes in the post-COVID-19 era for societies and families. Consequently, it is imperative for policymakers and health administrators to prioritize and pay greater attention to economic programs.

Availability of data and materials

Data sharing is not applicable as no new data were generated during the study. The data analysis file during this study is available from the corresponding author on reasonable request.

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Ahmad Faramarzi, Soheila Norouzi, Siamak Aghlmand & Hasan Yusefzadeh

Department of Medical Library and Information Science, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

Hossein Dehdarirad

Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

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Faramarzi, A., Norouzi, S., Dehdarirad, H. et al. The global economic burden of COVID-19 disease: a comprehensive systematic review and meta-analysis. Syst Rev 13 , 68 (2024). https://doi.org/10.1186/s13643-024-02476-6

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Research Article

Effects of COVID-19 on trade flows: Measuring their impact through government policy responses

Contributed equally to this work with: Javier Barbero, Juan José de Lucio, Ernesto Rodríguez-Crespo

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Joint Research Centre (JRC), European Commission, Seville, Andalucía, Spain

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Economic Structure and Development Economics, Universidad de Alcalá de Henares, Alcalá de Henares, Madrid, Spain

Affiliation Department of Economic Structure and Development Economics, Universidad Autónoma de Madrid, Madrid, Spain

• Javier Barbero, 
• Juan José de Lucio, 
• Ernesto Rodríguez-Crespo

• Published: October 13, 2021
• https://doi.org/10.1371/journal.pone.0258356
• Peer Review
• Reader Comments

This paper examines the impact of COVID-19 on bilateral trade flows using a state-of-the-art gravity model of trade. Using the monthly trade data of 68 countries exporting across 222 destinations between January 2019 and October 2020, our results are threefold. First, we find a greater negative impact of COVID-19 on bilateral trade for those countries that were members of regional trade agreements before the pandemic. Second, we find that the impact of COVID-19 is negative and significant when we consider indicators related to governmental actions. Finally, this negative effect is more intense when exporter and importer country share identical income levels. In the latter case, the highest negative impact is found for exports between high-income countries.

Citation: Barbero J, de Lucio JJ, Rodríguez-Crespo E (2021) Effects of COVID-19 on trade flows: Measuring their impact through government policy responses. PLoS ONE 16(10): e0258356. https://doi.org/10.1371/journal.pone.0258356

Editor: Stefan Cristian Gherghina, The Bucharest University of Economic Studies, ROMANIA

Received: April 12, 2021; Accepted: September 26, 2021; Published: October 13, 2021

Copyright: © 2021 Barbero 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: The data underlying the results presented in the study are available from UN Comtrade ( https://comtrade.un.org ), the Centre d'Études Prospectives et d'Informations Internationales (CEPII) Gravity database ( http://www.cepii.fr/cepii/en/bdd_modele/presentation.asp?id=8 ) and from Our World in Data COVID-19 Git Hub repository ( https://github.com/owid/covid-19-data/tree/master/public/data ). The three datasets are publicly available for all researchers. Merging the three datasets and following the steps described in the “Model description and estimation strategy” section readers can replicate the results of this manuscript.

Funding: de Lucio and Rodríguez-Crespo thank financial support from Universidad de Alcalá de Henares (UAH) and Banco Santander through research project COVID-19 UAH 2019/00003/016/001/007. De Lucio also thanks financial support from Comunidad de Madrid and UAH (ref: EPU-INV/2020/006 and H2019/HUM5761).

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

Introduction

The world is facing an unexpected recession due to the disruption of the COVID-19 pandemic in the global economy. In parallel with the consequences of the 2008–2009 crisis, international trade has once again collapsed. World trade volumes decreased by 21% between March and April 2020, while during the previous crisis the highest monthly drop was 18%, between September and October 2008. Cumulative export growth rate for the period December 2019– March 2020 was -7%, while for the period July 2008 –February 2009 it was -0,8%. The 2020 downturn was less prolonged than that caused by the latter crisis. Trade volumes in August 2020 only showed a 3% decrease compared to March 2020. The World Trade Organization (WTO) estimated that international merchandise trade volumes fell by 9.2% in 2020, a figure similar in magnitude to the global financial crisis of 2008–2009, although factors such as the economic context, the origins of the crisis and the transmission channels are deemed to be very distinct from the previous crisis [ 1 ].

Due to its rapid propagation, a proper evaluation of the economic impacts of COVID-19 crisis is not only desirable but challenging if the aim is to mitigate uncertainty [ 2 ]. The COVID-19 crisis has its origins in the policy measures adopted to combat the health crisis, while the 2008–2009 crisis had economic roots contingent on financially related issues. At the current time, the collapse of international trade has been driven by the voluntary and mandatory confinement measures imposed on world trade. We aim to analyse the impact of said confinement measures on trade. Estimating COVID-19 impacts on trade would shed light on the cost of confinement measures and the evolution and forecast of bilateral trade.

From an empirical point of view, we resort to [ 3 ], who use quarterly data for the period 2003–2005 in order to analyse the impact of the SARS epidemic on firms. They show that regions with higher transmission of SARS experienced lower import and export growth compared to those in the unaffected regions. The propagation of a virus resembles natural disasters, with both interpreted as external non-economic based shocks, the effects of which have been addressed already in the literature (e.g., [ 4 – 9 ]).

Another strand of the literature directly analyses the effects of the COVID-19 pandemic on global trade in terms of the transmission mechanism of the shock: demand, supply and global supply chains. Some authors argue that demand factors have played an important role in explaining the shock [ 10 , 11 ] conclude that both shocks (demand and supply) are present in the crisis [ 1 ] highlight the role of global value chains in the transmission of shocks [ 12 ] focus on supply chain disruptions and reveal that those sectors with large exposure to intermediate goods imports from China contracted more than other sectors [ 13 ]. Focus on the role of global supply chains on the GDP growth for 64 countries during the COVID-19 pandemic and show that one quarter of the total downturn is due to global supply chains transmission. They also conclude that in general global supply chains, make countries more resilient to pandemic-induced contractions in labour supply. Finally, the collapse in trade can also be considered as a trade-induced effect caused by economic recessions (e.g., [ 14 – 16 ]) and may also be associated with the impact of COVID-19.

During the current wave of globalization, time lags in synchronizing business cycles between countries are reduced significantly in terms of the intensity of trade relationships [ 17 ]. Find that business cycle synchronization increases when countries trade more with each other [ 18 ]. Show that bilateral trade intensity has a sizeable positive, statistically significant, and robust impact on synchronization. These results are in line with [ 19 ], who finds that greater trade intensity increases business cycle synchronization, especially in country pairs with a free trade agreement and among industrial country pairs. Our paper provides prima facie evidence that this relationship also holds during pandemic-related trade shocks.

We contribute to the literature by integrating monthly data for a trade analysis of 68 countries, 31 of which are classified as high-income. We additionally focus on differential effects between high-income and low- and middle-income countries. This paper aims to shed light on the impact of COVID-19 on exports by means of an integrated approach for a significant number of countries, thereby avoiding an individual analysis of a single country or region that could potentially be affected by idiosyncratic shocks. Given the existence of substantial differences in trade performance and containment measures exhibited by countries and trade partners and attributable in part to their income, we also study whether the impact of COVID-19 on trade differs in terms of income levels. To the best of our knowledge, both questions, the integrated impact of confinement measures and the income related effects, remain unexplored in previous studies.

A proper analysis of ex-post trade impacts related to COVID-19 requires a suitable and fruitful methodology. Gravity models can be helpful in achieving this goal, since they have recently started to incorporate monthly trade data into the analyses, albeit with empirical evidence that is still scarce and far from conclusive [ 8 , 20 , 21 ]. At the same time, several methodological issues need to be resolved adequately when using gravity models [ 22 ]. Resorting to monthly data may pose several advantages in terms of accomplishing our research goals and exploiting the explanatory power exhibited by gravity models and monthly country confinement measures. First, the data reflect monthly variations and allow us to better capture any differential effects arising across countries. Second, annual trade data do not capture the short-run impact of shocks that occur very rapidly, something which a monthly time span can achieve. Monthly data can pick up any rapid movements associated with COVID-19 measures and allow for differential shocks in relation to months and countries. This is particularly relevant given the growing importance of nowcasting and short-term analysis techniques required nowadays for an understanding of world economy dynamics. Finally, monthly data can explain the relative importance of demand and supply shocks during the course of the trade crisis.

We collect monthly trade data for 68 countries ( S1 Table ), which exported to 222 destinations between January 2019 and October 2020. Using state-of-the-art estimation techniques for trade-related gravity models, our results are threefold. First, we reveal a negative impact of COVID-19 on trade that holds across specifications. Second, we obtain results that do not vary substantially when considering different governmental measures. Finally, our results show that the greatest negative COVID-19 impact occurs for exports within groups (high-income countries and low-middle-income countries), but not between groups. These findings are robust to different tests resulting from the introduction of lagging explanatory variables, alternative trade flows (exports vs imports as the dependent variable) or COVID-19 impact measures (independent variables such as stringency index or the number of reported deaths per million population).

Literature review on COVID-19 and trade

The specific literature covering the COVID-19 induced effects on trade can be catalogued as flourishing and burgeoning, but also as incipient and inconclusive at the current time. Some studies have addressed the impact of the health-related crisis on trade. The first strand of literature analyses the effects of previous pandemics by emphasizing asymmetric impacts across sectors. Using the quarterly transaction-level trade data of all Chinese firms in 2003 [ 3 ], estimate the effects of the first SARS pandemic on trade in that year. They find that (i) Chinese regions with a local transmission of SARS experienced a lower decline in trade margins, and (ii) the trade of more skilled and capital-intensive products was less affected by the pandemic.

Despite data being scarce, other studies focus on the current COVID-19 trade shock but are usually restricted to specific countries. For the case of Switzerland [ 23 ], combine weekly and monthly trade data, for the lockdown between mid-March and the end of July. They use goods information disaggregated by product and trade partner. They find that: (i) During lockdown Swiss trade fell 11% compared to the same period of 2019, and this trade shock proved more profound than the previous trade shock in 2009, (ii) contraction in Swiss exports seems to be correlated with the number of COVID-19 cases in importing countries, but at the same time, Swiss imports are related to the stringency of government measures in the exporter country (iii) for the case of products, only pharmaceutical and chemical products remained resilient to the trade shock and (iv) the pandemic negatively affected the demand and supply sides of foreign trade [ 24 ]. Use a gravity model and focus on exports from China for the period January 2019 to December 2020. They find a negative effect of COVID-19 on trade, but said effect is largely attenuated for medical goods and products that entail working from home.

For the case of Spain [ 10 ], find that for the period between January and July 2020, stringency in containment measures at the destination countries decreased Spanish exports, while imports did not succumb to such a sharp decline. Finally [ 25 ], extends the discussion of the Spanish case to analyse the impact of COVID-19 on trade in goods and services, corroborating the existence of a negative effect. He finds a more pronounced decline for trade in services, due to the importance of tourism in the Spanish economy.

Other studies have provided additional evidence by considering a larger sample of countries. Using monthly bilateral trade data of EU member states covering the period from June 2015 to May 2020 [ 20 ], use a gravity model framework to highlight the role of chain forward linkages for the transmission of Covid-19 demand shocks. They explain that when the pandemic spread and more prominent measures were taken, not only did demand decrease further, but labour supply shortage and production halted [ 21 ]. Find a negative impact of COVID-19 on trade growth for a sample of 28 countries and their most relevant trade partners. Their findings suggest that COVID-19 has affected sectoral trade growth negatively by decreasing countries’ participations in Global Value Chains from the beginning of the pandemic to June 2020 [ 26 ]. Analyse the impact of COVID-19 on trade for a larger sample of countries, focusing on export flows for 35 reporting countries and 250 partner countries between January and August in both 2019 and 2020. However, they restrict, their study exclusively to trade in medical goods and find that an increase in COVID-19 stringency leads to lower exports of medical products. Finally [ 27 ], use maritime trade shipping data from January to June 2020 for different countries, such as Australia, China, Germany, Malaysia, New Zealand, South Africa, United States, United Kingdom, and Vietnam. By applying the automatic identification system methodology to observational data, they obtain pronounced declines in trade, albeit the effect is different for each country.

Surprisingly, little attention has been paid to the impacts of COVID-19 on trade for different country income levels and we find several reasons to consider this issue as important. First, the role of trade costs is important, as the latter are related to economic policy and direct policy instruments (e.g., tariffs) are less relevant compared to other components [ 28 ]. According to [ 29 ], differences are expected in trade between high-,low- and middle-income countries due to the composition of trade costs: information, transport, and transaction costs seem to be more important for trade between high-income countries, while trade policy and regulatory differences better explain trade between low- and middle-income countries. The second reason is related to the composition of products, given that average skills in making a product are more intensive in high-income countries resulting in increasing complexity of the products traded compared to low- and middle-income countries [ 30 ]. Accordingly, specific product categories incorporate more embedded knowledge and their production may require engaging in a global production network with multiple countries. However, it has been alleged that participation of countries in global value chains depends on their income levels due to the objectives pursued: high-income countries focus on achieving growth and sustainability, while low- and middle-income countries seek to attract foreign direct investment and increase their economic upgrading [ 31 ]. Third, it is found that low-income countries present a lower share of jobs that can be done at home [ 32 ], rendering them more sensitive to lockdowns that affect services. Finally, due to the paucity of health supplies to mitigate COVID-19, as certain healthcare commodities may not be affordable for certain low-income countries [ 33 ]. Consequently, the effect of COVID-19 on the global economy may be more pronounced for those countries with fewer healthcare resources and impacts on trade do not constitute an exception.

Due to the reasons mentioned above, we expect different countries’ responses to trade shocks induced by COVID-19 depending on their income levels, but this issue remains largely unexplored by the academic literature. The only exception is [ 34 ], but they reduce their analysis to COVID-19 impacts on trade concerning Commonwealth countries. Using the period from January 2019 to November 2020, they find ambivalent evidence: an increase in the number of COVID-19 cases in low-income countries reduced Commonwealth exports, but an identical scenario in high-income countries boosted their export flows.

All these findings are summarized as follows. In Table 1 , we present a compilation of studies using monthly data that feature the impact of COVID-19 on trade.

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https://doi.org/10.1371/journal.pone.0258356.t001

It is also worth noting that the main shock started in March 2020, when most countries closed their borders and implemented lockdown measures. Accordingly, further analysis of COVID-19 impacts on trade requires periods with high frequency, such as monthly data, in order to deliver satisfactory results. Apart from the aforementioned studies in the context of COVID-19 research, to the best of our knowledge, monthly data are scarcely used in gravity models. They have been used either in the context of trade preferences [ 35 , 36 ] or, more recently, to study the impact of natural disasters on trade [ 8 ].

Additionally, we aim to provide evidence concerning the COVID-19 effects on trade at country level, without restricting our research to either specific countries or territories, or, specific trade flows, such as intermediate goods. Since the COVID-19 crisis is still ongoing, it is also necessary to incorporate the most recent and updated time spans to provide policy recommendations aligned with the business cycle. We intend to analyse whether COVID-19 impacts on trade have affected the world economy from a global perspective. This analysis will allow us to distinguish different impacts in terms of levels of economic development, which to the best of our knowledge, remain largely unexplored by the academic literature.

Empirical analysis

This section is organized into three separate sub-sections. First, we describe the empirical model and the estimation strategy. Second, we report information on data issues. Finally, we cover country policy responses to COVID-19.

Model description and estimation strategy

For the purpose of accomplishing our research objectives, we resort to a bilateral trade gravity model, which has progressively become the reference methodology for analysing the causal impacts of specific variables on trade (e.g., [ 22 , 37 – 40 ]; among other scholars).

Where subscripts i , j and m refer to exporter and importer country and month, respectively. COVID m is a control variable that takes value 1 for a COVID-19 trade shock, after March 2020, and 0 otherwise. DIST ij is the geographical distance between exporter and importer country. CONTIG ij is a control variable that takes value 1 when exporter and importer country are adjacent and 0 otherwise. COMLANG ij is a control variable that takes value 1 when exporter and importer country share a common language and 0 otherwise. COLONY ij is a control variable that takes value 1 when exporter and importer country share past colonial linkages and 0 otherwise. RTA ij is a control variable that takes value 1 when exporter and importer country have a regional trade agreement in force and 0 otherwise. In addition to these explanatory variables, we also consider other control variables to capture omitted factors. φ im and γ jm are exporter-month and importer-month fixed effects, respectively. Finally, ε ijm stands for the error term.

The logic behind including these variables is found in the literature, and the explanation is provided as follows: COVID-19-related variables are introduced to estimate the impact of the current COVID-19-shock on trade, since it is expected to be detrimental (e.g., [ 3 , 23 ]). Governmental actions are expected to reduce the duration and magnitude of COVID-19 shock by facilitating a smoother transition to a post-pandemic scenario while generating an economic downturn in the short-run due to the limitations of economic activity and the increase in government expenditure. Adjacency and distance relate to the geographical impacts on trade, given the great influence exerted by geography on trade patterns [ 43 ]. Adjacency is included due to the existence of a border effect, where countries tend to concentrate their trade flows with nearby trade partners [ 41 , 44 ], so that higher adjacency leads to increasing trade flows. The reasons to include distance in gravity models stem from the early contributions [ 45 ]. Countries prefer to trade with less distant trade partners, so that a negative coefficient is expected. Colonial linkages and common language respond to the flourishing literature on the impact of institutions on trade, where the latter play a key role in reducing trade costs and facilitating trade (e.g., [ 46 , 47 ]; among others). Finally, regional trade agreements have multiplied exponentially in the context of globalization and trade liberalization, so that they contribute to decreasing trade costs and enhancing trade [ 48 , 49 ].

Exporter-month and importer-month fixed effects are included to comply with multilateral resistance terms (MRTs), which are related to third-country impacts on the bilateral relationship. They are considered as a pivotal element of modern gravity equations [ 41 ]. According to the structural gravity literature, the omission of such aforementioned MRTs is expected to lead to inconsistent and biased outcomes [ 22 ].

