case study on natural disasters

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Kerala flood case study

Kerala flood case study.

Kerala is a state on the southwestern Malabar Coast of India. The state has the 13th largest population in India. Kerala, which lies in the tropical region, is mainly subject to the humid tropical wet climate experienced by most of Earth’s rainforests.

A map to show the location of Kerala

Eastern Kerala consists of land infringed upon by the Western Ghats (western mountain range); the region includes high mountains, gorges, and deep-cut valleys. The wildest lands are covered with dense forests, while other areas lie under tea and coffee plantations or other forms of cultivation.

The Indian state of Kerala receives some of India’s highest rainfall during the monsoon season. However, in 2018 the state experienced its highest level of monsoon rainfall in decades. According to the India Meteorological Department (IMD), there was 2346.3 mm of precipitation, instead of the average 1649.55 mm.

Kerala received over two and a half times more rainfall than August’s average. Between August 1 and 19, the state received 758.6 mm of precipitation, compared to the average of 287.6 mm, or 164% more. This was 42% more than during the entire monsoon season.

The unprecedented rainfall was caused by a spell of low pressure over the region. As a result, there was a perfect confluence of the south-west monsoon wind system and the two low-pressure systems formed over the Bay of Bengal and Odisha. The low-pressure regions pull in the moist south-west monsoon winds, increasing their speed, as they then hit the Western Ghats, travel skywards, and form rain-bearing clouds.

Further downpours on already saturated land led to more surface run-off causing landslides and widespread flooding.

Kerala has 41 rivers flowing into the Arabian Sea, and 80 of its dams were opened after being overwhelmed. As a result, water treatment plants were submerged, and motors were damaged.

In some areas, floodwater was between 3-4.5m deep. Floods in the southern Indian state of Kerala have killed more than 410 people since June 2018 in what local officials said was the worst flooding in 100 years. Many of those who died had been crushed under debris caused by landslides. More than 1 million people were left homeless in the 3,200 emergency relief camps set up in the area.

Parts of Kerala’s commercial capital, Cochin, were underwater, snarling up roads and leaving railways across the state impassable. In addition, the state’s airport, which domestic and overseas tourists use, was closed, causing significant disruption.

Local plantations were inundated by water, endangering the local rubber, tea, coffee and spice industries.

Schools in all 14 districts of Kerala were closed, and some districts have banned tourists because of safety concerns.

Maintaining sanitation and preventing disease in relief camps housing more than 800,000 people was a significant challenge. Authorities also had to restore regular clean drinking water and electricity supplies to the state’s 33 million residents.

Officials have estimated more than 83,000km of roads will need to be repaired and that the total recovery cost will be between £2.2bn and $2.7bn.

Indians from different parts of the country used social media to help people stranded in the flood-hit southern state of Kerala. Hundreds took to social media platforms to coordinate search, rescue and food distribution efforts and reach out to people who needed help. Social media was also used to support fundraising for those affected by the flooding. Several Bollywood stars supported this.

Some Indians have opened up their homes for people from Kerala who were stranded in other cities because of the floods.

Thousands of troops were deployed to rescue those caught up in the flooding. Army, navy and air force personnel were deployed to help those stranded in remote and hilly areas. Dozens of helicopters dropped tonnes of food, medicine and water over areas cut off by damaged roads and bridges. Helicopters were also involved in airlifting people marooned by the flooding to safety.

More than 300 boats were involved in rescue attempts. The state government said each boat would get 3,000 rupees (£34) for each day of their work and that authorities would pay for any damage to the vessels.

As the monsoon rains began to ease, efforts increased to get relief supplies to isolated areas along with clean up operations where water levels were falling.

Millions of dollars in donations have poured into Kerala from the rest of India and abroad in recent days. Other state governments have promised more than $50m, while ministers and company chiefs have publicly vowed to give a month’s salary.

Even supreme court judges have donated $360 each, while the British-based Sikh group Khalsa Aid International has set up its own relief camp in Kochi, Kerala’s main city, to provide meals for 3,000 people a day.

International Response

In the wake of the disaster, the UAE, Qatar and the Maldives came forward with offers of financial aid amounting to nearly £82m. The United Arab Emirates promised $100m (£77m) of this aid. This is because of the close relationship between Kerala and the UAE. There are a large number of migrants from Kerala working in the UAE. The amount was more than the $97m promised by India’s central government. However, as it has done since 2004, India declined to accept aid donations. The main reason for this is to protect its image as a newly industrialised country; it does not need to rely on other countries for financial help.

Google provided a donation platform to allow donors to make donations securely. Google partners with the Center for Disaster Philanthropy (CDP), an intermediary organisation that specialises in distributing your donations to local nonprofits that work in the affected region to ensure funds reach those who need them the most.

Google Kerala Donate

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• v.37(3); Jul-Sep 2012

Disaster Management in Flash Floods in Leh (Ladakh): A Case Study

Preeti gupta.

Regimental Medical Officer, Leh, Ladakh, India

Anurag Khanna

1 Commanding Officer, Army Hospital, Leh, India

2 Registrar, Army Hospital, Leh, India

Background:

On August 6, 2010, in the dark of the midnight, there were flash floods due to cloud burst in Leh in Ladakh region of North India. It rained 14 inches in 2 hours, causing loss of human life and destruction. The civil hospital of Leh was badly damaged and rendered dysfunctional. Search and rescue operations were launched by the Indian Army immediately after the disaster. The injured and the dead were shifted to Army Hospital, Leh, and mass casualty management was started by the army doctors while relief work was mounted by the army and civil administration.

The present study was done to document disaster management strategies and approaches and to assesses the impact of flash floods on human lives, health hazards, and future implications of a natural disaster.

Materials and Methods:

The approach used was both quantitative as well as qualitative. It included data collection from the primary sources of the district collectorate, interviews with the district civil administration, health officials, and army officials who organized rescue operations, restoration of communication and transport, mass casualty management, and informal discussions with local residents.

234 persons died and over 800 were reported missing. Almost half of the people who died were local residents (49.6%) and foreigners (10.2%). Age-wise analysis of the deaths shows that the majority of deaths were reported in the age group of 25–50 years, accounting for 44.4% of deaths, followed by the 11–25-year age group with 22.2% deaths. The gender analysis showed that 61.5% were males and 38.5% were females. A further analysis showed that more females died in the age groups <10 years and ≥50 years.

Conclusions:

Disaster preparedness is critical, particularly in natural disasters. The Army's immediate search, rescue, and relief operations and mass casualty management effectively and efficiently mitigated the impact of flash floods, and restored normal life.

Introduction

In the midnight of August 6, 2010, Leh in Ladakh region of North India received a heavy downpour. The cloud burst occurred all of a sudden that caught everyone unawares. Within a short span of about 2 h, it recorded a rainfall of 14 inches. There were flash floods, and the Indus River and its tributaries and waterways were overflowing. As many as 234 people were killed, 800 were injured, and many went missing, perhaps washed away with the gorging rivers and waterways. There was vast destruction all around. Over 1000 houses collapsed. Men, women, and children were buried under the debris. The local communication networks and transport services were severely affected. The main telephone exchange and mobile network system (BSNL), which was the lifeline in the far-flung parts of the region, was completely destroyed. Leh airport was flooded and the runway was covered with debris, making it non-functional. Road transport was badly disrupted as roads were washed away and blocked with debris at many places. The civil medical and health facilities were also severely affected, as the lone district civil hospital was flooded and filled with debris.