Concerning the previous gravity specification, it is important to highlight that our baseline gravity Eq ( 1 ) does not contain GDP, which is considered a fundamental variable in the seminal gravity models because it measures country size [ 45 ]. The omission of GDP is intentional due to several reasons: (i) GDP variables tend to vary quarterly or yearly and (ii) the inclusion of exporter-month and importer-month fixed effects are perfectly collinear with GDP, so that these control variables will capture its effects on trade.

Eq ( 2 ) introduces some novelties in relation to the previous Eq ( 1 ). First, by interacting the COVID-19 variable with the control variable for regional trade agreements we can compute the impact of COVID-19 on trade by assuming that countries with regional trade agreements affect this empirical relationship. Thus we assess whether COVID-19 impacts on trade are more (less) profound for these countries with (without) previous regional trade agreements. We sum 1 to the variable before taking the logs to avoid losing the observations before the COVID started. This strategy responds to the strands of literature that acknowledge the role of international trade as a driver of business cycle synchronization (e.g., [ 51 , 52 ]) and, more specifically, that regional trade agreements may be behind the transmission of shocks across countries (e.g., [ 19 , 53 , 54 ]). In particular, we follow [ 26 ] approach since the interaction between COVID-19 variables and regional trade agreements allows us to study the heterogeneous impacts of COVID-19 on trade by bringing economic linkages into the discussion.

Another advantage is that interacting an explanatory variable with a control variable may also relieve us from endogeneity issues, as shown by [ 55 ]. In particular, COVID-19 impacts on trade may be driven by omitted variable bias. However, this comes at the cost of not interpreting exporter and importer impacts simultaneously, as both coefficients become symmetric and only one of them can be interpreted, as shown by [ 56 ] and [ 57 ] when studying the impact of institutions on trade using gravity equations at country and regional level, respectively.

Eq ( 2 ) also considers a third set of fixed effects, which are exporter-importer pair effects denoted by η ij . Considering pair effects in the gravity equation as an explanatory variable may pose the advantage of mitigating the estimation from endogenous impacts induced by time-invariant determinants (e.g., [ 58 , 59 ]) and hence may improve the empirical specification. Three-way fixed effects, constituted by pair, exporter-month and importer-month fixed effects, have become the spotlight in gravity specifications assessing the impact of natural disasters on trade, even for those scholars using monthly data, such as [ 8 ].

Finally, we acknowledge that the simultaneous inclusion of such three-way fixed effects, requires large amounts of data in order to carry out the estimation procedure. For this reason, sophisticated PPML estimation commands have recently been developed for gravity equation estimations, so that they can include a high number of dimensional fixed effects and run relatively fast in contrast to previous existing commands [ 60 , 61 ].

Our sample covers a set of 68 countries exporting across 222 destinations, between January 2019 and October 2020 with 31 of these exporters classified as high-income countries. Due to the specific monthly nature of COVID-19 shock, we rely on monthly bilateral trade flows gathered from UN Comtrade. Trade data were extracted on the 15 of February 2021, using the UN Comtrade Bulk Download service. According to the degree of availability of monthly trade flows for countries, our analysis covers aggregate trade flows. For those observations with missing trade flows, we conveniently follow previous studies that suggest that missing trade flows can be completed with zeros, [ 50 , 62 ].

Variables related to the COVID-19 government response have been taken from the systematic dataset of policy measures elaborated by the Blavatnik School of Government at Oxford University [ 63 ]. These indices refer to government response, health measures, stringency, and economic measures. Their composition and implications are described more broadly in the following sections.

The rest of the variables, institutional and geographical, are gathered from the Centre d’Études Prospectives et d’Informations Internationales (CEPII) Gravity database [ 64 ]. S1 and S2 Tables show the list of countries and the main descriptive statistics, respectively.

Policy responses to COVID-19: equal or unequal?

Once COVID-19 spread across a significant number of countries, they were urged to implement policy actions of response. For this reason, several indicators were built to measuring countries´ governmental response to COVID-19. As mentioned previously, the set of policy indicators developed by [ 63 ] constitutes the most noteworthy approach for measuring countries´ policy responses. The four indicators are described as follows:

• Stringency index (upper left chart, Fig 1 ) contains the degree of lockdown policies to control the pandemic via restricting people´s social outcomes. The index is built using data on the closure in education (schools and universities), public transport and workspaces, the cancellation of public events, limits on gatherings, restrictions in internal movements, and orders to confine at home.
• Economic Support index (upper right chart, Fig 1 ) includes measures related to public expenditure, such as income support to people who lose their jobs or cannot work, debt relief to households, fiscal measures, and spending to other countries.
• Containment and Health index (lower left chart, Fig 1 ) combines lockdown measures with health policies, such as the openness of the testing policy to all the population with symptoms or asymptomatic, the extent of the contact tracing, the policy on mandatory use of facial coverings, and monetary investments in healthcare and in vaccines.
• Overall Government Response index (lower right chart, Fig 1 ) collects all governments’ responses to COVID-19 by assessing whether they have become stronger or weaker. This index combines all the variables of the containment and health index and the economic support index.

Source: own elaboration from [ 63 ]. Note: each point represents a country, and the concentration of countries with similar values produces darker areas. Additionally, the mean and 95% confidence bands are represented.

https://doi.org/10.1371/journal.pone.0258356.g001

These indices vary from 0 to 100, with a higher value indicating stronger country measures in response to COVID-19. As these indicators are collected daily, we convert them to monthly averages. The evolution of the four indicators is presented in Fig 1 , where each point represents a country and the concentration of countries with similar values produces darker areas. Additionally, the mean and 95% confidence bands are represented. We pay special attention to differences between high-income and low- and middle-income countries, in line with our research objectives.

Stringency reaches its maximum in April 2020 when the first wave reaches its peak in most countries. Since then, restrictions have been slowly lifted but started to increase again after the summer in high-income countries, coinciding with the beginning of the second wave.

Economic support increased rapidly in March and April and remains stable, with high-income countries granting more economic support to the population than low- and middle-income countries. The economic support variable identifies significant differences between high-income and low- and middle-income countries during the whole period.

The containment and health index and the overall government response index, present a similar pattern regarding income levels. However, we observe that low- and middle-income countries relaxed the measures from April 2021 onwards, whereas high-income countries did so in July 2021. In any case, the countries analysed show significant variability in all the indices, as indicated by the estimates made in the following section.

To sum up, we find that in low- and middle-income countries, pandemic measures have been slightly stricter than in high-income ones, as the values of their COVID-19 policy responses indices are higher for all cases except for the economic support index. The greater availability of resources in high-income countries to control the pandemic explains this difference.

Fig 2 displays the evaluation of total monthly exports in 2020, relative to January 2020, by income level for our sample of exporting countries. We observe the big decline in exports between March and April mentioned previously. In fact, the observed magnitude of trade decline as a consequence of COVID-19 is identical to the previous global recession, but contractions in GDP and trade flows are more profound at the current stage [ 65 ]. However, we observe that high-income countries have gradually been recovering their export flows, revealing a larger degree of resilience and how economic support policies might have helped them in recovering economic activity. In particular, greater firm engagement in trade because of previous global recession may be beneficial, as they have been able to recover in a shorter period of time from this new contraction in GDP due to the openness to foreign markets [ 66 ]. Find that net firm entry in export markets contributed less to export growth during the Great Trade Collapse, between 2008 and 2013, than continuing exporters. Export capacity to foreign markets in order to counteract the negative impact of local demand shocks is illustrated by [ 67 ] for the specific case of Spain.

Source: own elaboration using UN Comtrade trade data.

https://doi.org/10.1371/journal.pone.0258356.g002

We acknowledge that policy responses differ by country, as the impacts of COVID-19 have been strongly unequal for countries due to several reasons. First, countries have reported differences in the number of deaths, mainly attributable to the population composition. There is an increasing number of elder populations in a significant number of high-income OECD countries [ 68 ] and this group is the most vulnerable to COVID-19 (e.g., [ 69 ]).

At the same time, countries have also implemented trade policy actions to mitigate the influence of COVID-19 on the global economy. For the sake of brevity and in line with the aim of the article, we only consider the trade policy response. Readers interested in analyzing a complete set of economic policy responses (i.e., budgetary and/or monetary) to COVID-19 are entitled to check the International Monetary Fund (IMF) Policy Tracker at https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19 .

We focus on the corresponding period coincident with our sample, but further trade policy actions have been implemented after this period because of the increasing number of cases related to COVID-19 in subsequent waves. They can be checked at the IMF Policy Tracker previously reported, which is updated regularly [ 70 ]. Summarizes the major stylized facts during the first nine months of the pandemic. First, there was a noticeable rise in trade policy activism consisting mainly of export controls and import liberalization measures with strong cross-country variation. Second, this activism was reported to vary by country and products, where medical and food products experimented a substantial overall increase in their demand from February 2020. Third, we observe a further trade liberalization process after May 2020, where the number of liberalization measures exceeded the number of trade restrictions in medical products.

Such cross-country variation in trade policy response aligns with our expectations since, as mentioned previously, trade specialization differs by country. Accordingly, their sensitivity to the growing demand for food and medical products may vary substantially. For this reason, some countries were more resilient to COVID-19 trade shocks than other countries, as shown by the decreases observed in their trade flows. To this end, we compare trade drops for the most affected countries relative to January 2020 and their governmental response, from May 2020 to October 2020. As described by [ 70 ], countries experienced a substantial relaxation in most of their trade measures in May 2020.

For the ten countries with the largest trade drop evidence is ambivalent. For the sake of brevity, we have omitted the data concerning each country and provide a general overview. Data is available from the authors upon request to the corresponding author and can be obtained from the UN Comtrade database. On the one hand, four high-income (Macao, Mauritius, Portugal, and Slovakia) and six middle-income countries (El Salvador, Mexico, Montenegro, Guyana, Egypt, and Romania) were among the most affected countries in May 2020, with El Salvador registering the highest level of governmental response. Trade relative to January 2020 ranges from 51 to 69 percent in this period. On the other hand, we find that the number of high-income countries increased to six in October 2020 but, at the same time, differences in governmental response decreased their observed variance. Israel registered the highest level of governmental response during this month. In this case, relative trade ranges from 76 to 102, corroborating the previous finding that countries recovered rapidly from this trade shock. To sum up, despite differences in governmental response due to the impact of COVID-19 by countries, recovery can be alleged to follow similar patterns for the most affected countries.

This section is organized into three separate sub-sections. First, we present a benchmark analysis, and afterwards we show the main results obtained for the four different COVID-19 government policy response indices. Finally, we review whether COVID-19 impacts differ by levels of economic development.

Benchmark analysis

This section reviews four specifications of the gravity equation: Column (I) in Table 2 includes the COVID-19 binary variable without interactions and only including exporter and importer fixed effects. Column (II) departs from Eq ( 2 ) but introduces exporter-month and importer-month fixed effects and, finally, column (III) adds pair fixed effects to the specification showed in column (II). For this robustness analysis, we use COVID m variable, which takes value 1 from March 2020, when several countries worldwide implemented lockdown measures, and 0 otherwise.

https://doi.org/10.1371/journal.pone.0258356.t002

The coefficients shown in column (I) may be biased because the specification does not accurately capture those factors related to MRTs and structural gravity. We hence move on to the alternative specification considered in columns (II) and (III), where we choose column (3) because it corresponds to Eq ( 2 ) and solves limitations in the baseline Eq ( 1 ).

Alternative specifications show an unequivocal detrimental effect of COVID-19 shock on trade. While the magnitude of the COVID-19 coefficient in column (I) is larger, -0.204, the size decreases when we move to our baseline specification in column (III), where we interact with the RTA variable, with an estimated coefficient of -0.050 when we include pair fixed-effects. The rest of the variables show the expected coefficients according to the theoretical insights and projections of gravity models.

Results by COVID-19 government policy responses indices

We now estimate our results introducing the four COVID-19 government response indices using Eq ( 2 ), as governments in countries more affected by the pandemic are expected to response with stringency, health, and economic support measures. Table 3 shows that the effect of COVID-19 on trade is negative and significant for all the variables considered. We agree with the existing literature on the negative impact of COVID-19 on trade (e.g. [ 21 , 23 ]); and also with the negative impact of previous pandemics [ 3 ]. We also find that results do not vary substantially across indices related to COVID-19, as they range between -0.009, for containment and health measures, and -0.012 for economic support. Although estimated parameters are not statistically different from each other, this might indicate that countries demanding more support to boost their economies have been the most affected ones by the COVID-19 trade shock. We also test our results using the traditional variable of COVID-19 reported deaths per million population as impact measure of the pandemic by country. Results, available under request, show no relevant variation to those presented in the article, which might be considered as an additional robustness test of the results presented.

https://doi.org/10.1371/journal.pone.0258356.t003

Our results suggest that COVID-19 may be detrimental to trade flows for those countries engaged in previous regional trade agreements compared to the countries that were not members of these agreements, as shown by the result of interacting these variables. However, interaction terms fail to reflect that those countries not participating in regional trade agreements were not affected by COVID-19, given the large set of existing possibilities of trade integration between countries. Furthermore, these countries could have been expanding their trade flows via preferential trade agreements, which are less restrictive than regional trade agreements.

Although the estimated elasticity may mistakenly appear low, it reflects a large elasticity of trade to COVID-19, given the observed change in the four indicators under consideration. For instance, the overall government response indicator increases, on average, from 3.16 to 70.09 from February to April 2020. This change corresponded to a 2,155% increase in the government response indicator that, multiplied by the estimated elasticity of -0.010, results in a sharp decrease in export flows of around 21%. The explanation may be twofold. On the one hand, the COVID-19 trade shock may be expected to be less dampening for the economy in comparison to the trade shock induced by the global financial crisis [ 23 ], as we highlighted previously. On the other hand, the COVID-19 trade shock is still ongoing and it may be necessary to include results for the second wave commencing September 2020.

We include two additional robustness tests. First, in S3 Table we show our results with lagged independent variables, in order to check whether there exist non-contemporary impacts of COVID-19 on trade. Our results show that the estimated coefficients remain significant and with greater values than those presented in Table 3 . Second, in S4 Table we consider the estimations for import trade flows as the dependent variable. The reason for including imports responds to [ 71 ] suggestion of using mirrored datasets, given that import trade flows are more subject to trade barriers than exports. In contrast to import trade regimes, most export trade regimes tend to be free and do not require additional documents or licenses to trade the goods. Results remain invariant in relation to those presented in Table 3 and S3 Table .

Results by levels of economic development

Finally, we complement the results by analysing whether COVID-19 impacts on trade depend on the levels of economic development of exporter and importer countries, distinguishing between high, low and middle-income importers, we follow the last version of the World Bank´s classification. These results are shown in Table 4 , where each cell contains the estimated coefficient and robust standard error for a different estimation of the COVID-19 government response indicators in a PPML regression that includes exporter-month, importer-month, and pair fixed effects. For instance, column (I) presents results for trade between high income countries, where the first row shows the estimated parameter for our reference equation, in line with column (4) in Table 1 . The following four rows correspond to the estimated parameters in Table 2 for COVID-19 related variables. Results with lagged independent variables, presented in S5 Table , show that estimated coefficients remain statistically significant and higher than those presented in Table 3 .

https://doi.org/10.1371/journal.pone.0258356.t004

We find that the COVID-19 effect on trade remains negative, but it seems to be inversely related to the income levels of the importer country. While the COVID-2019 dummy variable registers the highest negative impact, -0.103 for exports between high-income countries, (see column (I)), it is -0.055 between low-and middle-income countries, (see column (II)). The greater effect of the pandemic among integrated countries is in line with the results obtained by [ 20 ]. The effect found by [ 34 ] is the opposite, as COVID-19 in high-income countries spurs Commonwealth countries´ trade. However, their analysis focuses on a selected group of countries and distinguishes product categories so that the greater demand for medical products during the pandemic can explain these results. This fact reinforces the importance of including many countries in the sample since we can expect substantial differences in cross-country variation. We also find no significant effect of COVID-19 on the trade from exports of high-income to low-income countries in column (III) and a positive effect of exports from low-income to high-income in column (IV). These results remain similar when using lagged independent variables, presented in S5 Table . As robustness tests we include in S6 Table estimations for import trade flows. Results also remain invariant in relation to those presented in S3 Table .

These differences, within similar income groups and between groups, could be explained by the following. It is expected that countries more integrated into global supply chains and with greater business cycle synchronization are the most affected by trade collapse. We find that the existence of cross-country differences in capabilities is relevant since they determine countries’ comparative advantage [ 72 ]. In this context, high-income countries tend to be associated with exports of high-quality goods since they charge higher prices [ 73 ] and require larger amounts of skills to produce these goods [ 74 ]. Hence, the set of products exported between high-income countries may be related to goods with a higher degree of economic complexity, which exhibit a greater degree of resilience to economic shocks. This difference may also be explained by the higher demand for medical products by high-income countries, since the first COVID-19 wave starting in February 2020 reached this group of countries first.

We find a marginally significant positive effect for exports from low- and middle-income countries to high-income countries. This shows that countries belonging to this income group might have found an opportunity to supply the high-income countries’ markets, with whom they have regional trade agreements, these proving most affected by COVID-19 during the months in our sample. Low and middle-income countries are indeed progressively increasing their gains from trade because of greater exposure to globalization [ 75 ]; and this growth of trade flows from low and middle- income countries to high-income countries corroborates this evidence. Finally, not all kinds of products have been affected in the same magnitude. Evidence for outdoor goods can be found in [ 76 ]. Domestic consumption products, such as food, had a better performance during the pandemic, and low-income countries specializing in exporting these sorts of goods to high-income countries might have increased their exports to high -income countries.

Conclusions

In this study, we shed light on how the current COVID-19 crisis affects trade flows for the world economy during the first wave of the pandemic. We apply a PPML estimator with three sets of fixed effects in consistency with the recent literature on gravity models. Using monthly trade data for a sample of 68 countries, we find a negative impact of COVID-19 on trade flows that it is greater for countries with RTA. In addition, we also find a negative impact for a set of four indicators related to government responses against COVID-19, although a substantial variation in the impact on trade of the different measures is not observable. Furthermore, our results show that the COVID impacts on trade are only negative when income levels for exporter and importer country with regional trade agreements are identical, and in particular for high-income level countries.