Materials and Methods

The present case study is based on the authors’ own experience of managing a natural disaster caused by the flash floods. The paper presents a firsthand description of a disaster and its prompt management. The data was collected from the records of the district civil administration, the civil hospital, and the Army Hospital, Leh. The approach used was both quantitative as well as qualitative. It included data collection from the primary sources of the district collectorate, interviews with the district civil administration and army officials who organized rescue operations, restoration of communication, and transport, mass casualty management, and informal discussions with local residents.

Disaster management strategies

Three core disaster management strategies were adopted to manage the crisis. These strategies included: i) Response, rescue, and relief operations, ii) Mass casualty management, and iii) Rehabilitation.

Response, rescue, and relief operations

The initial response was carried out immediately by the Government of India. The rescue and relief work was led by the Indian Army, along with the State Government of Jammu and Kashmir, Central Reserve Police Force (CRPF), and Indo-Tibetan Border Police (ITBP). The Indian Army activated the disaster management system immediately, which is always kept in full preparedness as per the standard army protocols and procedures.

There were just two hospitals in the area: the government civil hospital (SNM Hospital) and Army Hospital. During the flash floods, the government civil hospital was flooded and rendered dysfunctional. Although the National Disaster Management Act( 1 ) was in place, with the government civil hospital being under strain, the applicability of the act was hampered. The Army Hospital quickly responded through rescue and relief operations and mass casualty management. By dawn, massive search operations were started with the help of civil authorities and local people. The patients admitted in the civil hospital were evacuated to the Army Hospital, Leh in army helicopters.

The runway of Leh airport was cleared up within a few hours after the disaster so that speedy inflow of supplies could be carried out along with the evacuation of the casualties requiring tertiary level healthcare to the Army Command Hospital in Chandigarh. The work to make the roads operational was started soon after the disaster. The army engineers had started rebuilding the collapsed bridges by the second day. Though the main mobile network was dysfunctional, the other mobile network (Airtel) still worked with limited connectivity in the far-flung areas of the mountains. The army communication system was the main and the only channel of communication for managing and coordinating the rescue and relief operations.

Mass casualty management

All casualties were taken to the Army Hospital, Leh. Severely injured people were evacuated from distant locations by helicopters, directly landing on the helipad of the Army Hospital. In order to reinforce the medical staff, nurses were flown in from the Super Specialty Army Hospital (Research and Referral), New Delhi, to handle the flow of casualties by the third day following the disaster. National Disaster Cell kept medical teams ready in Chandigarh in case they were required. The mortuary of the government civil hospital was still functional where all the dead bodies were taken, while the injured were handled by Army Hospital, Leh.

Army Hospital, Leh converted its auditorium into a crisis expansion ward. The injured started coming in around 0200 hrs on August 6, 2010. They were given first aid and were provided with dry clothes. A majority of the patients had multiple injuries. Those who sustained fractures were evacuated to Army Command Hospital, Chandigarh, by the Army's helicopters, after first aid. Healthcare staff from the government civil hospital joined the Army Hospital, Leh to assist them. In the meanwhile, medical equipment and drugs were transferred from the flooded and damaged government civil hospital to one of the nearby buildings where they could receive the casualties. By the third day following the disaster, the operation theatre of the government civil hospital was made functional. Table 1 gives the details of the patients admitted at the Army Hospital.

Admissions in the Army Hospital, Leh

The analysis of the data showed that majority of the people who lost their lives were mainly local residents (49.6%). Among the dead, there were 10.3% foreign nationals as well [ Table 2 ]. The age-wise analysis of the deaths showed that the majority of deaths were reported in the age group 26–50 years, accounting for 44.4% of deaths, followed by 11–25 year group with 22.2% deaths.

Number of deaths according to status of residence

The gender analysis showed that 61.5% were males among the dead, and 38.5% were females. A further analysis showed that more females died in <10 years and ≥50 years age group, being 62.5% and 57.1%, respectively [ Table 3 ].

Age and sex distribution of deaths

Victims who survived the disaster were admitted to the Army Hospital, Leh. Over 90% of them suffered traumatic injuries, with nearly half of them being major traumatic injuries. About 3% suffered from cold injuries and 6.7% as medical emergencies [ Table 4 ].

Distribution according to nature of casualty among the hospitalized victims

Rehabilitation

Shelter and relief.

Due to flash floods, several houses were destroyed. The families were transferred to tents provided by the Indian Army and government and non-government agencies. The need for permanent shelter for these people emerged as a major task. The Prime Minister of India announced Rs. 100,000 as an ex-gratia to the next of kin of each of those killed, and relief to the injured. Another Rs. 100,000 each would be paid to the next of kin of the deceased from the Chief Minister's Relief Fund of the State Government.

Supply of essential items

The Army maintains an inventory of essential medicines and supplies in readiness as a part of routing emergency preparedness. The essential non-food items were airlifted to the affected areas. These included blankets, tents, gum boots, and clothes. Gloves and masks were provided for the persons who were working to clear the debris from the roads and near the affected buildings.

Water, sanitation, and hygiene

Public Health is seriously threatened in disasters, especially due to lack of water supply and sanitation. People having lost their homes and living in temporary shelters (tents) puts a great strain on water and sanitation facilities. The pumping station was washed away, thus disrupting water supply in the Leh Township. A large number of toilets became non-functional as they were filled with silt, as houses were built at the foothills of the Himalayan Mountains. Temporary arrangements of deep trench latrines were made while the army engineers made field flush latrines for use by the troops.

Water was stagnant and there was the risk of contamination by mud or dead bodies buried in the debris, thus making the quality of drinking water questionable. Therefore, water purification units were installed and established. The National Disaster Response Force (NDRF) airlifted a water storage system (Emergency Rescue Unit), which could provide 11,000 L of pure water. Further, super-chlorination was done at all the water points in the army establishments. To deal with fly menace in the entire area, anti-fly measures were taken up actively and intensely.

Food and nutrition

There was an impending high risk of food shortage and crisis of hunger and malnutrition. The majority of food supply came from the plains and low-lying areas in North India through the major transport routes Leh–Srinagar and Leh–Manali national highways. These routes are non-functional for most part of the winter. The local agricultural and vegetable cultivation has always been scanty due to extreme cold weather. The food supplies took a further setback due to the unpredicted heavy downpour. Food storage facilities were also flooded and washed away. Government agencies, nongovernmental organizations, and the Indian Army immediately established food supply and distribution system in the affected areas from their food stores and airlifting food supplies from other parts of the country.

There was a high risk of water-borne diseases following the disaster. Many human bodies were washed away and suspected to have contaminated water bodies. There was an increased fly menace. There was an urgent need to prevent disease transmission due to contaminated drinking water sources and flies. There was also a need to rehabilitate people who suffered from crush injuries sustained during the disaster. The public health facilities, especially, the primary health centers and sub-health centers, were not adequately equipped and were poorly connected by roads to the main city of Leh. Due to difficult accessibility, it took many hours to move casualties from the far-flung areas, worsening the crisis and rescue and relief operations. The population would have a higher risk of mental health problems like post-traumatic stress disorder, deprivation, and depression. Therefore, relief and rehabilitation would include increased awareness of the symptoms of post-traumatic stress disorder and its alleviation through education on developing coping mechanisms.