These results pose important policy recommendations. The current trade shock induced by COVID-19 is still reshaping the world economy at the moment of writing these lines. However, current effects on trade can be considered as less detrimental than in the first wave from March to May 2020. The reason is contingent on countries’ capacity of adaptation to the different stages of the crisis. Countries may need to mitigate this trade shock by implementing public expenditure programs, as well as encouraging private investment. Such governmental actions may require further institutional initiatives, given the importance of the latter’s sizeable effects on trade flows (e.g., [ 47 ]). Nevertheless, countrywide attention has currently shifted towards vaccines, which may determine the future formulation of policies which concentrate vaccines on a small group of producers [ 77 ]. The transition to a non-COVID-19 context is expected to depend strongly on the vaccination efforts that are being undertaken by most countries. It is fundamental for countries to remain competitive throughout the course of the COVID-19 pandemic, simultaneously rebuilding wherever possible their trade relationships.

Finally, this manuscript presents certain limitations and avenues that must be taken into consideration for future research. First, the current study only offers a preliminary impact of COVID-19 on trade, as the shock is currently ongoing. The final magnitude of the shock may be assessed once it is over. Second, we only consider aggregate trade and the impact of COVID-19 on trade may depend on the sectoral comparative advantage of each country, as shown by the previous literature [ 23 ]. Hence, we may use trade data disaggregated by sectors, although we acknowledge that sectoral trade data availability is less forthcoming than its aggregate counterpart. Third, the existence of a subset of COVID-19 stringency indicators although highly correlated with the stringency index used in this article, may be capturing measures in very specific areas.

The analysis may also be extended to services trade flows in line with [ 25 ] approach. It would also be convenient to replicate these results for the subnational level. Despite recent efforts to estimate inter and intraregional trade flows in specific areas or territories, such as the European Union [ 78 ], this data is elaborated with a considerable time lag much larger than that for country data, and such analysis is not expected to be possible in the near future. Finally, it is necessary to acknowledge the importance of firms as international trade actors since there are substantial productivity differences across exporters. For this reason, it would be convenient to extend these findings to the firm level, as studied by [ 79 ] for Colombian firms.

Supporting information

S1 table. list of exporting countries..

High-income countries in bold.

https://doi.org/10.1371/journal.pone.0258356.s001

S2 Table. Main descriptive statistics.

https://doi.org/10.1371/journal.pone.0258356.s002

S3 Table. Results with one lag of COVID-19 government response indicators estimated by PPML, January 2019–October 2020.

Dependent variable is trade flows. Robust standard errors in parentheses, such as *** p<0.01, ** p<0.05, * p<0.1. All the specifications include exporter-month, importer-month and pair fixed effects.

https://doi.org/10.1371/journal.pone.0258356.s003

S4 Table. Robustness: Imports, M. Results by COVID-19 government response indicator estimated by PPML, January 2019–October 2020.

Robust standard errors in parentheses, such as *** p<0.01, ** p<0.05, * p<0.1. All the specifications include exporter-month, importer-month and pair fixed effects.

https://doi.org/10.1371/journal.pone.0258356.s004

S5 Table. One lag of COVID-19 government response indicator.

Results by income levels. Estimated by PPML, January 2019–October 2020. Dependent variable is exports. Robust standard errors in parentheses, such as *** p<0.01, ** p<0.05, * p<0.1. Each COVID-19 indicator is estimated on a different regression but paired in the same column for the sake of brevity. All the specifications include exporter-month, importer-month and pair fixed effects.

https://doi.org/10.1371/journal.pone.0258356.s005

S6 Table. Robustness: Imports, M. Results by income levels and COVID-19 indicators estimated by PPML, January 2019-October 2020.

Dependent variable is imports. Robust standard errors in parentheses, such as *** p<0.01, ** p<0.05, * p<0.1. Each COVID-19 indicator is estimated on a different regression but paired in the same column for the sake of brevity. All the specifications include exporter-month, importer-month and pair fixed effects.

https://doi.org/10.1371/journal.pone.0258356.s006

Acknowledgments

We thank two anonymous reviewers for their useful comments, which have contributed to improving the quality of the manuscript. Comments received from attendants of XXII Conference on International Economics and XXIII Applied Economics Meetings are also gratefully acknowledged. The views expressed are purely those of the authors and cannot under any circumstances be regarded as stating an official position on the part of the European Commission.

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The Global Economic Impacts of Covid-19

Photo: PHILIP FONG/AFP via Getty Images

Critical Questions by Stephanie Segal and Dylan Gerstel

Published March 10, 2020

Confirmed cases of the novel coronavirus (Covid-19), which first appeared in China at the end of last year, now exceed 115,000 as of March 10 and are likely to climb significantly higher. While over two-thirds of the total confirmed cases are in mainland China, the vast majority of new cases reported since February 25 have occurred outside the country. What was initially seen as a largely China-centric shock is now understood to be a global crisis. The virus’s spread has regrettably borne out analysts’ downside scenarios, with investors digesting the implications of disrupted supply chains, official containment measures, and spillovers from the real economy to financial markets. A decision by two of the world’s largest energy producers to maintain current levels of production, despite falling energy prices, has further unnerved investors while questions about governments’ abilities to mount an effective and coordinated response linger. The increased uncertainty has led to financial market volatility last seen during the global financial crisis.

Q1: What will be the impact of Covid-19 on the economy?

A1: The extent of the damage will depend on how quickly the virus is contained, the steps authorities take to contain it, and how much economic support governments are willing to deploy during the epidemic’s immediate impact and aftermath.

Early indications of Covid-19’s impact on the Chinese economy are worse than initially forecast. Surveys of China’s manufacturing and services sector plunged to record lows in February, automobile sales sank a record 80 percent , and China’s exports fell 17.2 percent in January and February. The official data confirmed a widespread slowdown in economic activity foreshadowed in low pollution levels and depressed shipping traffic , among other informal barometers. Analysts have sharply revised down estimates of Chinese growth, with many now predicting a drop in first quarter GDP, the first contraction since China began reporting quarterly data in 1992. As Covid-19 spreads, China’s economic recovery will be challenged as demand from other countries drops as they cope with the virus.

Although the outbreak appears to have slowed in China, Covid-19 and its impacts have gone global. Infections are mounting in Europe, South Korea, Iran, the United States, and elsewhere, with authorities implementing increasingly restrictive measures to contain the virus. Europe and Japan are likely already in recession territory given their weak fourth quarter performance and high reliance on trade. While the United States entered the crisis with a tailwind , some analysts are forecasting a contraction in U.S. GDP in the second quarter. Estimates of the global impact vary: early last week, the Organisation for Economic Co-operation and Development (OECD) predicted that Covid-19 will lower global GDP growth by one-half a percentage point for 2020 (from 2.9 to 2.4 percent); Bloomberg Economics warns that full-year GDP growth could fall to zero in a worst-case pandemic scenario.

Q2: What sectors and economies are most vulnerable?

A2: The Covid-19 outbreak has generated both demand and supply shocks reverberating across the global economy. Among major economies outside of China, the OECD forecasts the largest downward growth revisions in countries deeply interconnected to China, especially South Korea, Australia, and Japan. Major European economies will experience dislocations as the virus spreads and countries adopt restrictive responses that curb manufacturing activity at regional hubs, including in Northern Italy. As a result of depressed activity, the United Nations projects that foreign direct investment flows could fall between 5 and 15 percent to their lowest levels since the 2008-2009 global financial crisis.

At the sectoral level, tourism and travel-related industries will be among the hardest hit as authorities encourage “social distancing” and consumers stay indoors. The International Air Transport Association warns that Covid-19 could cost global air carriers between $63 billion and $113 billion in revenue in 2020, and the international film market could lose over $5 billion in lower box office sales. Similarly, shares of major hotel companies have plummeted in the last few weeks, and entertainment giants like Disney expect a significant blow to revenues. Restaurants, sporting events , and other services will also face significant disruption. Industries less reliant on high social interaction, such as agriculture, will be comparatively less vulnerable but will still face challenges as demand wavers.

Q3: What’s the relationship between the economy and the energy sector?

A3: Economic slowdowns generally lead to lower energy demand, and the fallout from Covid-19 has proved no different . Often, producers respond to demand slumps by cutting supply to buoy prices. Last week, members of the Organization of the Petroleum Exporting Countries (OPEC) and a few other major oil producers met to discuss an additional cut of 1.5 million barrels per day through the end of June in response to the outbreak. When the agreement collapsed, Saudi Arabia cut prices and lifted output , ostensibly to harm Russia for refusing to agree to production cuts . Following the Saudi decision, Brent Crude fell more than 20 percent , the sharpest one-day drop since 1991, with analysts predicting further declines ahead. The damage from the Saudi-Russian price war sends an unsettling signal to markets hungry for a coordinated policy response to the epidemic, especially considering Saudi Arabia’s current role as G20 president.

In response to the price shock, large oil producers, including U.S. firms, could pare back investment and production, with heavily indebted firms in particular at risk of layoffs, consolidations, and even bankruptcy . Investors are well aware that energy companies account for more than 11 percent of the U.S. high yield (below investment grade) market, with rollovers nearly impossible under current market conditions. In theory, lower oil prices should help oil-importing countries, but depressed activity due to Covid-19 could limit that benefit. In addition, the boom in domestic U.S. energy production in recent years means the United States is exposed to price declines in a way not seen in previous economic downturns.

Q4: How does the economic slowdown impact financial markets?

A4: Fears of a broader outbreak and its economic impact spread to financial markets last month, and most international indices are nearing bear market territory (declining at least 20 percent from the 52-week high) as investors process the lower corporate earnings that will result from the virus. The S&P 500 fell 7 percent to open the March 9 session, triggering a “ circuit breaker ” that briefly suspended trading for the first time since 1997. Overall, the index is down about 17 percent from its record high on February 19. Amid the equity rout, investors have fled to safe haven assets such as U.S. Treasury bonds, leading to record low yields . Low yields translate into low borrowing costs for the U.S. government, but low interest rates may not benefit private companies or individuals (or even all sovereigns) who may find financial markets too risk adverse to extend credit in light of such uncertainty. The longer the virus spreads, the more economic and company performance will be impacted, raising concerns about debt sustainability, especially for highly indebted countries and companies, absent official support.

Q5: How have governments responded to cushion the economic fallout from the epidemic?

A5: Thus far, national governments have announced largely uncoordinated, country-specific responses to the virus. In China, the epicenter of the outbreak, officials announced billions in special-purpose loans to companies facing liquidity constraints as well as financial support to specific sectors such as aviation. In the United States, the Federal Reserve cut the policy rate in an emergency action on March 3, and on March 9, in coordination with other U.S. bank regulators, it encouraged financial institutions to “meet the financial needs of customers and members affected by the coronavirus,” a move aimed at supporting financial conditions to prevent the growth shock from turning into a broader financial crisis. On March 9, the Federal Reserve Bank of New York also announced expanded overnight repurchase operations by $50 billion to avoid a deeper credit crunch.

The European Central Bank and Bank of England are expected to take action when their monetary policy committees meet later this month. On the fiscal front, President Trump previewed his administration’s plans to seek a payroll tax cut and assistance for impacted hourly workers and industries. Countries announcing fiscal measures just this month include Japan ($9.6 billion, or 0.19 percent of GDP), South Korea ($9.2 billion, 0.56 percent of GDP), and Italy ($4.1 billion, 0.20 percent of GDP). The adequacy of such spending will depend on the virus’s path as well as the effectiveness of other measures to contain negative spillovers from the growth shock.

In terms of coordinated action, on March 6, the G20 finance ministers and central bank governors pledged to take “appropriate” fiscal and monetary measures but made no specific commitments. On a March 3 phone call , G7 finance ministers reaffirmed their “commitment to use all policy tools” but did not outline specific steps. For their part, the International Monetary Fund and World Bank last week announced the availability of $50 billion and $12 billion in financing, respectively, to support low income and emerging market economies’ responses to the virus.

Scientists do not yet have a clear understanding of the virus’s behavior, transmission rate, and the full extent of contagion; uncertainty will be part of the backdrop for the foreseeable future. Coherent, coordinated, and credible policy responses provide the best chance at limiting the economic fallout from what is already and sadly a human tragedy.

Stephanie Segal is a senior fellow with the Simon Chair in Political Economy at the Center for Strategic and International Studies in Washington, D.C. Dylan Gerstel is a research assistant with the CSIS Simon Chair in Political Economy.

Critical Questions is produced by the Center for Strategic and International Studies (CSIS), a private, tax-exempt institution focusing on international public policy issues. Its research is nonpartisan and nonproprietary. CSIS does not take specific policy positions. Accordingly, all views, positions, and conclusions expressed in this publication should be understood to be solely those of the author(s).

Stephanie Segal

Dylan Gerstel

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The global macroeconomic impacts of COVID-19: Seven scenarios

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Warwick j. mckibbin and warwick j. mckibbin former expert - economic studies , center on regulation and markets , distinguished professor of economics & public policy - crawford school of public policy, the australian national university @warwickmckibbin roshen fernando roshen fernando phd candidate in economic policy - crawford school of public policy, australian national university @roshensfernando.

March 2, 2020

The outbreak of coronavirus named COVID-19 has disrupted the Chinese economy and is spreading globally. The evolution of the disease and its economic impact is highly uncertain which makes it difficult for policymakers to formulate an appropriate macroeconomic policy response. In order to better understand possible economic outcomes, this paper explores seven different scenarios of how COVID-19 might evolve in the coming year using a modelling technique developed by Lee and McKibbin (2003) and extended by McKibbin and Sidorenko (2006). It examines the impacts of different scenarios on macroeconomic outcomes and financial markets in a global hybrid DSGE/CGE general equilibrium model.

The scenarios in this paper demonstrate that even a contained outbreak could significantly impact the global economy in the short run. These scenarios demonstrate the scale of costs that might be avoided by greater investment in public health systems in all economies but particularly in less developed economies where health care systems are less developed and popultion density is high.

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Impact of COVID-19 pandemic on global economy

Ashwini katole.

Department of Community and Family Medicine, All India Institute of Medical Sciences (AIIMS), Raipur, Chhattisgarh, India

COVID-19 is an emerging global health concern. It is rapidly spreading all over the world. The transmission of COVID-19 is faster as compared to the SARS-CoV infection, and this has created a global emergency-like situation. To overcome the situation, many countries had locked down the states and sealed the country's borders. This resulted in reduced transmission of COVID-19 infection but deteriorated the world's economy. The lockdown effect can be very well seen in various sectors like education, industries, food, tourism, health, and jobs. Poor production levels, lack of connectivity, lack of import and export facilities, break in school education, and overstretched healthcare facilities placed an economic burden on the countries. This economic burden pulled back many countries in the crisis that will require several years to normalise. To overcome the situation, governments must take precautionary majors by focusing on healthcare facilities and providing financial support to reopen small businesses and industries.

Introduction

The coronavirus infection is a well-known pandemic in the world now. It has already affected 216 countries, and as of 8 July 2020, 11,635,939 confirmed cases and 5,39,026 confirmed deaths have been reported.[ 1 ] It started in Wuhan city, China and spread like wildfire all over the world.[ 2 ] On 7 January 2020, the Centre for Disease Control and Prevention named it Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and subsequently, the World Health Organization (WHO) named it as COVID-19.[ 3 ] On 30 January 2020, WHO declared COVID-19 infection as public health emergency and gave alarm to all the countries with vulnerable healthcare system. They also mentioned that transmission of the COVID-19 infection can be broken by social distancing, early diagnosis of cases, treatment, contact tracing and awareness.[ 4 ]

The infection of COVID-19 is spreading very fast as compared to the SARS-CoV, and governments are not able to stop the transmission. But efforts are being made like lockdown and social distancing measures, travel restrictions, border sealing, and quarantine to reduce transmission. All these measures ultimately reduced the regular functioning of the economy and created an economic crisis-like situation.[ 5 ] Lockdown led to a decrease in consumption and production levels. This resulted in the interruption of the global supply chain. In all systems, millions of people are losing their jobs, causing low wages, low opportunities, and low production that ultimately caused financial crises.

As per the United Nation Development Programmes, the current COVID-19 pandemic made the situation prolonged to achieve the Sustainable Development Goals.[ 6 ] It mentioned the impact of COVID-19 on the various field like:

• Poverty: 40-60 million people will be pushed into the extreme poverty line because of loss of jobs and daily wages.
• Gender inequality: Impact on the women work culture, as there is already gender inequality in terms of payment. Quarantine caused an increase in domestic violence dramatically.
• Education: 1.2 billion students are affected by school closures. Schools are closed to prevent the spread of COVID-19 infection among the students. Education was hampered because of the lockdown situation. It directly or indirectly affected the economy of the world.
• Social protection: 55% of people worldwide are not covered by social insurance. Those having social insurance are also not fully protected.
• Internally Displaced People (IDP): IDP are at health risk of COVID-19 infection because of lack of awareness, education, fragile situation and high population density. They are having a high socioeconomic impact of the COVID-19 pandemic.
• Slum dwellers: Over one billion people live in informal settlements or slum-like areas. These people are highly vulnerable to COVID-19 infection.
• Jobs: 1.6 billion workers have already lost their jobs because of the COVID-19 pandemic, causing a financial crunch in the population and poverty. It led to the economic crisis.
• Remittance: There is a 20% cutting of remittance because of economic burden.
• Commodities: There is a 20% fall in commodity prices during the COVID-19 pandemic.
• Food insecurity: 265 million people all over the globe will face food insecurity in low and middle-income countries.
• Tourism: Tourism is the biggest impacted field, with a loss of estimated 850 million to 1.1 billion tourists. This has resulted in an average revenue loss of $910 billion to $1.2 trillion.[ 6 ]

The impact of any epidemic or disaster can be measured by loss of income, financial loss because of illness, and case fatality rate. In the case of mortality, forgone income is estimated by the capitalised value of future lifetime earnings lost to the disease-related death, based on projected incomes for different age groups and age-specific survival rates.[ 7 ] The world is experiencing a drop in the global financial market because of the COVID-19 pandemic. As per the financial experts, this downfall is bigger than 2007–2008 financial crisis.[ 8 , 9 ] The share prices of various companies of the United States are down by 20%. The market price of the Nikkei has also experienced volatility of the share prices because of the COVID-19 pandemic.[ 8 ] The impact of COVID-19 is huge; nobody knows when this will end and the routine life starts. We will require robust policies and some time to overcome this crisis.

Impact of COVID-19 pandemic on industries

The impact of the COVID-19 pandemic is dramatic on the industrial sector as industries require material and manpower for production. In the COVID-19 pandemic, the transmission is human to human. So, the government in many countries enforced strict social distancing and a complete lockdown-like situation that resulted in loss of production days and loss of production. Because of this, many workers who had previously migrated for the work purpose wanted to go back to their native places. This resulted in anxiety among the employees and owners, leading to low production, shutdown and financial crisis.

In the British Plastic Federation survey, about 80% of companies dropped their turnover for the next 6 months. Out of that, 98% of staff expressed concern for the COVID-19 pandemic, while 90% expected the impact of coronavirus on the supply chain, the production rate and the manpower.[ 10 ] The chemical industries are facing the worst phase. The global production rate for chemical industries fell by 1.2%.[ 11 ] The impact on the industrial sector is huge in terms of economic burden. It will take a couple of years to overcome this situation.

Impact of COVID-19 pandemic on food sector

The food supply chain is involved with manufacturing, packing, distribution and storage of food items. It should be balanced with demand and supply of the consumers.[ 12 ] One of the important sectors of day-to-day life is the food sector. Food supply chain should be smooth for the continuous supply of the required items. Food chain includes demand for food items, purchasing patterns and continuous supply. During the COVID-19 pandemic, the flow of food supply was interrupted because of the lockdown, resulting in unessential storage of food items and panic buying from consumers. This led to a food crisis. To overcome these problems, many countries are adopting various techniques. Canadian Food Inspection Agency has made a number of temporary changes to the regulations surrounding labelling and packaging of food.[ 13 , 14 ] Likewise, the distribution of food items should be equitable. Unnecessary storage at the production and distribution level is avoided so that artificial crisis-like situation can be managed and the cost of essential food items can be regulated.

Impact of COVID-19 pandemic on Education sector

COVID-19 had a huge impact on the education sector. Students are bound to be at home to break the transmission of infection. More than 100 countries have closed down the educational institutes. It directly affected 900 million learners all over the world.[ 15 ] In low- and middle-income countries, many programmes are going on in the school, like mid-day meal programmes and health surveys which are free of cost. Due to the closure of schools, these programmes lag, resulting in hunger. It had a significant impact on the family income and expenditure.[ 16 ]

As per the study by the Brookings Institute, the closure of major schools in US cities because of lockdown. Cost per student allows any regional breakdown of interest. Cost per student per week of closure is $142. Four weeks of lockdown in New York City led to the loss of 1.1 billion of the economy, and 12 weeks cost about 1% of the Gross Domestic Product (GDP). This is a huge economic loss for the country. The lockdown negatively affected students and healthcare workers, costing 3% of GDP.[ 17 ] To overcome this problem, online teaching classes have started. But, this is leading to internet addiction among the students. This also increases the expenditure on electronic appliances that may not be affordable to all individuals in this time of economic crisis.[ 18 ]

Impact of COVID-19 pandemic on health sector

This pandemic made people aware of the health systems in their country. Every country is trying to make a balance between the infected population and the capacity of hospitals. The existing infrastructure of healthcare sector is already overstretched. The ratio of doctor to patient is highly imbalanced. The diagnostic lab facilities are also inadequate in low- and middle-income countries. The provision of treatment costs in private hospitals is very high and unaffordable for a common person.

At an alarming rate, every country is trying to increase the present infrastructure, resources, medical staff and the equipment to handle the patient's load. Many countries started massive investments in the infrastructure of hospitals and equipment to reduce morbidity and mortality. It accelerated the digital transformation of the health sector. The COVID-19 pandemic disaster taught everyone that there are immense opportunities in the health sectors, and government needs to strengthen them. The economic budget allocation has to increase to deal with the emergencies. There is also a need to focus on the preventive and promotive aspects of the diseases that can reduce the infection and its transmission.

This pandemic altered the preparedness for emergencies and disaster. For this, new programmes and policies are required to deal better with and overcome the situation.[ 19 ] In the United States, active pharmaceutical ingredients are imported largely from India (18%) and the EU (26%), while China gives 13%.[ 20 ]

Impact of COVID-19 pandemic on tourism

National and international tourism are the most affected sectors due to the COVID-19 pandemic. This situation will continue for a couple of years due to fear of pandemic in the people. International, regional and local travel restrictions immediately affected national economies, including tourism. It led to cancellations of conferences, sports events, festivals and other meetings. The World Travel and Tourism Council estimated that around 90 million jobs in tourism are at risk because of the COVID-19 pandemic.[ 21 ] Tourism is the channel through which a huge amount of liquidity flows from one country to another. This is a source of income for many in European countries.[ 22 ]

Impact of COVID-19 pandemic on Jobs social protection

Public health is a priority for all governments during this pandemic. Borders are closed in more than 80 countries. Various majors have been taken to control the spread of the infection such as the closure of public platforms, restaurants, transportation, sealing borders of states and countries and closure of schools for more than 1.5 billion children. It significantly declined economic activities, leading to decreased economic output and unemployment. Closure of business for long periods erodes the employee's skills and causes permanent harm to future jobs. Small and medium-sized businesses are the heart of the economy. Closure of business for a long time will cause a slow recovery of the economy.[ 22 , 23 ] On 8 May 2020, the Bureau of Labor Statistics reported that 20 million Americans lost their jobs in April 2020, pushing unemployment to 23 million in America out of 158 civilian labour force. This led to an unemployment rate of 14.7%, higher than the 1930s unemployment depression.[ 24 ]

As per the Bureau of Economic Analysis, United States GDP has fallen by 5.0% in the first quarter of 2020. It is a more significant fall in GDP than the 2008 financial crisis. The fall in GDP is a reflection of negative contributions from Personal Consumption Expenditure, private inventory investment, imports, and federal government spending.[ 24 ]

The COVID-19 pandemic has hugely affected the European countries. During the COVID-19 pandemic, the European economy contracted by 3.8% annually. It is the most significant economic decline since 1995 (since the series began). The industrial production has decreased across the European countries by 17%. It is estimated that in 2020 there will be an 8–12% decline in the European economy due to the COVID-19 pandemic.[ 25 , 26 ] The ongoing public health problem leads to unemployment and major economic crises. A similar situation is also seen in Asian countries.

The loss of daily wages and unemployment is the leading cause of poor socioeconomic growth. This will push the major population below the poverty line. It is going to change the poverty scenario all over the world.

The impact of the COVID-19 pandemic is enormous all over the world. It reverted the flow of the economy. It has affected all sectors including health, finance, industries, agriculture, production, import and export. The shutdown of the markets and industries, complete lockdown to control COVID-19 spread, and sealing borders of states and countries caused many to lose jobs, leading to poverty in many countries.

To overcome the situation, the strong policies by the government should be proposed for the general population. The government should provide support through liquidity by non-bank financial systems. Reopening small-scale industries and businesses with all precautions is important. A loan system with low interest rates should be provided to developers and industrialists. Once the small-scale businesses and industries regulate the economy, it will help the country to overcome the financial setback. Public–private partnership is another priority area that needs to be strengthened.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

Global Commodity Chains and the Pandemic

Labor-power in agricultural sectors in kenya and chile.

• Lara M. Espeter Technische Universität Berlin
• Patricia Retamal University of Chile, Chile

The availability of labor-power is a critical element of all commodity chains. This is especially true of labor-intensive production processes such as agriculture. The COVID-19 pandemic had a major impact on this, as well as on many other aspects of the economy and everyday life. The institutions of the modern world-system responded in various ways to the new situation influenced by COVID-19, taking measures to mitigate and avert the detrimental effects. This paper examines these responses and their impact on the availability of labor-power in the agricultural areas of Nakuru County, Kenya, and O’Higgins Region, Chile. By practically applying world-systems analysis, we shed light on the significance of institutions during periods of stagnation and their impact on the availability of labor-power in global commodity chains. This allows us to draw conclusions about the general impact of institutional responses to stagnation phases at the worker level. We show that the institutions studied responded in very different ways to the stagnation phase affected by COVID-19. As a result, O’Higgins Region experienced a labor-power shortage that Nakuru County had not, which may have a lasting impact on labor-power availability.

Author Biographies

Lara m. espeter, technische universität berlin.

Lara M. Espeter is a research associate in the project Apples and Flowers. Effects of Pandemics on the (Re-)Organization of Commodity Chains for Fresh Agricultural Products and an associate member at the Collaborate Research Centre Re-Figuration of Spaces (CRC 1265) at Technische Universität Berlin. Her research focuses on the origins and effects of social inequality by looking at current and historical structures of the world-economy.

Patricia Retamal, University of Chile, Chile

Patricia Retamal is a PhD candidate in the Territory, Space and Society program at the University of Chile and a thesis student in the regular Fondecyt program (N° 1210331) Extractive citizenships? Citizen practices in rural territories . She is currently the Gender Coordinator of the Vice-Rectory of Research and Development at the University of Chile. Her research focuses on the effects of agribusiness on the social reproduction of women in the workforce.

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Funding data

• Deutsche Forschungsgemeinschaft,Deutsche Forschungsgemeinschaft Grant numbers BA 3488/10-1
• Fondo Nacional de Desarrollo Científico, Tecnológico y de Innovación Tecnológica Grant numbers 1210331

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COVID-19 outbreak: Impact on global economy

Affiliations.

• 1 School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China.
• 2 Department of Management Sciences, Kinnaird College for Women, Lahore, Pakistan.
• 3 Department of Obstetrics and Gynecology, Nishtar Hospital, Multan, Pakistan.
• 4 Riphah School of Business and Management, Riphah International University Lahore, Lahore, Pakistan.
• 5 Department of Finance, Babes-Bolyai University, Cluj-Napoca, Romania.
• PMID: 36793360
• PMCID: PMC9923118
• DOI: 10.3389/fpubh.2022.1009393

COVID-19 has been considered the most significant threat since World War II and the greatest global health disaster of the century. Wuhan City, Hubei Province, China, reported a new infection affecting residents in December 2019. The Coronavirus Disease 2019 (COVID-19) has been named by the World Health Organization (WHO). Across the globe, it is spreading rapidly, posing significant health, economic, and social challenges for everyone. The content of this paper is solely intended to provide a visual overview of COVID-19 global economic impact. The Coronavirus outbreak is causing a global economic collapse. Most countries have implemented full or partial lockdown measures to slow the spread of disease. The lockdown has slowed global economic activity substantially, many companies have reduced operations or closed down, and people are losing their jobs at an increasing rate. Service providers are also affected, in addition to manufacturers, agriculture, the food industry, a decline in education, the sports industry, and of entertainment sector also observed. The world trade situation is expected to deteriorate substantially this year.

Keywords: COVID-19; global economy; mitigation strategies; pandemic; sectors.

Copyright © 2023 Naseer, Khalid, Parveen, Abbass, Song and Achim.

Publication types

• Research Support, Non-U.S. Gov't
• COVID-19* / epidemiology
• Communicable Disease Control
• Disease Outbreaks

Grants and funding

REVIEW article

Covid-19 outbreak: impact on global economy.

• 1 School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China
• 2 Department of Management Sciences, Kinnaird College for Women, Lahore, Pakistan
• 3 Department of Obstetrics and Gynecology, Nishtar Hospital, Multan, Pakistan
• 4 Riphah School of Business and Management, Riphah International University Lahore, Lahore, Pakistan
• 5 Department of Finance, Babes-Bolyai University, Cluj-Napoca, Romania

COVID-19 has been considered the most significant threat since World War II and the greatest global health disaster of the century. Wuhan City, Hubei Province, China, reported a new infection affecting residents in December 2019. The Coronavirus Disease 2019 (COVID-19) has been named by the World Health Organization (WHO). Across the globe, it is spreading rapidly, posing significant health, economic, and social challenges for everyone. The content of this paper is solely intended to provide a visual overview of COVID-19 global economic impact. The Coronavirus outbreak is causing a global economic collapse. Most countries have implemented full or partial lockdown measures to slow the spread of disease. The lockdown has slowed global economic activity substantially, many companies have reduced operations or closed down, and people are losing their jobs at an increasing rate. Service providers are also affected, in addition to manufacturers, agriculture, the food industry, a decline in education, the sports industry, and of entertainment sector also observed. The world trade situation is expected to deteriorate substantially this year.

1. Introduction

Wuhan, China, reported 27 cases of novel pneumonia on December 31, 2019. The cause and origin of the novel pneumonia were unknown. Wuhan city, Hubei province, is a trendy industrial hub in central China, having a population of more than 11 million ( 1 ). Novel pneumonia patients experienced fever, dyspnea, and lung infiltrates on imaging and dry cough. The spreading of new unknown pneumonia related to Wholesale Seafood Market Wuhan, famous for trading bats, crocodiles, dogs, pigs, fish, snakes, marmots, and wild animal species poultry animals ( 2 ). A swab of the throat, taken on January 7, 2020, was analyzed by the Chinese Center for Disease Control and Prevention (CCDC) and was identified as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). This disease is classified as COVID-19 ( 3 ). COVID-19 was designated a worldwide health emergency on January 30, 2020, with poor health systems most in danger.

The WHO didn't name the Chinese outbreak a pandemic, but it did call it a public health emergency. Amid the uncertain situation in Wuhan due to Cov-19, international trade and supply chains have been suspended, asset prices have been upended, and the decision-making process for multinational companies is challenging due to limited information. Central China's financial hub has been identified as Wuhan. Several of the world's 500 best companies are headquartered in Wuhan, including SAP, Microsoft, and Group PSA ( 4 ). In addition to being the country's most prominent steel and car producer, it is also a major trade and transportation hub. Economic development has outpaced China's national growth in recent years, with its GDP growing by 7.8% in 2019, rather than the national average of 6.1% ( 5 ). In response to the spread of this novel virus, business activities temporarily ceased, and numerous international firms evacuated their workers. The strict travel restrictions in Wuhan and different urban communities in Hubei are expected to harm global trade in China. Tourism, retail, and hospitality will all be negatively affected ( 6 ). More than 70 thousand cinema was closed in China ( 7 ); several airlines canceled flights from and to China, disrupting tourism and other activities. It is already beyond the Hubei borders that the novel virus is harming the economy. This influence severely causes stock markets to crash ( 8 – 10 ). The lockdown of Hubei province, with a population slightly smaller than the U.K. and France, threatened to hit the global economy was very exciting. Due to China's complete integration, after the United States, China is the second-largest economy, then the rest of the world. Global economic losses of $40 billion were estimated as a result of the SARS epidemic in China from 2002 to 2003 ( 11 ).

Currently, China's economic scale is 8–9 times higher than the epidemic of SARS. Financial experts worldwide say that the impact of COVID-19 on the world economy will be considerably vast in 2019. In 2019, China was estimated to contribute 39% to the global economy by the International Monetary Fund. Currently, China contributes 16.3% to the worldwide economy ( 12 ). It is suggested that the global economy was go-down if the nation's economy drops ( 13 ). As shown in Figure 1 , the Chinese economy has grown considerably from 2003 to 2019, contributing to the global economy.

Figure 1 . GDP growth in China and China's share of the global economy. Source: World Bank, OECD.

Due to the Lunar New Year holiday's extension, a sharp drop was seen in market value when China resumed its important businesses and industries on Monday, February 3, 2020. There was an 8.4% decline in the market value of the Shenzhen Composite, and a 7.7% decline was experienced by the benchmark Shanghai Composite, which is approximately a $375 billion decline in market value since August 2015; it is the sharpest 1-day decline in market value. The decline was also experienced in other businesses, such as transportation stocks, consumer services, and retail companies ( 14 ).

The 11 provinces of China, under strict control then, produced two-thirds of all the auto, the country's vehicles. Chinese auto parts are manufactured in these provinces, especially Hubei, for the South Korean market, Europe, and the United States. The experts believe there could be about 350 000 unit's production loss that might be possible if the production companies and manufacturing industries remain inactive until February 10, 2020. If the outbreak continues till the mid of March, this loss may be reached over 1.7 million, and it is expected that this decline could be reached up to 32.3% ( 15 ). Similarly, Standard & Poor's (S & P) believes that auto parts production in the United States may decline by 50%. Some automakers have expressed concerns about the shortage of auto parts. A lack of features in China has forced the Hyundai Motor Company to close its domestic factories. Automakers in the United States and Europe are similarly concerned that a shortage of spare parts could disrupt their business ( 16 ). Several industry analysts have forecast that China's automotive market will shrink. If the coronavirus outbreak continues into the second quarter, it will reach 3–5% in 2020.

According to the United Nations Tourism Organization (UNWTO) ( 17 ), China was a significant tourist source market during this outbreak and a major tourist destination. In 2019, approximately 6.3 million Chinese tourists traveled abroad for Lunar New Year, generating about US$73 billion in revenue. However, this number declined significantly in 2020 ( 18 ). According to reliable sources, this also substantially impacts tourism outside of China. Vietnamese tourism is expected to lose up to $ 7.7 billion in the first quarter due to cancellations of Chinese groups and the general economic downturn. In 2020, there will be an 80% decrease in Chinese tourists visiting Thailand, reducing Thailand's revenue by the U.S. $ 3.1 billion ( 19 ). Indonesia, Singapore, South Korea, Malaysia, Cambodia, Hong Kong, Japan, Australia, and other nations will be affected, in addition to Vietnam and Thailand ( 20 ).

The economic consequences of this outbreak are enormous in China and even around the world, considering the loss of trade, and tourism, rise in unemployment, industry recession, a decline in sustainability and quality of life, the decline in the education sector, and the effect on the agriculture industry, impact on the food industry, the fall of the sports industry, deterioration of the entertainment sector, and aspects of the global supply chain. In the first quarter, Bloomberg Market Diagnosis predicts China's GDP growth will fall by 4.5%. Global GDP is expected to decline by about 0.42% in the first quarter due to the outbreak, as shown in Figure 2 ( 21 ).