Economic impact

Although it would be too early to estimate the impact on economy, the economy of the region would be severely affected due to the disaster. The scanty local vegetable and grain cultivation was destroyed by the heavy rains. Many houses were destroyed where people had invested all their savings. Tourism was the main source of income for the local people in the region. The summer season is the peak tourist season in Ladakh and that is when the natural disaster took place. A large number of people came from within India and other countries for trekking in the region. Because of the disaster, tourism was adversely affected. The disaster would have a long-term economic impact as it would take a long time to rebuild the infrastructure and also to build the confidence of the tourists.

The floods put an immense pressure and an economic burden on the local people and would also influence their health-seeking behavior and health expenditure.

Political context

The disaster became a security threat. The area has a high strategic importance, being at the line of control with China and Pakistan. The Indian Army is present in the region to defend the country's borders. The civil administration is with the Leh Autonomous Hill Development Council (LAHDC) under the state government of Jammu and Kashmir.

Conclusions

It is impossible to anticipate natural disasters such as flash floods. However, disaster preparedness plans and protocols in the civil administration and public health systems could be very helpful in rescue and relief and in reducing casualties and adverse impact on the human life and socio economic conditions.( 2 ) However, the health systems in India lack such disaster preparedness plans and training.( 3 ) In the present case, presence of the Indian Army that has standard disaster management plans and protocols for planning, training, and regular drills of the army personnel, logistics and supply, transport, and communication made it possible to immediately mount search, rescue, and relief operations and mass casualty management. Not only the disaster management plans were in readiness, but continuous and regular training and drills of the army personnel in rescue and relief operations, and logistics and communication, could effectively facilitate the disaster management operations.

Effective communication was crucial for effective coordination of rescue and relief operations. The Army's communication system served as an alternative communication channel as the public communication and mobile network was destroyed, and that enabled effective coordination of the disaster operations.

Emergency medical services and healthcare within few hours of the disaster was critical to minimize deaths and disabilities. Preparedness of the Army personnel, especially the medical corps, readiness of inventory of essential medicines and medical supplies, logistics and supply chain, and evacuation of patients as a part of disaster management protocols effectively launched the search, rescue, and relief operations and mass casualty reduction. Continuous and regular training and drills of army personnel, health professionals, and the community in emergency rescue and relief operations are important measures. Emergency drill is a usual practice in the army, which maintains the competence levels of the army personnel. Similar training and drill in civil administration and public health systems in emergency protocols for rescue, relief, mass casualty management, and communication would prove very useful in effective disaster management to save lives and restore health of the people.( 2 – 4 )

Lessons learnt and recommendations

Natural disasters not only cause a large-scale displacement of population and loss of life, but also result in loss of property and agricultural crops leading to severe economic burden.( 3 – 6 ) In various studies,( 3 , 4 , 7 , 8 ) several shortcomings have been observed in disaster response, such as, delayed response, absence of early warning systems, lack of resources for mass evacuation, inadequate coordination among government departments, lack of standard operating procedures for rescue and relief, and lack of storage of essential medicines and supplies.

The disaster management operations by the Indian Army in the natural disaster offered several lessons to learn. The key lessons were:

• Response time is a critical attribute in effective disaster management. There was no delay in disaster response by the Indian Army. The rescue and relief operations could be started within 1 h of disaster. This was made possible as the Army had disaster and emergency preparedness plans and protocols in place; stocks of relief supplies and medicines as per standard lists were available; and periodic training and drill of the army personnel and medical corps was undertaken as a routine. The disaster response could be immediately activated.
• There is an important lesson to be learned by the civil administration and the public health system to have disaster preparedness plans in readiness with material and designated rescue officers and workers.
• Prompt activation of disaster management plan with proper command and coordination structure is critical. The Indian Army could effectively manage the disaster as it had standard disaster preparedness plans and training, and activated the system without any time lag. These included standard protocols for search, rescue, and evacuation and relief and rehabilitation. There are standard protocols for mass casualty management, inventory of essential medicines and medical supplies, and training of the army personnel.
• Hospitals have always been an important link in the chain of disaster response and are assuming greater importance as advanced pre-hospital care capabilities lead to improved survival-to-hospital rate.( 9 ) Role of hospitals in disaster preparedness, especially in mass casualty management, is important. Army Hospital, Leh emergency preparedness played a major role in casualty management and saving human lives while the civil district hospital had become dysfunctional due to damage caused by floods. The hospital was fully equipped with essential medicines and supplies, rescue and evacuation equipments, and command and communication systems.
• Standard protocols and disaster preparedness plans need to be prepared for the civil administration and the health systems with focus on Quick Response Teams inclusive of healthcare professionals, rescue personnel, fire-fighting squads, police detachments, ambulances, emergency care drugs, and equipments.( 10 ) These teams should be trained in a manner so that they can be activated and deployed within an hour following the disaster. “TRIAGE” has to be the basic working principle for such teams.
• Effective communication system is of paramount importance in coordination of rescue and relief operations. In the present case study, although the main network with the widest connectivity was extensively damaged and severely disrupted, the army's communication system along with the other private mobile network tided over the crisis. It took over 10 days for reactivation of the main mobile network through satellite communication system. Thus, it is crucial to establish the alternative communication system to handle such emergencies efficiently and effectively.( 2 , 11 )
• Disaster management is a multidisciplinary activity involving a number of departments/agencies spanning across all sectors of development.( 2 ) The National Disaster Management Authority of India, set up under National Disaster Management Act 2005,( 1 ) has developed disaster preparedness and emergency protocols. It would be imperative for the civil administration at the state and district levels in India to develop their disaster management plans using these protocols and guidelines.
• Health system's readiness plays important role in prompt and effective mass casualty management.( 2 ) Being a mountainous region, the Ladakh district has difficult access to healthcare, with only nine Primary Health Centers and 31 Health Sub-Centers.( 12 ) There is a need for strengthening health systems with focus on health services and health facility network and capacity building. More than that, primary healthcare needs to be augmented to provide emergency healthcare so that more and more lives can be saved.( 7 )
• Training is an integral part of capacity building, as trained personnel respond much better to different disasters and appreciate the need for preventive measures. Training of healthcare professionals in disaster management holds the key in successful activation and implementation of any disaster management plan. The Army has always had standard drills in all its establishments at regular intervals, which are periodically revised and updated. The civil administration and public health systems should regularly organize and conduct training of civil authorities and health professionals in order to be ready for action.( 1 – 4 )
• Building confidence of the public to avoid panic situation is critical. Community involvement and awareness generation, particularly that of the vulnerable segments of population and women, needs to be emphasized as necessary for sustainable disaster risk reduction. Increased public awareness is necessary to ensure an organized and calm approach to disaster management. Periodic mock drills and exercise in disaster management protocols in the general population can be very useful.( 1 , 3 , 4 )

Source of Support: Nil

Conflict of Interest: None declared.