Figure 2 . Growth deviation in percentage points from Q1 2020. Source: Bloomberg economics.

2. Methodology

The research mainly included reviewing previous studies and determining COVID-19's global economy hit. According to experts, the virus disrupted global supply chains and closed industries. This interruption impacts worldwide demand and production. The research methods included a review of previous studies, determining global recession due to the natural disaster, and determining vital economic indicators, such as the rise in unemployment, the industry hit hard by the recession, the slump in manufacturing activity, a bad year for trade, the global economy shrinkage in 2020, the decline in Sustainability and Quality of Life, the decline in Education Sector, resultant Effects on the Agriculture Industry, and reduction in the entertainment sector. In addition, as explained in Figure 3 , the search approach was used for this research topic. In this study, many research databases were consulted to search for relevant papers and download them from the database (Google Scholar, Scopus Index Journals, Emerald, Elsevier Science Direct, Springer, and Web of Science). Our primary emphasis was on items published in academic publications, including research articles, feedback pieces, brief remarks, discussions, BBC news, world bank source, and review articles. The reports were utilized to search for numerous keywords, such as “impact of COVID-19 internationally,” “impact of COVID-19 on a macro level,” and “spillover effects of COVID-19 on micro-level globally,” etc.; in summary, a keyword list and the complete text have been created. In the beginning, searching for keywords produced a significant volume of published material.

Figure 3 . Methodology search for finalized articles for investigations. Source: Author's constructed.

Since 2020, it has been challenging to assess all papers; the literary display has constraints. The research searched 92 articles in the database mentioned above. It removed 39 irrelevant publications since they were copied from a previous search. The framework consists of two things:

(i) Articles focused on “Global COVID-19 Impacts, impact on the global economy, and sustainable mitigation strategies.”

(ii) Search phrases linked to study requirements.

Our search produced 60 articles. We scan “Web of Science and Google Scholars” to improve search results and verify cited papers. This study analyses 60 papers' research subjects, techniques, settings, and theories explained in Figure 4 . This study explores connected domains to provide future research prospects.

Figure 4 . Framework of the study. Source: Author's constructed.

3. The global economy hit by coronavirus pandemic

Johns Hopkins University data shows that as a result of the coronavirus, 270 million people have been infected, and 190,000 have died. Several countries and cities worldwide have prevented the virus from spreading further. Some measures include closing borders, shutting down schools and workplaces, and prohibiting huge gatherings. Global economic activity was halted by the “Great Lockdown” due to the global financial crisis, harming businesses and causing job losses. “We are facing a real global crisis since no country is immune,” IMF chief economist Gita Gopinath wrote earlier this month ( 22 ). Some facts and graphs illustrate how the Coronavirus epidemic has affected the global economy.

3.1. The rise in unemployment

In some economies, the number of unemployed has increased due to global lockdown measures, as shown in Figure 5 .

Figure 5 . Coronavirus padamic hit jobs. Source: Bureau of Labor Statistics of the U.S.; National Bureau of Statistics of China, Deutsche Bundesbank, Australian Bureau of Statistics, Statistics Korea, Refinitiv.

In the U.S., the world's biggest economy, almost 26 million jobs have been lost in the previous 5 weeks ( 23 ). Unemployment in March topped 4.4%, the highest level since August 2017. It is not limited to the United States. There has also been increased unemployment in Australia and South Korea, and some economists believe this trend will continue.

3.2. The industry hit hard by the recession

Numerous significant economies, notably the United States and China, depending on the service industry for employment and economic development. During the Pandemic, many stores closed, and customers stayed home due to a lockdown imposed by both countries. Amazon and some other retailers reported an increase in online sales, but this did not stop the decline ( 24 ). Despite lifting the locking measures, economists warn that consumers may not regain their consumption. Even after China allowed companies to reopen gradually, analysts at the Oxford Economic Research Institute observed that China's retail industry did not improve rapidly, as illustrated in Figure 6 .

Figure 6 . Plunge in retail sales. Source: U.S. Census Bureau, National Bureau of Statistics of China, Refinitiv.

Reports indicate that consumer spending will not return immediately after restrictions are lifted due to the slow improvement in household spending. According to IHS Market, the global service industry, transportation, real estate, and business activities in the travel and tourism industries show significant decline ( Figure 7 ).

Figure 7 . Services activity in major economies. Source: IHS Market, Caixin, au Jibun Bank, Refinitiv.

Growing economies tend to create more wealth and jobs. It is measured as the change in the overall value of goods and services produced over 3 or 12 months. In 2020, the IMF predicted a 4.4% contraction in the global economy. According to analysts, this will be worse than the Great Depression. Among the major economies, China was the only one to grow in 2020. Its growth rate was 2.3%. According to the IMF, global growth is expected to be 5.2% in 2021. India and China are expected to lead the way with growth of 8.8 and 8.2%, respectively. The recovery is expected to be slow in the U.K. and Italy, two big, services-reliant economies that have been hit hard by the outbreak ( Figure 8 ).

Figure 8 . Majority of countries in recession. Source: IMF.

3.3. The slump in manufacturing activity

Manufacturers have been impacted by the trade war between the U.S. and China for the past two years and have been under pressure again while the coronavirus spreads worldwide (see Figure 9 ).

Figure 9 . Manufacturing in significant economies. Source: IHS Market, Caixin, au Jibun Bank, Refinitiv.

Due to the COVID-19 epidemic, non-Chinese firms that received their raw materials or components from Asian vendors (sometimes known as “intermediate products”) were impacted. However, due to the authorities' efforts to contain the virus, the operation of the Chinese plant was suspended for longer than expected. Lockdown measures have hit more and more manufacturing companies as more and more countries implement them. A few are forced to close temporarily, while others are restricted from accessing intermediate materials and goods. In addition, a reduction in commodity demand has made manufacturing more challenging. As shown in Figure 10 , the production of U.S. factories in Europe and Asia declined in the past month.

Figure 10 . Coronavirus impact on factory output. Source: U.S. Federal Reserve, National Bureau of Statistics of China, Refinitiv.

3.4. A lousy year for trade

In 2020, it was expected that global trade would slow even more than it has in 2019. An anticipated decline in merchandise trade is shown below (see Figure 11 ).

Figure 11 . Decline in merchandise trade. Source: World Trade Organization forecast.

The World Trade Organization forecasts that global trade could decline by 12.9 or 31.9% this year, depending on how global economic conditions develop ( 25 ). According to the WTO, imports and exports in all regions will decline by double-digits in 2020.

3.5. The global economy shrinkage in 2020

Numerous institutions have drastically reduced their global economic forecasts due to the Coronavirus pandemic. Global economic contraction is expected to reach 3% this year, according to the International Monetary Fund ( 26 ). Figure 12 shows only a few economies (including China and India) will grow in 2020.

Figure 12 . Economic forecasts (2020). Source: IMF World Economic Outlook.

Although the economy is expected to bounce back next year, the recovery will only be a part of the reason because the level of activity will be lower than in 2021 before the virus hit” ( 27 ). In 2020 and 2021, the pandemic crisis may cause the global economy to lose nearly $9 trillion, more than Japan and Germany combined.” wrote the organization's chief economist Gopinath.

3.6. The decline in sustainability and quality of life

Politics, ecology, and the economy influence sustainable development ( 28 ). These factors also dictate how people should live so future generations may experience the same quality of life. According to Garfin et al. ( 29 ), tiny changes in human existence steadily impact the future. The future generations will not recognize the changes in their lives as a response to change; they will assume that individuals before them had comparable lives. The COVID-19 Pandemic has altered human existence's political, environmental, and economic elements, which affect psychological growth and sustainability. This impacts people's living standards and quality of life. The COVID-19 era resulted in social problems and international crises in the early 2020's ( 30 ). According to Arden and Chilcot ( 31 ), progress and development have halted, and the epidemic has disrupted the financial stability of industrialized and developing nations. COVID-19 measures such as social isolation and locking up reduce the quality of life because of the resulting stress and sadness ( 32 ).

Most people are losing employment and money worldwide ( 33 ). Profit margins and sales have fallen. Many nations have created methods to rescue their economies and populations' mental health from the epidemic, although regaining economic stability will take years. COVID-19 and related crises have harmed mental health, particularly among job losers. Employees' mental health is damaged by their unpleasant, contentious relationship with the company. The international economy has stopped, and millions have perished as COVID-19 caused unemployment, illness, and despair. The coronavirus triggered a large-scale psychological experiment that would impact life. Knowing that the Pandemic harms mental health reduces life quality and motivates prevention. Sustainable management improves worker wellbeing, according to Topa et al. ( 34 ). Anxiety, stress, and exhaustion affect behavior ( 35 ). Lockdown endangers lives; on the other hand, PTSD, hunger, and mental issues are rising. COVID-19 kills people.

3.7. The decline in education sector

Coronavirus outbreak affects global schooling. Due to the coronavirus outbreak, several educational institutions closed worldwide (“Impact of Coronavirus Pandemic on Education,” 2020). According to Education UNESCO ( 36 ), over half of the world's students were affected by Worldwide closures observed by the United Nations Educational, Scientific, and Cultural Organization (UNESCO). This event severely disrupted many students' academic and professional ambitions; consequently, coronavirus damaged over $600 billion in businesses. In 2020, educators and students throughout the globe felt the rippling impact of the coronavirus as institutions and universities were urged to close.

On the whole, 44 countries on four continents had school cancellations, affecting hundreds of millions of pupils. Schools without an online learning platform were disproportionately affected. Moody's lowered the U.S. higher education outlook from stable to “negative” because 30% of U.S. colleges and universities already had inadequate operational performance, and it was difficult for them to react to the financial and academic reforms needed to deal with the coronavirus pandemic ( 37 ). As the coronavirus pandemic developed seriously in Italy, France, and Spain, several U.S. universities urged study-abroad students to return home. However, the epidemic encouraged online and remote learning, while only a minuscule proportion of the world's education is taught online ( 38 ). COVID's worldwide influence was considerable, according to UNESCO data showed that by 2021. The COVID pandemic impacted around a 10.5 million learners worldwide, or 0.7% of all registered students, yet there was only one country-wide learning shutdown by 2021 ( 39 ). In the first quarter of 2022, 6 country-wide closures and 43.5 million impacted students, or 2.8% of all registered students. This caused students to have more debt, take longer to graduate, and break their academic dreams during COVID-19 from 2020 to 2022 ( 40 ).

3.8. Resultant effects on the agriculture industry

Due to the COVID-19 epidemic, the resiliency of the agriculture industry has been put to the test. The cost of agricultural goods has decreased by 20% due to a worldwide slump in demand from hotels and restaurants. Nations have implemented several preventative measures over the globe to halt the rapidly escalating spread of the disease ( 41 ). This involves separating oneself from their social circle, minimizing the amount of needless travel one does, and not attending any churches. If people are urged to self-isolate after contacting suspected virus carriers, the number of available inspectors and delivery workers may be affected ( 42 ). This will have significant repercussions for perishable products, such as food that is high in fat and protein, such as meat and vegetables. In addition, markets have taken matters into their own hands by prohibiting floor trading, which has harmed the capability of commodities exchange ( 43 ). One example from more recent times is the Chicago Mercantile Exchange. “Panic purchasing” complicates shortages beyond what's available in supermarkets. Concern has been voiced by the American Veterinary Medical Association (AVMA) over the inadequate supply of animal medications made available by several prominent drug suppliers.

3.9. The decline in travel and tourism

The hotel business has also suffered from COVID-19's travel ban. Community lockdowns, social isolation, stay-at-home orders, and travel and mobility limitations have led to the temporary closure of numerous hospitality enterprises and diminished demand for those permitted to continue operating ( 44 ). The coronavirus has affected hospitality the most. Hourly employees in the hospitality and tourism industries faced severe difficulties due to the economic downturn. Marriott International, which has around 174,000 employees, furloughed tens of thousands of those people. In addition, Hilton Worldwide informed its lenders on March 5, 2020, that it would be borrowing a precautionary $1.75 billion as part of a revolving loan ( 45 ). This move was made to save money and to keep the company's flexibility” in light of the volatility in the global markets. The U.S. hotel industry's income per available room declined by 11.6% on March 7, 2020, while China's occupancy rates plunged 89% by January 2020 ( 44 ). Other U.S. hotel corporations want $150 billion in direct employee help, owing to a drop in demand and a $1.5 billion loss since mid-February ( 46 ). Since March 1, 2020, German hotel occupancy has dropped 36%. Rome's occupancy rate is 6%, whereas London's is 47% ( 47 ). The COVID-19 crisis has caused worldwide distortions in the hospitality sector and the European hotel market slumps.

3.10. Effects on the food industry

Due to consumer purchasing during COVID-19, the food industry has been under much pressure, particularly in food delivery and retailing. The food supply chain affected farmers, distributors, consumers, and labor-intensive food-processing businesses. Many factories restricted, halted, or temporarily ceased production owing to COVID-19 employees who were unwilling to go to work, assuming they would become sick at work, mainly in meat-processing food firms during the epidemic ( 48 ). Because of this, there was a growing fear that there may be a scarcity of food, and panic purchasing has led to an increase of £1 billion worth of food stored in households throughout the United Kingdom ( 49 ). The rising demand for food goods has also impacted online meal delivery. In addition, food banks have been affected by people shopping in a panic and storing food in response to the decline in contributions ( 50 ). Despite government assurances, businesses have made substantial adjustments, including limiting the number of products consumers might purchase, creating more than 30,000 new jobs to refill shelves, and introducing special shopping hours for the elderly, vulnerable populations, and NHS goods ( 51 ).

3.11. The decline of the sports industry

COVID-19 influenced the scheduling of athletic events, including some of the biggest competitions in the sport's history. The highly anticipated football event, Euro, was pushed back a year, and the playoffs won't occur until at least June of the following year ( 52 ). The athletes and their nations agreed to postpone the games until 2021. Many tournaments, including the British Open, were canceled or moved. Pandemic cancellations cost billions ( 53 ). Tokyo's Olympics and Paralympics were postponed. The 2020 English hockey games were postponed. MLR's 2020 season was canceled, and all MLB season games in Mexico and Puerto Rico were canceled ( 54 ).

Due to the Portuguese government's declaration of an emergency, all sports activities will be postponed until further notice. The World Snooker Championship scheduled to take place in Sheffield has been delayed ( 55 ). The 2020 European Aquatics Championship in Hungary was postponed until August. Sponsors and organizers of the aborted games lost billions. In 2021 and 2022, numerous nations restarted sports, but the number of participants at sports arenas was limited, and face masks were required.

3.12. The decline of the entertainment sector

Pandemic affected cinema, theater, and entertainment. The 2020 coronavirus pandemic cost the entertainment industry $5 billion in losses, and several Hollywood films delayed their release. Consequently, most of the 120,000 below-the-line entertainment jobs lost were stage roles. The shutdown lost 150,000 members and 120,000 jobs, and the IATSE requested the government to include the entertainment industry in its stimulus package ( 56 ). From February 23 to March 1, 2020, the COVID-19 epidemic in Italy cost millions of euros every week. Film screenings, theaters, live music, dancing, and exhibits lose money (Khan et al., 2022). Collectively, unemployment in the entertainment business surged to new highs, but it was uncertain whether it would receive government stimulus funding. Some senators contended that the entertainment business contributes less to the economy than the finance and industrial sectors ( 57 ). Entertainment activities resumed in 2021 and 2022, but the industry's performance didn't approach pre-COVID levels due to weak economic recovery. 221,762,724 and 498,162,785 tickets were sold in 2020 and 2021, compared to 1,314,169,169 and 1,225,910,803 in 2018 and 2019. COVID reduced the number of moviegoers by 83%. In 2020 and 2021, box office revenue was $2,033,566,047 and 4,568,154,099. In 2018 and 2019, it was $11,972,083,658 and 11,229,345,558. Film revenue plummeted by 83.1% under COVID over pre-COVID ( 58 ).

4. Challenges and mitigation strategies

The suggested study approach advises concentrating on economic policies that have extended the medical crisis and combining monetary, industrial, and other sectors to settle worldwide and financial market issues. Figure 11 illustrates the study's structure and topics.

4.1. Policy challenges

Economic analysts have used aggressive strategies to handle short-term irregularities in nations that can endure the outbreak without fabrications. The rapid evolution of the global health catastrophe into international corporate trade and economic calamity has left experts throughout the globe stunned. As the epidemic's fiscal effect develops, analysts concentrate more on immediate monetary repercussions than longer-term factors like debt accretion. Due to the poor elasticity of budgetary and financial assistance under conservative criteria, many strategists have limited their ability to react to the present crisis, given the recent drop in global economic growth, mainly production and trade, since the flu pandemic began influencing international policymaking. Initially, the Pandemic's fiscal effect was anticipated to be a temporary supply of items as employees self-quarantined via social interaction to reduce the outbreak's breadth. As corporations store cash, these growing financial impacts may create liquidity and credit limitations in international monetary markets and hurt economic development. Some viewers wonder whether these economic undercurrents portend full-scale worldwide financial difficulties.

According to the World Bank's Global Economic Prospects report from June 2021, some impoverished countries and rising economies are still battling COVID-19. Two-thirds of emerging markets and developing economies won't recoup per capita income losses by 2022. The Pandemic has reversed poverty-reduction benefits and created insecurity in low-income nations where vaccinations have lagged. Many countries had already pledged or implemented steps to help epidemic-affected economies. These nations decided on the kind of help to give (loans vs. direct payments), the amount needed, and how to define resources and financing conditions if any. Several governments have taken surprise fiscal and monetary measures to fight the crisis ( 59 ). These economies lack financial resources, and the medical care system is overworked and vulnerable. The downturn or upcoming recession harms all nations; thus, global leaders must overcome COVID-19's consequences on their economies quickly and aggressively. Therefore, according to Fernandes ( 60 ), global action is required to lay down a proposed framework with international cooperative macroeconomic policies to restore confidence and address the feasible solution to the post-COVID crisis barriers to overcome the expected great recession by using collective action with the following research questions ( Figure 13 ).

• How can governments negotiate and cooperate to overcome severe revenue declines, strains on public budgets, and the prospect of sovereign evasions and a 2021–2025 recession?

Figure 13 . The proposed study framework for significant development. Source: Author's development.

Figure 14 . Sectorial impacts of climate change with adaptation and mitigation measures. Source: Author's constructed.

4.2. Mitigation strategies

Immediate and decisive policy actions are required not just to control the epidemic and save lives but also to safeguard the most vulnerable members of our society from economic disaster and maintain economic development and financial stability factors shown below in Figure 14 .

4.3. Fiscal issues

Wealthy nations have launched massive health and public expenditure measures to combat the COVID-19 epidemic. Poorer countries have fewer fiscal and monetary alternatives, and international organizations may aid. Most wealthy countries have ramped up expenditures and used monetary policy to soften the shock of lockdowns and other measures that have closed enterprises and left many jobless. But underdeveloped nations, beginning to react more vigorously, may have fewer alternatives. Plan, coordinate, and execute a business continuity plan for COVID-19. According to Building a Resilient Recovery: How We Can Emerge Stronger from the COVID-19 Pandemic, Ensure revenue collection and agency operations to fund and manage crisis responses.

4.4. Policies for macroeconomics and development

National authorities and multilateral organizations worldwide are exploring extraordinary policy steps in response to the rising health crisis and dismal economic prospects. Central banks in developed and developing nations slashed interest rates, injected liquidity, and provided emergency financing for enterprises and families. About 60 monetary authorities have scored rates since the crisis began, typically in emergency meetings ( 61 ). Direct wage or income assistance measures may mitigate short-term socioeconomic consequences while retaining recovery capability. Tax deferrals, government-subsidized short-term labor, mortgage moratoriums, and immediate cash handouts are examples. Social assistance programs must target the elderly and those in vulnerable jobs during the crisis.

4.5. Manage vital supply and demand

Addressing fundamental supply and demand concerns may avoid shortages, price surges, and misery. Food and medication manufacturing and delivery need reliable transportation, energy, and communications. The crisis-management agency must form committees with the private sector and critical operators to assess daily the flow of essential products and services and the health of employees and vital people ( 62 ).

4.6. Foreign currency supply and demand

There is a good chance that the governments will have to limit foreign currency transactions to stop a run on the local currency caused by a rise in the amount of money in circulation. This is because a rise in the amount of money in circulation directly causes an increase in circulation. For the government to manage its foreign reserves effectively, it must be able to compute the cash flow required to fund the importation of food, medicines, energy, and other essential commodities for at least 6 months while also considering the flows of external debt.

4.7. International organizations have a significant influence

International organizations have authority in mediation, conflict settlement, peacekeeping, and penalties. They assist global health and monetary policies ( 63 ). COVID-19 showed how important international institutions are to our health, economy, and security. United Nations, World Bank Group, and other global and regional institutions' concerted, cohesive response to the crisis's socioeconomic repercussions. As noted above, international organizations must urge for more “unconventional monetary policies” integrated with fiscal stimulus in developing nations, providing them with policy latitude to determine how to do so. They should also push developing country leaders to create a central crisis office ( 64 ). We need international funding to limit the epidemic and protect the most vulnerable. International collaboration may improve results. The post-virus global economy will have slowed growth, increased fragility, and more division. The IMF, OECD, G7, G20, World Bank, and Regional Development Banks must back focused, efficient, and proven-to-work measures in economies that need them to deal with the health, economic, employment, and social effects of the Pandemic on workers in all sectors of the economy, including self-employed and non-permanent, casual, and informal workers, and all businesses, tiny and medium-sized ones (SMEs). The world economy needs to change right away in the real world.

4.8. Getting and making investments easier

The term “foreign direct investment,” abbreviated as “FDI,” has emerged as essential in providing developing nations with financial resources, employment, advanced technology, and managerial expertise. Several underdeveloped countries have reaped significant dividends from these investments in accelerated economic growth and improved overall quality of life. Foreign direct investment could be a big part of the money needed to reach the Sustainable Development Goals by 2030 in areas like basic infrastructure, food security, preventing and adapting to climate change, and health and education ( 65 ). To achieve this goal, governments need to improve the efficiency with which they attract private investment, direct that investment toward sustainable development sectors, and make the most of the economic, social, and environmental benefits that result from that investment.

4.9. Sector-specific policy implications

Upon the onset of the crisis, many economists felt unable to respond. The globally synchronized halt in economic growth, particularly in manufacturing and commerce that preceded the viral breakout was made possible by currency flexibility and consistent government backing ( 62 ). In addition to liquidity and credit market difficulties, the need for supply-side information has encouraged financial analysts to acquire this understanding. If a person loses their job, they might not be able to pay their mortgage or rent before banks offer credit deferment or a way to get money. Mortgage fraud could hurt the market for mortgage-backed securities, mortgage funds, and the economy's growth ( 60 ). Losses on the financial markets happen worldwide and pull people's wealth, especially pensioners with fixed incomes and people who own securities. Investors in mortgage-backed securities have sold some of their shares. Markets haven't always been able to handle traditional policy tools like financial aid. With this instability, it's hard to know how to fix the world economy.

1. Trust and demand recovery necessitate supporting macroeconomic measures in this situation. Employers may use the ETC to pay salaries. Companies may benefit from interest-free loans to offset lost revenue.

2. Despite its flaws, the healthcare business has earned a lot of money mass-producing masks, hand sanitizers, and surgical equipment. Another recession-hit company might make medical equipment. It lacks the required hospitals and quarantine facilities. Updating and dispersing medical facilities may reduce viral transmission.

3. Online buying is trendy and will rise despite the uncertain economy. COVID-19 has natural and long-lasting repercussions for social contact and entertainment, making the tourist business obsolete and leaving many jobless.

5. Conclusion

Multiple industries may be impacted by a disease outbreak, including the capital market, labor market, foreign trade, consumer spending, and production. Because of the lockdown and the risk of spreading the disease, it's taking longer to make things people need. The products' supply chain has been broken, and national and international businesses will lose money. Revenue growth is slowing down because of poor cash flow in the market. There have been thousands of job losses due to the shutdown of industries. A disruption of the production process in several sectors has also negatively affected the GDP of many countries. This article demonstrates the global economic impact of the COVID-19 Pandemic in a straightforward but vivid manner. The virus has claimed thousands of lives and posed considerable challenges to countries. Bloomberg economists say it's too early to know the disease's full impact because it hasn't reached its peak yet. Capital economists estimate that if urgent action is not taken to reduce Wuhan 2019-nCoV as soon as possible, the first quarter of this year can bring China losses of up to $ 62 billion. In comparison, the world will suffer over $280 billion. The World Bank estimates that even a weak influenza pandemic, such as the H1N1 outbreak of 2009, could reduce global GDP by half, or about $300 billion.

5.1. Future study recommendation

It is the right time and place to study the effects of COVID-19 on the global economy and monetary policy of different countries. Even though there are some problems, the research on COVID-19 may lead to many good things. This could lead to new ideas and tests. In light of the COVID-19 crisis, the hidden assumptions in current research need to be looked at separately. The present study provides a roadmap on the literature tying the COVID-19 Pandemic to worldwide harm, directing future research on this new topic. COVID-19 receives regular media attention, although its relationship to monetary and public policy literature is weak. Researchers and policymakers must work together to create additional COVID-19 research. It would assist handle current and future problems. COVID-19 has complicated economic, health, and societal repercussions, making it a sensitive subject. This article focuses on the essential concerns and issues related to COVID-19 to help scholars and policymakers comprehend its worldwide influence on diverse industries.

Author contributions

SN and KA contributed the ideas of the original draft. SN and SK wrote the introduction, literature review, and empirical outcomes sections. SP, HS, and MA helped to collect and visualize data of observed variables. SN, HS, and KA constructed the methodology section in the study. All authors contributed to the article and approved the submitted revised version.

This work was supported by a grant of the Romanian Ministry of Education and Research, CNCS - UEFISCDI, project number PN-III-P4-ID-PCE-2020-2174, within PNCDI III.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: COVID-19, pandemic, sectors, mitigation strategies, global economy

Citation: Naseer S, Khalid S, Parveen S, Abbass K, Song H and Achim MV (2023) COVID-19 outbreak: Impact on global economy. Front. Public Health 10:1009393. doi: 10.3389/fpubh.2022.1009393

Received: 17 August 2022; Accepted: 27 December 2022; Published: 30 January 2023.

Reviewed by:

Copyright © 2023 Naseer, Khalid, Parveen, Abbass, Song and Achim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

The Impact of the Global COVID-19 Vaccination Campaign on All-Cause Mortality

The global COVID-19 vaccination campaign is the largest public health campaign in history, with over 2 billion people fully vaccinated within the first 8 months. Nevertheless, the impact of this campaign on all-cause mortality is not well understood. Leveraging the staggered rollout of vaccines, we find that the vaccination campaign across 141 countries averted 2.4 million excess deaths, valued at $6.5 trillion. We also find that an equitable counterfactual distribution of vaccines, with vaccination in each country proportional to its population, would have saved roughly 670,000 more lives. However, this distribution approach would have reduced the total value of averted deaths by $1.8 trillion due to redistribution of vaccines from high-income to low-income countries.

Funding provided by NIA R01AG073286 (Whaley) and the Peter G. Peterson Foundation Pandemic Response Policy Research Fund (Agrawal). We thank Coady Wing and seminar participants at the 2023 ASHE conference for helpful comments. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

Neeraj Sood reports personal fees from Amazon outside the submitted work.

MARC RIS BibTeΧ

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EIB Working Paper 2024/02 - The effect of uncertainty on investment

Evidence from EU survey data

Publication information

18 Apr 2024

54 Pages (PDF/EN)

ISBN: 978-92-861-5755-4 (PDF/EN)

DOI: 10.2867/723130

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Part of the series :

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Description

Using firm-level survey data combined with firm-level financial information, this working paper examines the impact of uncertainty on EU firms' investment and employment growth. The analysis shows that increased uncertainty significantly dampens investment and employment. Specifically, high uncertainty is associated with a 1 percentage point drop in employment growth and a 3 percentage point decrease in investment rates. Firms that cite uncertainty as a major impediment also report lower growth rates. The study reveals that if uncertainty levels had remained at 2021 levels, investments in 2022 would have been higher by 1 percentage point of fixed assets, and employment growth would have increased by 0.7 percentage points. The research underscores the substantial influence of uncertainty on economic activity and suggests policy measures to mitigate its impact.

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Review on Global Carbon Neutrality Development Based on Big Data Research in the Era of COVID-19: Challenges and Opportunities

• Review article
• Published: 16 April 2024

Cite this article

• Shangyi Zhang 1 ,
• Aleksandra E. Jachimowicz 2 ,
• Xinran Liu 3 ,
• Victor Amber 4 &
• He Zhang 5  

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The present study is based on an analysis of carbon indicators in the environment during the COVID-19 pandemic period. It aims to provide an outlook for the future development of global carbon neutrality in the post- pandemic period.The research on the carbon index during the COVID-19 epidemic is a new scientific work, which is of great significance for the future development of environmental science. Therefore, it is necessary to write a review report on past events before fully conducting this study. It utilized various climate models, initially 232 papers, but eventually narrowing it down to 49 papers for the final context and examination. By examining the carbon neutrality of different phase of the pandemic (pre-pandemic, mid-pandemic, and post-pandemic), as well as considering various climate scenarios, we aim to generate diverse research findings. As the result, the epidemic has had a global impact, posing threats not only to human health and lives but also having far-reaching economic and environmental implications. In the short term, the pandemic has had some positive impacts on advancing carbon neutrality goals. However, in the long-term, it could lead to a slowdown or delay in the achieving global carbon neutrality due to various challenges. These challenges include diverting more resources towards pandemic response and reducing investment in clean energy. Pandemics contribute to global health and economic crises, necessitating significant societal resources such as medical equipment, medicines, human and financial resources to control transmission and treat infected individuals. Consequently, other vital environmental issues like climate change may be neglected or postponed. Ultimately, the financial constraints faced by many countries and businesses during the pandemic may compel them to reduce investments in clean energy as a means to save money and cut costs.

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Acknowledgements

The research group members show the highest esteem for Feike Dijkstra in the Agricultural Science Department in the University of Sydney, for his assistance on linear regression analysis

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Shangyi Zhang

Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, NY, USA

Aleksandra E. Jachimowicz

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S.Z.—conceptualization; A.J.—data curation; V.A.—methodology; X.L.—supervision; H.Z.—writing – original draft preparation and writing – review & editing. All authors have read and agreed to the published version of the manuscript.

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Zhang, S., Jachimowicz, A.E., Liu, X. et al. Review on Global Carbon Neutrality Development Based on Big Data Research in the Era of COVID-19: Challenges and Opportunities. Waste Biomass Valor (2024). https://doi.org/10.1007/s12649-024-02506-3

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Twitter Analysis of Health Care Workers’ Sentiment and Discourse Regarding Post–COVID-19 Condition in Children and Young People: Mixed Methods Study

Authors of this article:

Original Paper

• Macarena Chepo 1 * , RN, BSN, MPH, PhD   ; 
• Sam Martin 2, 3 * , MSc, PhD   ; 
• Noémie Déom 2 , MSc   ; 
• Ahmad Firas Khalid 4 , MD, PhD   ; 
• Cecilia Vindrola-Padros 2 , BA, MA, PhD  

1 School of Nursing, Universidad Andrés Bello, Santiago, Chile

2 Department of Targeted Intervention, University College London, London, United Kingdom

3 Oxford Vaccine Group, Churchill Hospital, University of Oxford, Oxford, United Kingdom

4 Canadian Institutes of Health Research Health System Impact Fellowship, Centre for Implementation Research, Ottawa Hospital Research Institute, Otawa, ON, Canada

*these authors contributed equally

Corresponding Author:

Sam Martin, MSc, PhD

Department of Targeted Intervention

University College London

Charles Bell House 43-45

Foley Street

London, W1W 7TY

United Kingdom

Phone: 44 (0)20 3108 3232

Email: [email protected]

Background: The COVID-19 pandemic has had a significant global impact, with millions of cases and deaths. Research highlights the persistence of symptoms over time (post–COVID-19 condition), a situation of particular concern in children and young people with symptoms. Social media such as Twitter (subsequently rebranded as X) could provide valuable information on the impact of the post–COVID-19 condition on this demographic.

Objective: With a social media analysis of the discourse surrounding the prevalence of post–COVID-19 condition in children and young people, we aimed to explore the perceptions of health care workers (HCWs) concerning post–COVID-19 condition in children and young people in the United Kingdom between January 2021 and January 2022. This will allow us to contribute to the emerging knowledge on post–COVID-19 condition and identify critical areas and future directions for researchers and policy makers.

Methods: From a pragmatic paradigm, we used a mixed methods approach. Through discourse, keyword, sentiment, and image analyses, using Pulsar and InfraNodus, we analyzed the discourse about the experience of post–COVID-19 condition in children and young people in the United Kingdom shared on Twitter between January 1, 2021, and January 31, 2022, from a sample of HCWs with Twitter accounts whose biography identifies them as HCWs.

Results: We obtained 300,000 tweets, out of which (after filtering for relevant tweets) we performed an in-depth qualitative sample analysis of 2588 tweets. The HCWs were responsive to announcements issued by the authorities regarding the management of the COVID-19 pandemic in the United Kingdom. The most frequent sentiment expressed was negative. The main themes were uncertainty about the future, policies and regulations, managing and addressing the COVID-19 pandemic and post–COVID-19 condition in children and young people, vaccination, using Twitter to share scientific literature and management strategies, and clinical and personal experiences.

Conclusions: The perceptions described on Twitter by HCWs concerning the presence of the post–COVID-19 condition in children and young people appear to be a relevant and timely issue and responsive to the declarations and guidelines issued by health authorities over time. We recommend further support and training strategies for health workers and school staff regarding the manifestations and treatment of children and young people with post–COVID-19 condition.

Introduction

More than 3 years after the outbreak of the COVID-19 pandemic [ 1 ], the social, political, and economic impact of this phenomenon has been more than significant, considering >700 million worldwide cases and nearly 7 million people’s deaths [ 2 ]. Given the scale of the phenomenon, it is imperative for all countries to thoroughly examine the lessons gleaned from the pandemic, particularly regarding a matter that has raised significant concern among the populace: the long-term effects experienced by individuals who have had COVID-19, spanning weeks, months, or even years after their initial infection [ 3 ]. This phenomenon, referred to as post–COVID-19 condition (or more commonly “long COVID”), warrants careful consideration and analysis [ 4 ].

There is increasing information regarding the clinical manifestation of this condition, particularly in the adult population. The worldwide prevalence has been estimated at approximately 50% to 70% in individuals hospitalized during acute COVID-19 infection and 10% to 12% in vaccinated cases [ 5 ]. While children and young people have a low likelihood of severe COVID-19 infection [ 6 ], the information available to date indicates that the presence of post–COVID-19 condition in this group may be as disabling as in adults, reaching a prevalence rate of 23.4% (range 3.7%-66.5%) [ 7 ].

An agreed definition by the World Health Organization indicates that post–COVID-19 condition in children and young people is a condition that occurs “in individuals with a history of confirmed or probable SARS-CoV-2 infection when experiencing symptoms lasting at least two months which initially occurred within three months of acute COVID-19” [ 8 ]. Post–COVID-19 condition strongly impacts daily functioning and can develop or continue after COVID-19 infection and may fluctuate or relapse over time [ 4 , 8 , 9 ].