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Natural disasters occur all around the world, in the last two decades these natural disasters have brought sever damages to the world economy. Mostly developing countries bear severe consequences due to these natural disasters. In July 2010, Pakistan faced a massive flood, which affected almost all the countries. The disaster affected all sectors like daily life, transportation, infrastructure, etc., of the country. GOP did not have enough resources to cope with this giant disaster and called for international help. Local and international NGOs participated with GOP in the early phases of recovery. Millions of dollars were given away as the initial impact of this disaster, and GOP and other relief agents have spent other million to provide initial recovery and relief. GOP will need billions of dollars further to continue recovery from the disaster of 2010.

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Managing visiting scholars' program during the covid-19 pandemic.

International mobility outgoing and incoming from almost every university around the world is not just oriented to highly educative standards among them, but to enhance the development of international competences for students, as well as for academics. While students' mobility are mostly an individual effort that implies individual consequences, academics' mobility involve several resources from universities and trigger collective processes such as research collaboration, visiting lecturers, exchange experiences and best practices meetings, plenary sessions, classes, among others. This case study aims to provide insights about how planned activities related for/with visiting international scholars suffer major disruption and collateral damages when an unplanned and unexpected global crisis occurs, which forces them to react immediately under different real-time decisions and nonexistent protocols. The chapter focuses on Latin America, using the case of the Global Business Week organized by Universidad de Monterrey (UDEM) in Mexico, and involving visiting scholars from Peru and Colombia.

Managing E-commerce During a Pandemic: Lessons from GrubHub During COVID-19

The GrubHub Inc, started as a small food ordering service in Chicago in 2004, and has developed into an e-commerce food delivery giant worth over $3 billion. Since its merger with Seamless in 2013, GrubHub has experienced 53% year-over-year growth in revenue. While online food ordering commerce has been expanding over the years, due to the COVID-19, the industry is experiencing an economic shock. Consumers have begun to isolate themselves from outside as much as possible and local shops have been started to close one by one. As a result, demand has been shrinking to services such as GrubHub, even though otherwise would be expected. In order to survive, the company has to employ new measures and devise new ways of conducting business to protect its competitiveness through catering recently changed needs of community due to the pandemic. This case study explains and demonstrates the set of steps that are taken by GrubHub and their effects on its customers, key business partners, shareholder, and stakeholders.

The Role of Communications in Managing a Disaster: The Case of COVID-19 in Vietnam

Despite a ravaging pandemic worldwide, Vietnam managed to contain the local outbreak, partly owing to its carefully implemented risk communications campaign. This chapter investigated the effectiveness of official Vietnam government communications, the sentiment of foreign media reporting on Vietnam, and any challenges. Content analysis was applied to samples from government communications (43 samples); international articles (46); and social media conversations (33). Official government communications were quite accurate, timely, and effective in displaying transparency, employing war symbolism, and shared responsibility, but should more clearly separate between state and expert, offer differing views, and highlight the benefits of compliance. International articles praised the government's viral PSA TikTok video, its transparency, and the netizens' nationalist narratives. While some evidence was found for infodemic, blaming, and heroization, the sample was too small to be conclusive. Future studies should expand the timeframe to a longer duration, quantitatively appraise a wider sampling of social media conversations, and possibly conduct primary interviews with experts, policy makers, and the public.

Passage from the Tourist Gaze to the Wicked Gaze: A Case Study on COVID-19 with Special Reference to Argentina

The Day the World Stopped is a science fiction film that narrates the days of mankind amid an alien invasion headed to avoid the climate change. We made the decision to use a similar title to narrate the facts that precede the outbreak of COVID-19 in Wuhan, China, and its immediate effects on the industry of tourism. Over years, scholars cited John Urry and his insight over the tourist gaze as well as the importance of tourism as a social institution. Of course, Urry never imagined that one day this global world would end. This chapter centers on the needs of discussing the concept of the wicked gaze, which exhibits the end of hospitality, a tendency emerged after 9/11. This chapter punctuates on the decline of hospitality—at least as it was imagined by ancient philosophers—in a way that the tourist gaze sets the pace to a wicked gaze. Whether hospitality and free transit were the foundational values of West, COVID-19, and the resulted state of emergency reveals a new unknown process of feudalization which comes to stay. The chapter is framed based on long-dormant philosophical debates, but given the complexity of this issue, the efforts deserve our attention.

COVID-19 Outbreak in Finland: Case Study on the Management of Pandemics

COVID-19 has created an unprecedented situation for Finland like never before. These are desperate times for Finland. And desperate times need desperate measures. The Government of Finland is pulling out all the stops and doing everything possible in its continued fight against COVID-19 virus. The crisis primarily erupted due to the initial delay in action and lack of preparedness required to tackle this kind of crisis. Communication channels were put to best use by the Finnish Government in an effort to reach out to all the people in Finland. The people living in Finland should strictly follow the guidelines and support the measures by the Government in full tandem to ensure that the COVID-19 virus is defeated and stops further transmission by breaking the chain. This paper portrays different possible trajectories and outcomes associated with the impacts of the pandemic in Finland.

The COVID-19 Crisis Management in the Republic of Korea

Until recently, the business environment was characterized by a world in which nations were more connected than ever before. Unfortunately, the outbreak of coronavirus disease 2019 (COVID-19) has virtually ended the borderless and globalized world we were accustomed to. The World Health Organization (WHO) officially declared COVID-19 a pandemic at a news conference in Geneva on March 11, 2020. The multifaceted nature of this invisible virus is impacting the world at many levels, and this unprecedented pandemic may best be characterized as an economic and health war against humanity. More international cooperation is crucial for effectively dealing with the present pandemic (and future pandemics) because all nations are vulnerable, and it is highly unlikely that any pandemic would affect only one country. Therefore, this case study takes a sociological approach, examining various social institutions and cultural facets (i.e., government, press freedom, information technology [IT] infrastructure, healthcare systems, and institutional collectivism) to understand how South Korea is handling the crisis while drawing important implications for other countries. All aspects of how Korea is handling COVID-19 may not be applicable to other countries, such as those with fewer IT infrastructures and less institutional collectivism. However, its methods still offer profound insights into how countries espousing democratic values rooted in openness and transparency to both domestic and worldwide communities can help overcome the current challenge. As such, the authors believe that Korea's innovative approach and experience can inform other nations dealing with COVD-19, while also leading to greater international collaboration for better preparedness when such pandemics occur in the future. This case study also considers implications for both public policy and organization, and the authors pose critical questions and offer practical solutions for dealing with the current pandemic.

Empowering Patients through Social Media and Implications for Crisis Management: The Case of the Gulf Cooperation Council

Empowered patients are allies to the healthcare system, especially in emergency situations. Social media use has emerged to be a major means by which patients interact with the healthcare system, and in times such as the current COVID-19 situation social media has to play an even greater crisis management role by empowering patients. Social media channels serve numerous beneficial purposes, despite them also being blamed for the spread of misinformation during this crisis. In this Gulf Cooperation Council (GCC) focused case study, we will discuss the increasingly greater role being played by the social media in healthcare in the region and how that empowers not just the patients but the system as a whole. In the GCC region, the healthcare sector is found to reflect a steady growth, leading to an increased drive for empowering patients by lowering the barriers to effective communication and consultation through online media. As of today, social media has become an element of the telehealth infrastructure being deployed in the region. During COVID-19, patients are seen to leverage it pointedly for online health consultations thereby lowering the stress on the healthcare system and adding to efficiencies.