Among the symptoms most frequently attributable to post–COVID-19 condition in children and young people are fatigue, altered smell or anosmia, and anxiety [ 8 ]. However, other symptoms have also been reported, such as sleep disturbances, difficulty in concentrating, abdominal pain, myalgia or arthralgia, earache or ringing in ears, mood swings, persistent chest pain, stomach pain, light sensitivity, diarrhea, heart palpitations, and skin lesions [ 8 , 10 ]. One of England’s most significant studies is the Children and Young People With Long COVID study by Stephenson et al [ 11 ]. This national research matched longitudinal and cohort studies in adolescent individuals aged 11 to 17 years and found the presence of symptoms in 35.4% of the adolescent individuals who tested positive at baseline and 8.3% who of the adolescent individuals who tested negative at baseline. A total of 3 months after testing, 66.5% of those who tested positive and 53.3% of those who tested negative had any symptoms [ 11 ]. However, Stephenson et al [ 12 ] recently indicated that in a 6-month follow-up, the prevalence of specific symptoms reported at the time of the polymerase chain reaction testing decreased over time, where, for example, the prevalence of chills, fever, myalgia, cough, and sore throat among those who tested positive decreased from 10% to 25% to <3%.

As research on the symptoms, prevalence, and treatment of post–COVID-19 condition in children and young people continues, it is essential to add to the literature by developing studies that determine the condition’s impact on this group, considering that they are experiencing a range of unwanted symptoms that disrupt their quality of life and that of their families.

Considering that listening to the voices of families and health workers could be helpful to broaden the knowledge achieved in post–COVID-19 condition in children and young people, a powerful tool could be social media, such as Twitter (subsequently rebranded as X). With >3729 million daily active users, Twitter has become one of the most important social platforms in the world [ 13 ]. People used Twitter during the COVID-19 pandemic for different purposes, such as world leaders communicating with citizens [ 14 , 15 ], organizations monitoring movement [ 16 ], scientists studying public discourse around the pandemic [ 17 , 18 ], and researchers performing sentiment analysis [ 19 - 21 ]. In the case of physicians and health care workers (HCWs), Twitter has been used to share and evaluate scientific evidence, guidelines, and technical advice [ 22 - 24 ] and track the course and burden of disease [ 25 ].

Using the social media monitoring platform Pulsar [ 26 ], we aimed to explore HCWs’ perceptions concerning post–COVID condition in children and young people in the United Kingdom between January 2021 and January 2022. We aimed to contribute to the emerging knowledge on post–COVID-19 condition in children and young people and identify critical areas and future directions for researchers and policy makers.

We considered a mixed methods approach to be a pragmatic research paradigm. We analyzed data by conducting a Collaborative and Digital Analysis of Big Qualitative Data in Time Sensitive Contexts (LISTEN) [ 27 ]. This mixed methods analysis consisted of iterative cycles intercalating team discussion and using digital text and discourse analytics tools to analyze related social media data [ 27 ]. We used the LISTEN method to perform quantitative and qualitative analyses of Twitter posts, extracted through the Pulsar platform [ 26 ], related to the experience of post–COVID-19 condition in children and young people in the United Kingdom (eg, phrases, words, hashtags, videos, and images), published between January 1, 2021, and January 31, 2022. We created an advanced Boolean search for keywords mentioning “long COVID” and corelated words, hashtags, and symptoms; furthermore, we filtered for user accounts who identified as HCWs in their Twitter biography description ( Multimedia Appendix 1 ).

Quantitative analysis of all tweets included the following: (1) engagement analysis, where we specifically measured reactions to posts, for example, a retweet, a share, or a comment or quote made toward a tweet; (2) sentiment and emotion analysis, where we measured the positive or negative sentiment in the words and tone of each post within the context of post–COVID-19 condition and HCW’s roles ( Multimedia Appendix 2 ); (3) emotion analysis, where we measured the emotions expressed in the tweets, classified as sadness, anger, disgust, fear, and joy; (4) frequency analysis, where we observed the frequency of keywords and themes in the data set; (5) segmentation analysis, where we measured the key connections or relationships between keywords and their frequent use in the same context; (6) demographic analysis, where we measured the occupation, gender (man or woman or nonbinary or unknown), and city of origin related to the users posting tweets; and (7) analyses, where we evaluated the most influential accounts and the most mentioned websites.

Big qualitative analysis was carried out through thematic discourse analysis of the data sample, using InfraNodus [ 28 ], specifically analyzing the key themes and topics of concern expressed throughout the data set. A codebook was constructed based on the mapping of themes agreed upon by 3 researchers (ND, SM, and MC; Multimedia Appendix 3 ).

The principal investigators (ND, AFK, SM, and MC) interpreted and analyzed the data collected, following the recommendations for rigorous research provided by Creswell and Poth [ 29 ]. Using the LISTEN method [ 27 ], we aimed to show that the integration of qualitative insights through thematic analysis with the quantitative backing of topic modeling can offer a comprehensive view of the discourse. This mixed methods approach allows us to capture the richness of qualitative data while leveraging the objectivity of quantitative measures. Our initial data harvest of the larger corpus data from the Pulsar platform captured 300,000 tweets; this data harvest helped to underpin the software’s sentiment analysis modeling of this specific data set, providing a robust quantitative foundation. The addition of further qualitative data analyses from a smaller qualitative sample allowed for an in-depth understanding of nuanced conversations, particularly when exploring new or complex phenomena such as post–COVID-19 condition in children and young people, with the provision of insights into the context, subtext, and sentiment behind the tweets offering valuable snapshots of public perception and discourse. We used an iterative mixed methods approach, iterating between team discussions and using digital analytics tools to discern relevant themes from the Twitter data corpus. Specifically, we used InfraNodus for thematic analysis, which incorporates a topic modeling script for analyzing and identifying key topics of concern with a data set and provides a structured and objective interpretation of the data. The coding process involved 3 independent researchers (MC, SM, and ND), each with expertise in health care, social network analysis, and digital global health. When initial coding disagreements arose, we meticulously tagged any queries and discussed the posts in question. These instances led to 3 structured meetings wherein the research team deliberated collaboratively to resolve conflicting interpretations. This approach resulted in an 81.99% (2122/2588) initial intercoder agreement rate for the tweets analyzed. For the remaining instances where consensus was not initially reached, the majority rule was applied to finalize theme codings. To quantify the reliability of our coding procedure, with 81.99% (2122/2588) of the tweets coded identically, we used the Cohen κ score, which provides a measure of interrater agreement adjusted for chance. Including the calculation of all variations, this score was calculated to be approximately κ=0.70, indicating good agreement among the coders.

Ethical Considerations

The study only collected data from publicly accessible social networks that have been anonymized by various means, particularly by replacing all usernames and links with anonymous text and summaries of tweets that have been edited, retaining the original message, avoiding direct quotations being identifiable, and ensuring that no information is provided on the identity of the individuals who posted the content studied on the platform.

Internet research requires researchers to carefully consider guidelines to determine whether ethics approval and informed consent are needed [ 30 ]. On the basis of the terms set out by the Research Ethics Committee at the University College London [ 31 ], the study was considered exempt from formal ethics approval for the following reasons: (1) study involving information freely available in the public domain, such as published biographies, newspaper accounts of an individual’s activities, and published minutes of a meeting, that although is considered personal under the Data Protection Act, would not require ethics review; and (2) study involving anonymized records and data sets in the public domain, such as data sets available through the Office for National Statistics or the UK Data Archive where appropriate permissions have already been obtained and it is not possible to identify individuals from the information provided.

Therefore, we anonymized all records and data sets collected during the study to make identification impossible. We removed social media usernames from the data samples. No direct or easily traceable quotes have been included. These measures align with best practices [ 32 - 35 ]. While this study was beyond the scope of the human ethics committee, we adhered to the principles of ethics: beneficence, nonmaleficence, autonomy, and justice [ 36 ]. We collected and analyzed data through secure encrypted servers via the Meltwater and InfraNodus platforms.

Audience Analysis

During the period from January 2021 to January 2022, we obtained 300,000 tweets from 936 accounts. After filtering for relevant posts (refer to inclusion and exclusion criteria in Multimedia Appendix 1 ), we analyzed a sample of 2588 tweets using mixed methods analysis. In terms of gender (man, woman, nonbinary, or unknown), 32.88% (851/2588) were female individuals, 23.49% (608/2588) were male individuals, and 43.59% (1128/2588) were unknown. According to the description given in the user’s biography, the most frequently self-reported terms were “NHS” (582/2588, 22.49%), “health” (230/2588, 8.89%), “medical” (168/2588, 6.49%), “nurse” (166/2588, 6.41%), “clinical” (160/2588, 6.18%), “mum” (158/2588, 6.11%), “doctor” (145/2588, 5.6%), and “GP” (145/2588, 5.6%). In terms of city, tweets came mainly from London (958/2588, 37.02%), Newcastle upon Tyne (326/2588, 12.6%), Redcar (160/2588, 6.18%), Manchester (140/2588, 5.41%), and Bradford (111/2588, 4.29%).

Regarding profession described in the user’s biography, the most frequently mentioned roles were nurses (176/2588, 6.8%); medical roles, for example, paramedic and nursing assistant (173/2588, 6.68%); clinical roles, for example, surgeon, physiotherapist, and anesthesiologist (160/2588, 6.18%); general practitioners (GPs), for example, hospital GP or local surgery GP (142/2588, 5.49%); and physician (140/2588, 5.41%). The most frequent organization affiliated with was the National Health Service (587/2588, 22.68%).

Most Influential Accounts

One of the accounts that generated the highest number of mentions and, therefore, some of the most influence, as they were the ones that talked the most about post–COVID-19 condition in children and young people, was the account for @longcovidkids (593/2588, 22.91% tweets), related to the most shared website longcovidkids.org [ 37 ] , an international UK-based charity for families and children living with post–COVID-19 condition. Although the account was created in October 2020, it was first mentioned in our data collection timeline on January 1, 2021. It offers web support services, funding, and research participation and represents children and young people living with post–COVID-19 condition in expert forums, research panels, health organizations, and parliamentary groups. The other most shared web pages were theguardian.com (the United Kingdom) [ 38 ], bbc.co.uk (the United Kingdom) [ 39 ], peoplewith.com (the United States) [ 40 ], and ncbi.nlm.nih.gov (the United States) [ 41 ]. This shows that in the United Kingdom, there was a mixed influence of UK and US link resources linked to HCW Twitter users in the United Kingdom.

Keyword Analysis

The volume of social media engagement in the discussion about the post–COVID-19 condition experience in children and young people in the United Kingdom reached 1400 posts, 1550 engagements, and 1.9 million impressions. Overall, comments were very responsive to government decisions regarding the vaccination program and school closures ( Multimedia Appendix 4 ). During the first peak of comments in January 2021, the amount of discourse expanded leading up to March 2021, when there were different announcements of school closures, and the guidelines were delivered regarding the priority groups of the vaccination program (frontline HCW and people aged >80 years first). The highest engagement was between June and July 2021, which coincides with the government announcement regarding the availability of vaccines for people aged >18 years. The third peak of comments occurred in September 2021, the same month the authorities announced the extension of the vaccination program to children aged 12 to 15 years.

Top Keywords Analysis

The top words in posts associated with children and young people’s experience of post–COVID-19 condition in the United Kingdom were “Children” (352/2588, 13.6%), “kids” (160/2588, 6.18%), “people” (158/2588, 6.11%), “Young” (148/2588, 5.72%), and “schools” (83/2588, 3.21%). The top hashtags were #longcovid (1387/2588, 53.59%), #longcovidkids (448/2588, 17.31%), #covid19 (370/2588, 14.3%), and #covid (176/2588, 6.8%).

Sentiment and Emotions Analysis

According to sentiment analysis, 99.38% (2572/2588) of the posts reflected negative sentiments and 0.62% (16/2588) reflected positive sentiments. Negative sentiments were mainly associated with comments on hospitalization figures related to the COVID-19 pandemic, criticism of pandemic mitigation policies, and vaccination of children and young people. Furthermore, positive sentiments mainly concerned acknowledgments around decreasing numbers of community support groups.

The primary emotions identified were as follows:

• Sadness (1752/2588, 67.7%), such as in the following tweet:

@[Username] Really upset, after my tough on-call last night. Hospitalisations are still going up, and Gov announcement minismises the effect of long-COVID in adults and children. It’s so hard to keep spirits up today. But we’ll try and continue doing our best in the NHS.

• Joy (367/2588, 14.18%), such as in the following tweet:

@[Username] It’s been an amazing day! [...] I’ve been able to share the experience I’ve gained treating children and adolescents with Long COVID over the last year.

• Fear (233/2588, 9%), as seen in the following tweet:

@[Username] It’s really urgent that young people get the message that they need to get vaccinated. Long COVID is ruining many people’s lives! It’s not a lie or hypochondria, there are real, physiological changes, please understand!

Segmentation Analysis

This analysis revealed the critical clusters of conversation around the main topics of concern within the discourse network around post–COVID-19 condition. Comments were distributed in 4 key conversation segments as follows:

• People, schools, and prevention (1734/2588, 67%): Most of the comments related to measures taken in terms of COVID-19 prevention in schools, concern about the risk of exposure, and sharing experiences of infection in schools.
• Health, adults, and impact (401/2588, 15.49%): Comments mainly reflected concerns and uncertainty about the long-term effect of post–COVID-19 condition on both children and young people and adults.
• Cases, virus, and risk (326/2588, 12.6%): Comments reflected worries about the associated risks and long-term consequences attributable to post–COVID-19 condition (in both adults and children and young people) and the constant mutation of the virus, which will create a permanent risk in the population.
• Months, distress, and symptoms (106/2588, 4.1%): Some HCWs used Twitter to share how children and young people experience post–COVID-19 condition and the extent of these symptoms. Some HCWs exemplified certain typical manifestations, such as fatigue.

Discourse Analysis by Theme

To better understand the topics discussed from the segmentation analysis, we performed a discourse analysis of the key co-occurring themes and topics of concern shared within discussions regarding post–COVID-19 condition in children and young people. The following themes emerged ( Textbox 1 ): concern or uncertainty for the future, school attendance, mask protection from COVID-19, vaccine uptake, infection rates, policy (support or skepticism), understanding and visualizing symptoms, child mental health, access to care, community support, and research ( Figures 1 and 2 ).

• Concern for the future or uncertainty (615/2588, 23.76% tweets): Most comments showed a concern for the future, focusing on shared statistics regarding the rate and spread of infection in children and young people and how this would affect future health outcomes. Furthermore, this group expressed concern regarding political decisions; the presence of illness in loved ones; the eventual overload and response capacity of the health system in the face of an increase in post–COVID-19 condition cases; and the need for training of health care workers (HCWs) to deal with comorbid, potentially long-term symptoms ( Figure 1 A).
• Schools (460/2588, 17.77% tweets): Comments aimed to promote vaccination policies for schoolchildren and flexible measures regarding teachers’ work and attendance, considering cases of people with prolonged symptoms. In addition, several tweets expressed dissatisfaction with school risk mitigation measures, such as the use of face masks and air filters ( Figure 1 B).
• Vaccine (386/2588, 14.9% tweets): Most tweets from this group showed their disapproval of the constant changes in the government’s decisions regarding schools and priority groups for vaccination. Between March and June 2021, the first set of tweets criticized the lack of priority in the vaccination program for schoolchildren and other at-risk groups (such as teachers). Once the authorities announced a vaccination program for schoolchildren aged 12 to 15 years ( Multimedia Appendix 4 ), most comments promoted vaccination for this group. A few comments (78/2588, 3.01%) shared concerns about the vaccine’s efficacy for children, based on the experiences of COVID-19 reinfection in adults despite having received the recommended initial doses. However, to a lesser extent (26/2588, 1%), there was a refusal to vaccinate children, citing fear of possible adverse effects. Nonetheless, it is worth noting that the community frequently refuted such comments ( Figure 1 C).
• Share statistics (334/2588, 12.91% tweets): Frequently, HCWs shared statistical data, such as the number of affected children and young people, the number of post–COVID-19 condition cases, and hospital admissions and deaths. Some of these data were used to validate the existence of the post–COVID-19 phenomenon or to express concern about it ( Figure 1 D).
• Policy (316/2588, 12.21% tweets): The comments were responsive to the policies emanating from the authorities over time ( Multimedia Appendix 4 ). There were 5 main criticisms, including changes in school closure or opening policies; HCWs question why the authorities ignore the evidence of post–COVID-19 cases in children and young people, leading them to question whether decision makers have sufficient training to control the pandemic adequately; the failure to include teachers and school workers in the COVID-19 vaccination program as well as the younger population; the lack of mitigation measures in schools, such as improvements in ventilation systems and mandatory use of masks; and the herd immunity as a plan in the government’s hidden agenda , that is, to promote work and activate the economy ( Figure 1 E).
• “Proof” (280/2588, 10.82% tweets): Most tweets in this group argued regarding the existence of children and young people with post–COVID-19 condition through pictures; statistics; scientific papers; and personal, family, and professional experiences ( Figure 1 F).
• Signs and symptoms (189/2588, 7.3% tweets): Among the symptoms described, chronic fatigue and exhaustion were the most frequent symptoms, which prevent normal activities. Other symptoms were respiratory: dyspnea, chronic cough, and shortness of breath; gastrointestinal: acute or intense abdominal pain, nausea, bloating, gastroparesis, and change in smell or taste; muscular: severe joint pain, “painful foot” and difficulty with physical activity; mental health: anxiety and low mood; topical: rash, skin rashes, and redness and pain in the eyes; and nonspecific symptoms, such as chest pain, heart palpitations, constant high body temperature, precocious puberty, hormonal changes, and erectile dysfunction ( Figure 2 A).
• Face masks (119/2588, 4.6% tweets): Face masks were widely promoted, especially in schools, because HCWs considered them as a practical and straightforward strategy to control the pandemic ( Figure 2 B).
• Skepticism (101/2588, 3.9% tweets): Comments showed reticence toward post–COVID-19 condition in children and young people. Some of the arguments focused on a perceived lack of clarity in the clinical manifestations and stressed the need to better differentiate the post–COVID-19 condition from other related symptomatologies, such as mood disorders (eg, depression and anxiety due to confinement). In contrast, several arguments agreed on the need for more scientific evidence, arguing that post–COVID-19 condition in children and young people are isolated. Other users claimed not to know of such cases instead of calling post–COVID-19 condition in children and young people SMS text message an exaggeration. In addition, several arguments favored releasing restrictions for children and young people, particularly arguments related to the use of masks, because of possible associated risks, for example, hypoxia ( Figure 2 C).
• Mental health (54/2588, 2.09% tweets): Symptoms attributable to mental health problems in children and young people were also a concern. For instance, HCWs mentioned sadness, fear of infecting their family, anxiety regarding sick parents, stress, night terrors, self-harm, and suicidal ideation. Furthermore, users discussed a perceived lack of specific support for children and young people and their families in situations such as hospitalization; prolonged COVID-19 condition; admission to intensive care; and death of a family member, schoolmate, or teacher, all situations that triggered permanent stress in these groups ( Figure 2 D).
• Community support or asking for advice (93/2588, 3.59% tweets): Some HCWs used Twitter to ask for guidance on a specific issue or share experiences of having post–COVID-19 condition or caring for children and young people or family members. Furthermore, they shared informative infographics provided by experts regarding post–COVID-19 condition in children and young people ( Figure 2 E).
• Access to health care or treatment (72/2588, 2.78% tweets): Some HCWs mentioned the lack of specialist (cardiology) support, concerns regarding prolonged National Health Service burnout, and criticisms regarding how follow-up was carried out concerning the relative symptomatology of children and young people with post–COVID-19 condition. At the same time, opening new centers for children and young people with post–COVID-19 condition generated different reactions. On the one hand, some HCWs recognized it as a substantial development, but on the other hand, some HCWs recognized it as proof of the existence of post–COVID-19 condition in children and young people, which raised concerns for the future ( Figure 2 F).
• Research (52/2588, 2% tweets): Under this theme, tweets largely promoted study on post–COVID-19 condition in children and young people or highlighted the need for further study on the subject ( Figure 2 G).
• Images (57/2588, 2.2% tweets): Images shared were primarily from scientific studies, including infographics (from organizations such as National Health Service or @LongCovidKids) and visualization of children and young people’s symptoms, such as rashes, COVID-19 toe, and joint pain. Most infographics shared by organizations (and not individuals), such as the organization LongCovidKids, were related to statistics, such as the number of children and young people with post–COVID-19 condition or the quantification of the type of symptoms experienced. Shared photographs tended to show the more “visually recognizable” symptoms of post–COVID-19 condition, such as skin lesions, rashes, or inflammation. The less visible symptoms, such as chronic fatigue and neurological issues, were represented with photographs of children and young people lying, sleeping under blankets, or duvets or on hospital beds ( Figure 2 H).

Principal Findings

Our primary objective was to explore HCWs’ perceptions concerning post–COVID-19 condition in children and young people in the United Kingdom between January 2021 and January 2022. Our findings indicated that comments made by HCWs on Twitter were responsive to announcements issued by authorities regarding the management of the COVID-19 pandemic in the United Kingdom and associated regulations on the operation of schools. The most frequent feelings and emotions were negative, mainly sadness. In turn, we identified relevant themes for HCWs, such as uncertainty or concern about the future; policies; and regulations for the prevention, management, and addressing both COVID-19 and post–COVID-19 condition in children and young people; vaccination; and the use of Twitter as a strategy to share scientific literature, management strategies, and clinical and personal experiences.

Concern from HCWs regarding the policies for addressing the COVID-19 pandemic in the children and young people in the United Kingdom (including vaccination and schools) was a recurring theme in our findings. Furthermore, concern regarding the side effects of the COVID-19 vaccine and how the vaccine might interact with preexisting physiological symptoms of post–COVID-19 condition in children and young people was a topic of discussion. Similarly, the constant change in policy making in the United Kingdom, as public health bodies and governments have tried to understand and adapt to the emergence of post–COVID-19 condition, have added to the strength of this ongoing debate [ 42 ]. The lack of up-to-date evidence on post–COVID-19 condition in children and young people prompted HCWs to rely on Twitter during the pandemic to communicate relevant information. Twitter has a broad audience reach; is used as a communication tool by politicians, health bodies, and other key influences; and facilitates real-time updates [ 43 ]. During the pandemic, HCWs, primarily those in frontline roles and local response coordination, have often been challenged to become credible spokespersons for pandemic information [ 44 ]. Such credibility directly influences public confidence and decision-making, ultimately determining the success or failure of a public health intervention [ 43 ].

Furthermore, failures in risk communication could explain the presence of uncertainty and negative feelings associated with school regulations. When people are upset, distressed, or fearful, they often do not trust the authority, decrease the perceived validity of the communication received, and find information processing difficult [ 45 ]. In this regard, Fotheringham et al [ 46 ] indicated that during 2020, school leaders in the United Kingdom faced pressures and challenges related to translating and enacting school policies, particularly with the perceived lack of agency shared by the government concerning being able to translate centrally issued guidelines. In turn, Tomson et al [ 47 ] reported that the pandemic has negatively impacted the well-being of leaders in all types of schools and across all demographic groups, affecting their ability to think clearly and solve work-related problems. Given that the protection and care of children and young people health during the COVID-19 pandemic ultimately rests with school leaders, the search for support strategies that focus on the needs of these groups becomes an urgent necessity.

Findings in Relation to Other Studies

Using Twitter’s information, this is one of the first studies to capture health professionals’ perceptions of prolonged COVID-19 in the children and young people in the United Kingdom. However, other studies have addressed post–COVID-19 condition on this social network. Callard and Peregov [ 48 ] reviewed how, through social platforms such as Twitter, patients made the persistence and heterogeneity of COVID-19 symptoms visible, thus catapulting the inclusion of post–COVID-19 condition as a relevant phenomenon in clinical and policy debates. In contrast, other authors in the last 2 years have explored on various platforms (including Twitter) the persistence of symptoms and emotional impact after months of suspected and confirmed diagnosis of COVID-19 [ 49 - 55 ], including the period of vaccination. Furthermore, others have explored web discussions regarding this phenomenon [ 56 ]. Several of these authors agree on a perceived lack of support and specific resources from governmental bodies, a lack of information or clarity in the instructions given, and the absence of formal mechanisms to allow the voices of patients and the community to be heard. The above point is critical as it highlights the gap between the needs of the population and the response provided by policy makers, which not only translates into a gap in access to health services but also limits citizen participation in decision-making on the issues that affect their own health and increases distrust toward regulations and instructions issued by the government.

Implications for Policy and Practice

Several policy recommendations and implications are targeted at various stakeholders to consider while implementing future policy guidelines to address post–COVID-19 health care delivery. First, policy makers should consider investing appropriate resources to collect data regarding post–COVID-19 condition in children and young people, specifically on the impact of COVID-19 on the mental health of children and young people. This implies working closely with researchers to streamline data collection and reporting on post–COVID-19 condition. Second, policy makers should consider providing a basic level of psychosocial support with access to quality mental health and psychosocial support services for HCWs, school staff, parents, and children and young people experiencing post–COVID-19 condition. This implies strengthening health systems, community-based programming, and mobilization. Policies must include documenting the impact of mental health and psychosocial support interventions and innovative approaches to be more widely disseminated and scaled up across different contexts and target population groups. Third, to address the criticism around frequent changes in school closure and opening policies, decision makers should develop clear, easy-to-understand school mitigation plans informed by the best available evidence. The plans should incorporate teachers, school workers, and parents to ensure all voices are included in the policy plan. Fourth, policy makers should adopt a shared decision-making approach incorporating HCWs in the decision-making process for managing the COVID-19 pandemic. Finally, government decision makers should set post–COVID-19 pandemic recovery policies informed from a health equity perspective and how this affects children and young people living with post–COVID-19 condition, factoring in childhood, family income, housing, domestic violence, access to health care, and racism.

In terms of the needed clearer road map for recommendations to support training strategies for HCWs and school staff regarding post–COVID-19 condition in children and young people, we have outlined the following 10 steps.

Step 1: Data Collection and Analysis

Our study underlines the critical need for comprehensive data on post–COVID-19 condition’s impact on the mental health of children and young people. As a first step, it is recommended that policy makers should allocate resources for the systematic collection and analysis of data on post–COVID-19 condition in children and young people, particularly focusing on mental health outcomes. These data should be used to identify the most prevalent symptoms and the most effective treatment strategies. In this context, it is recommended that experts emphasize the importance of early detection and medical consultation for mental health issues in children and young people diagnosed with post–COVID-19 condition, including mood changes, irritability, social withdrawal, memory problems, difficulty in concentrating, anxiety, depression, posttraumatic stress, school absenteeism, and suicidal ideation [ 57 , 58 ]. This entails working closely with researchers to streamline data collection and reporting on post–COVID-19 condition.

Step 2: Psychosocial Support Framework

It has been noted that globally, programs for managing post–COVID-19 condition in children and young people are heterogeneous, ranging from the use of physiotherapy, pediatric occupational therapy, and psychological support to interventions aimed at lifestyle modifications [ 59 ]. This diversity could impact differential outcomes in the treatment, recovery, and timely and effective rehabilitation of children and young people with post–COVID-19 condition. Upon analyzing the wider literature and the social media data in this study, it is recommended that a basic level of psychosocial support should be established. This would involve ensuring access to quality mental health services for HCWs, school staff, parents, and children and young people with post–COVID-19 condition. This framework should be integrated into the health system and community-based programming, emphasizing the mobilization of resources and strengthening of support networks. It is suggested that the psychosocial support framework should facilitate access to quality mental health services and support networks that are robust and responsive. Community engagement gleaned from further Twitter discourse analysis should be a helpful guide in the development of these services to ensure they meet the real and expressed needs of children and young people with post–COVID-19 condition. Practical examples of basic psychosocial support include using web support services; individual or group therapy sessions; school-based emotional support programs; and counseling sessions aimed at parents, family members, or school staff.

Step 3: Educational Mitigation Plans

The frequent policy changes around school closures highlight the necessity for stable and clear educational mitigation plans. It is recommended that these plans should be directly informed by the evidence collected and further analysis of sentiments and emotions surrounding post–COVID-19 condition in schools. Incorporating the viewpoints of teachers, parents, and school staff, as identified in our thematic analysis, will ensure that the mitigation strategies are comprehensive, feasible, and sensitive to the psychosocial impact on children and young people. School staff and policy makers should collaborate to develop clear, evidence-informed educational mitigation plans. These plans should be straightforward and involve teachers, school workers, and parents in their creation, ensuring a unified approach that considers the voices of all stakeholders.

Step 4: Shared Decision-Making in Health Care

In health care settings, the adoption of a shared decision-making model is crucial, enabling HCWs to actively contribute to the formulation of COVID-19 and post–COVID-19 policies. This inclusive approach ensures that frontline workers can provide valuable insights toward policy development. To facilitate this, the establishment of advisory committees composed of representatives from HCWs is recommended. This committee can convene regularly to deliberate on key decisions pertaining to the COVID-19 pandemic management, including prevention measures, resource distribution, and vaccination strategies. Such collaborative groups have demonstrated effectiveness in identifying priority needs within the context of a pandemic [ 60 ].

Step 5: Health Equity in Policy Setting

Post–COVID-19 recovery policies should be set with a health equity lens. This means considering factors such as family income, housing, domestic violence, access to health care, and racism and how these factors affect children and young people living with post–COVID-19 condition. Our findings emphasize the importance of framing post–COVID-19 recovery policies through a lens of health equity. The concerns raised by HCWs regarding the socioeconomic impacts, such as family income and access to health care, underline the need for policies that address not just the medical aspects of post–COVID-19 condition but also the social determinants of health. An equitable approach will ensure that children and young people from diverse backgrounds receive appropriate support.

Step 6: Documenting and Disseminating Interventions

It is vital to document the impact of mental health and psychosocial support interventions. In this context, it is crucial to implement innovative strategies to disseminate unbiased information about post–COVID-19 condition among health care professionals and educators working with children and young people, ensuring it reaches different contexts and populations. These strategies may include creating interactive multimedia resources, such as videos and mobile apps; organizing webinars; actively using social media; and forming web support groups. These groups will provide a space where patients, health care professionals, and educators can share their experiences and knowledge regarding post–COVID-19 condition. These actions will not only help reduce isolation and social stigma but also strengthen support for these groups considered vulnerable [ 61 ].

Step 7: Developing a Clear Communication Strategy

Policy makers must develop a clear communication strategy to address frequent policy changes and mitigate confusion. This strategy should be informed by the data collected and analysis conducted in Step 1. The data reveal a palpable sense of uncertainty and frustration due to frequent policy shifts, underscoring the need for a clear and consistent communication strategy. This strategy should be grounded in the evidence gathered from the health care community’s discourse and aim to minimize confusion by providing timely, transparent, and reliable information regarding post–COVID-19 policies and support services.

Step 8: Training and Support Strategies

On the basis of the findings of the comprehensive data analysis, specific training and support strategies should be developed for HCWs and school staff. These strategies should be focused on the practical aspects of identifying and managing post–COVID-19 condition in children and young people. For instance, training sessions could include practical workshops on recognizing post–COVID-19 symptoms in children and adolescents, conducting diagnostic assessments, and implementing appropriate treatment and support interventions.

Step 9: Continuous Feedback and Policy Adaptation

The continuous evolution of the post–COVID-19 phenomenon demands an iterative approach to policy making. On the basis of our study, we recommend establishing feedback mechanisms with HCWs and school staff to monitor the reception and effectiveness of implemented policies. This feedback, coupled with ongoing research, should inform policy adaptations to ensure they remain aligned with the evolving landscape of post–COVID-19 condition and its impact on children and young people.

Step 10: Evaluation and Research

Finally, there should be a commitment to ongoing evaluation and research. This will involve not only monitoring the implementation of the abovementioned steps but also supporting new research to fill any remaining gaps in understanding the long-term effects of COVID-19 on children and young people.

This sequence of steps is designed to be iterative and responsive, ensuring that the recommendations from the study are translated into concrete actions that adapt to emerging data and research findings.

Strengths and Limitations

A key strength of this study is that our social media analysis of post–COVID-19 condition contributes toward an emerging understanding of reported experiential, emotional, and practical dimensions of post–COVID-19 condition in children and young people specifically and questions of vaccine hesitancy in children and young people with post–COVID-19 condition. This is one of the few studies to collect HCWs’ perceptions regarding post–COVID-19 condition in children and young people in the United Kingdom using information from Twitter. We identify key areas that need considering attention and focus, such as the provision of psychosocial support with access to quality mental health resources to alleviate the impact of post–COVID-19 condition in children and young people and the development of clear post–COVID-19 pandemic recovery guidelines that are informed by health equity perspective, and how this affects children and young people living with post–COVID-19 condition.

One of the limitations this study acknowledges is the definition of post–COVID-19 condition in children and young people. When data were collected, the lack of consensus on the definition of post–COVID-19 condition in children and young people forced us to formulate a definition of post–COVID-19 condition in children and young people based on the available literature. Furthermore, this study is limited to the perceptions of people who used descriptors in their web biography attributable to HCWs; therefore, our results only represent some HCWs in the United Kingdom and those in other countries. In turn, this research collected data from Twitter only; therefore, further inquiry into HCWs’ perceptions of post–COVID-19 condition in children and young people required expanding to other data sources or social networks and including languages other than English. We acknowledge that demographic factors, geographic location, and individual daily activities of social media users can significantly influence language use and word choice, introducing potential biases in tweet-based data. Such biases are inherent in any analysis of social media content and can affect the generalizability of findings. For instance, our study relies on Twitter data, which do not encompass the full spectrum of global or the UK public opinion on post–COVID-19 condition in children and young people. While Twitter serves as a valuable platform for capturing real-time sentiments and experiences, it is not fully representative of all demographics and geographic regions. Our results may reflect the perspectives of more vocal or active social media users, which may not correspond to the silent majority or those without access to social media. In addition, the absence of geotagged information for many users limits our ability to conduct a more nuanced spatial analysis of the sentiments expressed.

Furthermore, our study is built upon the recognition that social media data may overrepresent certain demographic groups while underrepresenting others, such as the older population or those without reliable internet access. This skew can influence the apparent prevalence of certain views or experiences of post–COVID-19 condition. Moreover, individuals’ patterns of daily life, reflected in their social media use and content, contribute additional layers of complexity and potential bias to the discourse analyzed.

Consistent with scholarly precedents on the subject [ 62 , 63 ], our study acknowledges these biases as intrinsic limitations of social media–based research. Although our analysis did not control for these factors, we recognize their potential impact on our results. Future studies would benefit from incorporating a broader array of data sources, including interviews or focus groups, to provide a more representative and comprehensive understanding of post–COVID-19 condition in children and young people. This approach would complement our Twitter-based findings and help mitigate the biases inherent in social media data.

Conclusions

More than a year after the start of the COVID-19 pandemic, the perceptions described on Twitter by HCWs concerning the presence of post–COVID-19 condition in children and young people appear to be a relevant and timely issue as well as very responsive to the declarations and guidelines issued by the health authorities over time. The most prominent group within the discourse studied was the activist or lobbying organization @LongCovidKids, which shared the most tweets and images over the period studied. We recommend that future research focus on how web health activism is organized and carried out for children and young people with post–COVID-19 condition. Such a strategy would allow for a better understanding of the scope and impact of this phenomenon and how it can influence decision-making. Furthermore, we suggest different mitigation strategies, support, and training of HCWs and school staff regarding manifestations and treatment of post–COVID-19 condition in children and young people across all demographic areas.

Acknowledgments

The authors would like to thank the Rapid Research Evaluation and Assessment Lab, Department of Targeted Intervention, University College London, London, United Kingdom, whose support has been essential for developing this project.

Conflicts of Interest

None declared.

Filters used for the search strategy on Twitter.

Sentiment analysis framework: attitudes toward post–COVID-19 condition in children and young people.

Theme codebook: examples of tweets that fit into main themes tagged for mention of children and young people with post–COVID-19 condition.

Timeline of national governmental policies and guidelines regarding children and young people.

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Abbreviations

Edited by A Mavragani; submitted 20.06.23; peer-reviewed by R Gore, A Wahbeh; comments to author 02.11.23; revised version received 14.02.24; accepted 08.03.24; published 17.04.24.

©Macarena Chepo, Sam Martin, Noémie Déom, Ahmad Firas Khalid, Cecilia Vindrola-Padros. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.04.2024.

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