Technology in Medicine: COVID-19 and the “Coming of Age” of Telehealth

Telehealth has been playing a progressively significant role in the management of the COVID-19 crisis. The enforcement of social distancing measures has had the consequence of reduced technology distance in almost every walk of life. In this chapter, based primarily on the still-unfolding experiences of deploying it during the current situation, we argue that telehealth has finally come of age and that it is time to move it from the peripheries to the center of the twenty-first-century healthcare. To provide a live context to the discussion, several instances of how telehealth strengthened our healthcare systems during the COVID-19 crisis are presented.

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In This Section

The program has developed an extensive catalogue of case studies addressing crisis events. These cases serve as an important tool for classroom study, prompting readers to think about the challenges different types of crises pose for public safety officials, political leaders, and the affected communities at large.

The following cases, here organized into three broad categories, are available through the  Harvard Kennedy School Case Program ; click on a case title to read a detailed abstract and purchase the document. A selection of these cases are also available in the textbooks Managing Crises: Responses to Large-Scale Emergencies  (Howitt and Leonard, with Giles, CQ Press) and Public Health Preparedness: Case Studies in Policy and Management (Howitt, Leonard, and Giles, APHA Press), both of which contain fifteen cases as well as corresponding conceptual material to support classroom instruction.

Natural Disasters, Infrastructure Failures, and Systems Collapse

At the Center of the Storm: San Juan Mayor Carmen Yulín Cruz and the Response to Hurricane Maria (Case and Epilogue) This case profiles how Carmen Yulín Cruz, Mayor of San Juan, Puerto Rico, led her City’s response to Hurricane Maria, which devastated the island and neighboring parts of the Caribbean in the fall of 2017. By highlighting Cruz’s decisions and actions prior to, during, and following the storm’s landfall, the case provides readers with insight into the challenges of preparing for and responding to severe crises like Maria. It illustrates how several key factors—including San Juan’s pre-storm preparedness efforts, the City’s relationships with other jurisdictions and entities, and the ability to adapt and improvise in the face of novel and extreme conditions—shaped the response to one of the worst natural disasters in American history.

A Cascade of Emergencies: Responding to Superstorm Sandy in New York City (A and B) On October 29, 2012, Superstorm Sandy made landfall near Atlantic City, New Jersey. Sandy’s massive size, coupled with an unusual combination of meteorological conditions, fueled an especially powerful and destructive storm surge, which caused unprecedented damage in and around New York City, the country’s most populous metropolitan area, as well as on Long Island and along the Jersey Shore. This two-part case study focuses on how New York City prepared for the storm’s arrival and then responded to the cascading series of emergencies – from fires, to flooding, to power failures – that played out as it bore down on the region. Profiling actions taken at the local level by emergency response agencies like the New York City Fire Department (FDNY), the case also explores how the city coordinated with state and federal partners – including both the state National Guard and federal military components – and illustrates both the advantages and complications of using military assets for domestic emergency response operations.

Part B of the case highlights the experience of Staten Island, which experienced the worst of Sandy’s wrath. In the storm’s wake, frustration over the speed of the response triggered withering public criticism from borough officials, leading to concerns that a political crisis was about to overwhelm the still unfolding relief effort.

Surviving the Surge: New York City Hospitals Respond to Superstorm Sandy Exploring the experiences of three Manhattan-based hospitals during Superstorm Sandy in 2012, the case focuses on decisions made by each institution about whether to shelter-in-place or evacuate hundreds of medically fragile patients -- the former strategy running the risk of exposing individuals to dangerous and life-threatening conditions, the latter being an especially complex and difficult process, not without its own dangers. "Surviving the Surge" illustrates the very difficult trade-offs hospital administrators and local and state public health authorities grappled with as Sandy bore down on New York and vividly depicts the ramifications of these decisions, with the storm ultimately inflicting serious damage on Manhattan and across much of the surrounding region. (Included in Howitt, Leonard, and Giles, Public Health Preparedness)

Ready in Advance: The City of Tuscaloosa’s Response to the 4/27/11 Tornado On April 27, 2011, a massive and powerful tornado leveled 1/8 of the area of Tuscaloosa, AL. Doctrine called for the County Emergency Management Agency (EMA) to take the lead in organizing the response to the disaster – but one of the first buildings destroyed during the event housed the County EMA offices, leaving the agency completely incapacitated. Fortunately, the city had taken several steps in the preceding years to prepare for responding to a major disaster. This included having sent a delegation of 70 city officials and community leaders, led by Tuscaloosa Mayor Walter Maddox, to a week-long training organized by FEMA. “Ready in Advance” reveals how that training, along with other preparedness activities undertaken by the city, would pay major dividends in the aftermath of the tornado, as the mayor and his staff set forth to respond to one of the worst disasters in Tuscaloosa’s history.

The Deepwater Horizon Oil Spill: The Politics of Crisis Response (A and B) Following the sinking of the Deepwater Horizon drilling rig in late April 2010, the Obama administration organized a massive response operation to contain the oil spreading across the Gulf of Mexico. Attracting intense public attention, the response adhered to the Oil Pollution Act of 1990, a federal law that the crisis would soon reveal was not well understood – or even accepted – by all relevant parties.

This two-part case series profiles how senior officials from the U.S. Department of Homeland Security sought to coordinate the actions of a myriad of actors, ranging from numerous federal partners; the political leadership of the affected Gulf States and sub-state jurisdictions; and the private sector. Case A overviews the disaster and early response; discusses the formation of a National Incident Command (NIC); and explores the NIC’s efforts to coordinate the actions of various federal entities. Case B focuses on the challenges the NIC encountered as it sought to engage with state and local actors – an effort that would grow increasingly complicated as the crisis deepened throughout the spring and summer of 2010.

The 2010 Chilean Mining Rescue (A and B) On August 5, 2010, 700,000 tons of rock caved in Chile's San José mine. The collapse buried 33 miners at a depth almost twice the height of the Empire State Building-over 600 meters (2000 feet) below ground. Never had a recovery been attempted at such depths, let alone in the face of challenges like those posed by the San José mine: unstable terrain, rock so hard it defied ordinary drill bits, severely limited time, and the potentially immobilizing fear that plagued the buried miners. The case describes the ensuing efforts that drew the resources of countless people and multiple organizations in Chile and around the world.

The National Guard’s Response to the 2010 Pakistan Floods Throughout the summer of 2010, Pakistan experienced severe flooding that overtook a large portion of the country, displacing millions of people, causing extensive physical damage, and resulting in significant economic losses. This case focuses on the role of the National Guard (and of the U.S. military, more broadly) in the international relief effort that unfolded alongside that of Pakistan’s government and military. In particular it highlights how various Guard and U.S. military assets that had been deployed to Afghanistan as part of the war there were reassigned to support the U.S.’s flood relief efforts in Pakistan, revealing the successes and challenges of transitioning from a war-footing to disaster response. In exploring how Guard leaders partnered with counterparts from other components of the U.S. government, Pakistani officials, and members of the international humanitarian community, the case also examines how they navigated a set of difficult civilian-military dynamics during a particularly tense period in US-Pakistan relations.

Inundation: The Slow-Moving Crisis of Pakistan’s 2010 Floods (A, B, and Epilogue) In summer 2010, unusually intense monsoon rains in Pakistan triggered slow-moving floods that inundated a fifth of the country and displaced millions of people. This case describes how Pakistan’s Government responded to this disaster and highlights the performance of the country’s nascent emergency management agency, the National Disaster Management Authority, as well as the integration of international assistance.

"Operation Rollback Water": The National Guard’s Response to the 2009 North Dakota Floods   ( A ,  B , and   Epilogue ) In spring 2009, North Dakota experienced some of the worst flooding in the state’s history. The state's National Guard responded by mobilizing thousands of its troops and working in concert with personnel and equipment from six other states. This case profiles the National Guard’s preparations for and response to the floods and focuses on coordination within the National Guard, between the National Guard and civilian government agencies, and between the National Guard and elected officials.

Typhoon Morakot Strikes Taiwan, 2009 (A, B, and C) In less than four days, Typhoon Morakot dumped close to 118 inches of rain on Taiwan, flooding cities, towns, and villages; washing away roads and bridges; drowning farmland and animals; and triggering mudslides that buried entire villages. With the typhoon challenging its emergency response capacity, Taiwan’s government launched a major rescue and relief operation. But what began as a physical disaster soon became a political disaster for the President and Prime Minister, as bitter criticism came from citizens, the opposition party, and the President’s own supporters.

Getting Help to Victims of 2008 Cyclone Nargis: AmeriCares Engages with Myanmar's Military Government (Case and Epilogue) In May 2008, Cyclone Nargis in Myanmar (Burma) left 138,373 dead or missing and 2.4 million survivors’ livelihoods in doubt, making it the country’s worst natural disaster and one of the deadliest cyclones ever. Friendly Asian countries as well as western governments which previously had used economic sanctions to isolate Myanmar’s military government now sought to provide aid to Myanmar’s people. But they met distrust and faced adversarial relationships from a suspicious government, reluctant to open its borders to outsiders.

China's Blizzards of 2008 From January 10-February 6, a series of heavy snow storms intertwined with ice storms and subzero temperatures created China’s worst winter weather in 50 years. The storms closed airports and paralyzed trains and roads, damaged power grids and water supplies, caused massive black-outs, and left several cities in hard-hit areas isolated and threatened. The disruption of the power supply and transport also severely affected the production and flow of consumer goods and industrial materials, triggering a cascade of crisis nationwide. Coal reserves at power plants were nearly exhausted, production was significantly cut back at big factories, the chronic winter power shortage was exacerbated, and food prices spiked sharply in many areas because of shortages.

Thin on the Ground: Deploying Scarce Resources in the October 2007 Southern California Wildfires  When wildfires swept across Southern California in October 2007, firefighting resources were stretched dangerously thin. Readers are prompted to put themselves in the shoes of public safety authorities and consider how organizations can best address resource scarcities in advance of and during emergency situations.

"Broadmoor Lives:" A New Orleans Neighborhood’s Battle to Recover from Hurricane Katrina (A, B, and Sequel) Stunned by a city planning committee’s proposal to give New Orleans neighborhoods hard-hit by Hurricane Katrina just four months to prove they were worth rebuilding, the Broadmoor community organized and implemented an all-volunteer redevelopment planning effort to bring their neighborhood back to life.

Gridlock in Texas (A and B) As Hurricane Rita bore down on the Houston metro area in mid-September 2005, just a few weeks after Hurricane Katrina had devastated the Gulf Coast, millions of people flocked to the roadways. Part A details the massive gridlock that ensued, illustrating the challenges of implementing safe evacuations and of communicating effectively amidst great fear. Part B explores post-storm efforts to improve evacuation policies and procedures -- and how the resulting plans measured up in 2008, when the area was once again under threat, this time from Hurricane Ike.

Wal-Mart’s Response to Hurricane Katrina: Striving for a Public-Private Partnership (Case and Sequel) This case explores Wal-Mart's efforts to provide relief in the immediate aftermath of Hurricane Katrina, raising important questions about government’s ability to take full advantage of private sector capabilities during large-scale emergencies. (Included in Howitt & Leonard, Managing Crises)

Moving People out of Danger: Special Needs Evacuations from Gulf Coast Hurricanes (A and B ) In the face of Hurricanes Katrina and Rita, officials in Louisiana and Texas grappled with the challenging task of evacuating people with medical and other special needs to safety. The shortcomings of those efforts sparked major initiatives to improve evacuation procedures for individuals requiring transportation assistance – plans that got a demanding test when Hurricanes Gustav and Ike threatened the Gulf Coast in the fall of 2008. (Included in Howitt, Leonard, and Giles, Public Health Preparedness)

Hurricane Katrina:  (A) Preparing for the Big One , and  (B) Responding to an "Ultra-Catastrophe" in New Orleans Exploring the failed response to Hurricane Katrina and its implications for the greater New Orleans area, the case begins with a review of pre-event planning and preparedness efforts. Part B details the largely ineffective governmental response to the rapidly escalating crisis.  (Included in Howitt & Leonard, Managing Crises; Also available in abridged form.)

Rebuilding Aceh: Indonesia's BRR Spearheads Post-Tsunami Recovery (Case and Epilogue) The December 26, 2004, Indian Ocean tsunami caused tremendous damage and suffering on several continents, with Indonesia's Aceh Province, located on the far northern tip of Sumatra Island, experiencing the very worst. In the tsunami's wake, the Indonesian government faced a daunting task of implementing a large-scale recovery effort, and to coordinate the many reconstruction projects that soon began to emerge across Aceh, Indonesia's president established a national-level, ad hoc agency, which came to be known by its acronym BRR. This case examines the challenges encountered by BRR's leadership as it sought to implement an effective recovery process.

When Imperatives Collide: The 2003 San Diego Firestorm   (Case and Epilogue) In October 2003, multiple wildfires burned across southern California. Focusing on the response to the fires, this case explores what can happen when an operational norm — to fight fires effectively but safely — collides with the political imperative to override established procedures to protect the public.  (Included in Howitt & Leonard, Managing Crises)

"Almost a Worst Case Scenario:" The Baltimore Tunnel Fires of 2001 (A, B, and C) When a train carrying hazardous materials derailed under downtown Baltimore, a stubborn underground fire severely challenged emergency responders. Readers are prompted to give particular attention to the significant challenges of managing a multi-organizational response.  (Included in Howitt & Leonard, Managing Crises)

Safe But Annoyed: The Hurricane Floyd Evacuation in Florida When far more citizens than necessary evacuated in advance of Hurricane Floyd, Florida’s roadways were quickly overloaded and emergency management operations overwhelmed. In detailing these (and other) problems, the case highlights the challenges of managing evacuations in advance of potentially catastrophic events. (Included in Howitt & Leonard, Managing Crises)

The US Forest Service and Transitional Fires This case outlines the operational challenges of decision making in a high stress, high stakes situation – in this instance during rapidly evolving wildland fires, also known as "transitional fires." (Included in Howitt & Leonard, Managing Crises)

The Tzu Chi Foundation's China Relief Mission Tzu Chi is one of the largest charities in Taiwan, and one of the swiftest and most effective relief organizations internationally. Rooted in the value of compassion, the organization has many unusual operating features -- including having no long term plan. This case explores the basic operating approach of the organization and invites students to explain the overall effectiveness and success of the organization and its surprising success (as a faith-based, Taiwanese, direct-relief organization -- all of which are more or less anathema to the Chinese government) in securing an operating license in China.

Security Threats

Ce Soir-Là, Ils n'Arrivent Plus Un par Un, Mais par Vagues: Coping with the Surge of Trauma Patients at L'Hôpital Universitaire La Pitié Salpêtrière-Friday, November 13, 2015 On November 13, 2015, Dr. Marie Borel, Dr. Emmanuelle Dolla, Dr. Frédéric Le Saché, and Prof. Mathieu Raux were the doctors in charge of the trauma center at L'Hôpital de la Pitié Salpêtrière in Paris, where dozens of wounded and dying patients, most with severe gunshot wounds from military grade firearms, arrived in waves after a series of terrorist attacks across the city. The doctors had trained for a mass-casualty event but had never envisioned the magnitude of what they now saw. This case describes how they rapidly expanded the critical care capacity available so as to be able to handle the unexpectedly large number of patients arriving at their doors.

Into Local Streets: Maryland National Guard and the Baltimore Riots (Case and Epilogue) On April 19, 2015, Freddie Gray, a young African American male, died while in the custody of the Baltimore Police. In response to his death, protestors mobilized daily in Baltimore to vocalize their frustrations, including what they saw as law enforcement’s long-standing mistreatment of the African American community. Then, on April 27, following Gray’s funeral, riots and acts of vandalism broke out across the city. Overwhelmed by the unrest, the Baltimore police requested assistance from other police forces. Later that evening, Maryland Governor Larry Hogan declared a state of emergency and activated the Maryland National Guard. At the local level, Baltimore Mayor Stephanie Rawlings-Blake issued a nightly curfew beginning Tuesday evening.

“Into Local Streets” focuses on the role of the National Guard in the response to the protests and violence following Gray’s death, vividly depicting the actions and decision-making processes of the Guard’s senior-most leaders. In particular, it highlights the experience of the state’s Adjutant General, Linda Singh, who soon found herself navigating a complicated web of officials and agencies from both state and local government – and their different perspectives on how to bring an end to the crisis.

Defending the Homeland: The Massachusetts National Guard Responds to the 2013 Boston Marathon Bombings On April 15, 2013, Dzhokhar and Tamerlan Tsarnaev placed and detonated two homemade bombs near the finish line of the Boston Marathon, killing three bystanders and injuring more than two hundred others. This case profiles the role the Massachusetts National Guard played in the complex, multi-agency response that unfolded in the minutes, hours, and days following the bombings, exploring how its soldiers and airmen helped support efforts on multiple fronts – from performing life-saving actions in the immediate aftermath of the attack to providing security on the region’s mass transit system and participating in the search for Dzhokhar Tsarnaev several days later. It also depicts how the Guard’s senior officers helped manage the overall response in partnership with their local, state, and federal counterparts. The case reveals both the emergent and centralized elements of the Guard’s efforts, explores the debate over whether or not Guard members should have been armed in the aftermath of the bombings, and highlights an array of unique assets and capabilities that the Guard was able to provide in support of the response.

Recovery in Aurora: The Public Schools' Response to the July 2012 Movie Theater Shooting (A and B) In July 2012, a gunman entered a movie theater in Aurora, Colorado and opened fire, killing 12 people, injuring 58 others, and traumatizing a community. This two-part case briefly describes the shooting and emergency response but focuses primarily on the recovery process in the year that followed. In particular, it highlights the work of the Aurora Public Schools, which under the leadership of Superintendent John L. Barry, drew on years of emergency management training to play a substantial role in the response and then unveiled an expansive recovery plan. This included hiring a full-time disaster recovery coordinator, partnering with an array of community organizations, and holding mental health workshops and other events to support APS community members. The case also details the range of reactions that staff and community members had to APS' efforts, broader community-wide recovery efforts, and stakeholders' perspectives on the effectiveness of the recovery.

"Miracle on the Hudson" (A, B, and C) Case A describes how in January 2009, shortly after takeoff from LaGuardia Airport, US Airways Flight 1549 lost all power when Canada geese sucked into its engines destroyed them. In less than four harrowing minutes, Flight 1549’s captain and first officer had to decide whether they could make an emergency landing at a nearby airport or find another alternative to get the plane down safely. Cases B and C describe how emergency responders from many agencies and private organizations on both sides of the Hudson River – converging on the scene without a prior action plan for this type of emergency – effectively rescued passengers and crew from the downed plane.

Security Planning for the 2004 Democratic National Convention in  Boston (A, B, and Epilogue) When the city of Boston applied to host the 2004 Democratic Party presidential nominating convention, it hoped to gain considerable prestige and significant economic benefits. But convention organizers and local officials were forced to grapple with a set of unanticipated planning challenges that arose in the aftermath of the 9/11 terrorist attacks.  (Included in Howitt & Leonard, Managing Crises)

Command Performance: County Firefighters Take Charge of the 9/11 Pentagon Emergency This case describes how the Arlington County Fire Department – utilizing the Incident Management System – took charge of the large influx of emergency workers who arrived to put out a massive fire and rescue people in the Pentagon following the September 11, 2001, suicide jetliner attack.  (Included in Howitt & Leonard, Managing Crises)

Rudy Giuliani: The Man and His Moment Although not long before the September 11, 2001 terrorist attacks, New York Mayor Rudolph Giuliani had been under fire for aspects of his mayoralty, the post 9/11 Giuliani won national and international acclaim as a leader. This case recounts the details of Giuliani’s response such that students of effective public leadership can analyze both Giuliani’s decisions and style as examples.

Threat of Terrorism: Weighing Public Safety in Seattle (Case and Epilogue) When a terrorist was arrested in late December 1999 at the Canadian-Washington State border in a car laden with explosives, public safety officials worried that the city of Seattle had been a possible target. This case explores the debate that ensued concerning the seriousness of the threat and whether the city should proceed with its planned Millennium celebration.  (Included in Howitt & Leonard, Managing Crises)

Protecting the WTO Ministerial Conference of 1999 (Case and Epilogue) Two very different sets of actors made extensive preparations in advance of the World Trade Organization's Ministerial Conference of 1999 — protesters opposing international trade practices and public safety officials responsible for event security. This case examines the efforts of both, highlighting why security arrangements ultimately fell short.  (Included in Howitt & Leonard, Managing Crises)

The Shootings at Columbine High School: Responding to a New Kind of Terrorism (Case and Epilogue) Within minutes of the shootings at Columbine, numerous emergency response agencies – including law enforcement, fire fighters, emergency medical technicians, and others – dispatched personnel to the school site. Under intense media scrutiny and trying to coordinate their actions, they sought to determine whether the shooters were still active and rescue the injured.

To What End? Re-Thinking Terrorist Attack Exercises in San Jose (Case, Sequel 1, Sequel 2) In the late 1990s, a task force in San Jose, CA mounted several full-scale terrorist attack exercises, but—despite the best of intentions—found all of them frustrating, demoralizing, and divisive. In response, San Jose drew on several existing prototypes to create a new “facilitated exercise” model that emphasized teaching over testing, and was much better received by first responders.

Security Preparations for the 1996 Centennial Olympic Games (A, B, and C) This case describes efforts by state and federal government entities to plan in advance for security protection for the Atlanta Olympics. It also recounts the Centennial Park bombing and emergency response.  (Included in Howitt & Leonard, Managing Crises)

The Flawed Emergency Response to the 1992 Los Angeles Riots (A, B, and C) Following the announcement of the not guilty verdicts for the law enforcement officers accused of beating Rodney King, the City of Los Angeles was quickly overrun by severe rioting. This case reviews how local, county, state, and federal agencies responded and coordinated their activities in an effort to restore order.  (Included in Howitt & Leonard, Managing Crises)

Public Health Emergencies

Mission in Flux: Michigan National Guard in Liberia ( Case and Epilogue ) In summer and fall of 2014, thousands of individuals in Liberia, Sierra Leone, and Guinea contracted the Ebola virus. This outbreak of the deadly disease, which until then had been highly uncommon in West Africa, prompted a major (albeit delayed) public health response on the part of the international community, including an unprecedented commitment made by the United States, which sent almost 3,000 active military soldiers to Liberia. “Mission in Flux” focuses on the US military’s role in the Ebola response, emphasizing the Michigan National Guard’s eventual involvement. In particular, it provides readers with a first-hand account of the challenges the Michigan Guard faced as it prepared for and then deployed to Liberia, just as the crisis had begun to abate and federal officials in Washington began considering how to redefine the mission and footprint of Ebola-relief in West Africa. 

Fears and Realities: Managing Ebola in Dallas   ( Case   and  Epilogue ) “Fears and Realities” describes how public health authorities in Dallas, TX - along with their counterparts at the state and local levels, elected officials, and hospital administrators - responded to the first case of Ebola identified on U.S. soil during the 2014 outbreak of the disease. The hugely difficult tasks of treating the patient and mounting a response was made all the more challenging by confusion over the patient's background and travel history, and, eventually, by the intense focus and considerable concern on the part of the media and public at large. Efforts to curtail the spread of the disease were further complicated when two nurses who had cared for the patient also tested positive for Ebola, even though they apparently had followed CDC protocols when interacting with him. With three confirmed cases of the disease in Dallas – each patient with their own network of contacts – authorities scrambled to understand what was happening and to figure out a way to bring the crisis to an end before more people were exposed to the highly virulent disease.  (Included in Howitt, Leonard, and Giles, Public Health Preparedness)

Confronting a Pandemic in a Home Rule State: The Indiana State Department of Health Responds to H1N1 When Indiana State Health Commissioner Dr. Judy Monroe learned of the emergence of H1N1 in late April 2009, she had to quickly figure out how to coordinate an effective response within a highly balkanized public health system in which more than 90 local health departments wielded considerable autonomy. She would rely heavily on relationships she had worked hard to establish with local health officials upon becoming commissioner -- but she and her senior advisors would still have to scramble to find new ways to communicate and coordinate with their local partners.

On the Frontlines of a Pandemic: Texas Responds to 2009 Novel H1N1 Influenza A  As cases of a new strain of influenza strike in the spring of 2009, Texas, just over the border from the initial epicenter of the epidemic in Mexico, faces great uncertainty about the severity and extent of the epidemic. State officials, presiding over a highly decentralized public health and health care system and needing to work with school systems and other non-health actors, strive to improvise their response to reduce the spread of this disease, while providing anti-viral drugs and, ultimately, a new vaccine to its citizens. (Included in Howitt, Leonard, and Giles, Public Health Preparedness)

Tennessee Responds to the 2009 Novel H1N1 Influenza A Pandemic Tennessee, not so severely struck by H1N1 in the spring of 2009 as some other states, expects to encounter worse in the fall. Working through a hybrid state- and local government-run health system, as well as a network of privately run pharmacies, Tennessee officials mobilize to cope with the expected demand for anti-viral medications and to distribute an expected new vaccine. (Included in Howitt, Leonard, and Giles, Public Health Preparedness)

Harvard Encounters H1N1 In the spring of 2009, as the H1N1 epidemic was beginning to emerge, Harvard University’s medical, dental, and public health schools had to be shut down when a rash of cases and the possibility of widespread exposure emerged among the student body. The case tracks the decision-making by University officials as they cope with the uncertainties surrounding the outbreak of a potentially dangerous emergent infectious disease. (Included in Howitt, Leonard, and Giles, Public Health Preparedness)

Beijing’s Response to the 2009 H1N1 Pandemic In spring 2009, H1N1 emerged in North America and began to spread rapidly throughout the world. Municipal government officials in Beijing, China – who feared a repeat of their painful experience with SARS in 2003 – responded by conducting health screenings at the airport, quarantining people with flu-like symptoms, and scaling capacity at Beijing’s hospitals. The case describes Beijing’s expansive effort to combat H1N1 and is designed to teach students about Beijing’s government as well as China’s public health system.

Keeping an Open Mind in an Emergency: CDC Experiments with 'Team B'   ( Case   and  Epilogue ) In the early 2000s, the US Centers for Disease Control and Prevention (CDC) sought to adapt its protocols for coping with public health emergencies. This case examines the usefulness of one such method, "Team B," which was designed to provide the principal investigating team with alternative explanations for and approaches to the incident at hand.  (Included in Howitt & Leonard, Managing Crises; and Howitt, Leonard, and Giles, Public Health Preparedness)

X-Treme Planning: Ohio Prepares for Pandemic Flu With concern developing about the possibility of a worldwide pandemic of avian flu, the Ohio Department of Health developed plans for how it would handle such an emergency, while at the same time seeking to exercise its nascent incident management system and continue its efforts to develop as an emergency response agency. (Included in Howitt, Leonard, and Giles, Public Health Preparedness)

Emergency Response System Under Duress: Public Health Doctors Fight to Contain SARS in Toronto (A, B, and Epilogue) When an emergent infectious disease arrived in Toronto in 2003, the Canadian public health system struggled to bring it under control. This case explores the efforts of Canadian public health authorities to identify and understand the mysterious illness, which threatened the health — and lives — of Toronto’s residents and healthcare workers for months on end.  (Included in Howitt & Leonard, Managing Crises; and Howitt, Leonard, and Giles, Public Health Preparedness)

Hong Kong Copes with SARS, 2003: The Amoy Gardens (Case and Epilogue) In the last days of March 2003, the frightening new disease known as Severe Acute Respiratory Syndrome, or SARS, seemed to threaten to spread out of control in one of the world’s most densely-populated cities: Hong Kong. The SARS outbreak at Amoy Gardens became an exercise in crisis management for public health officials in Hong Kong—with their counterparts around the world either observing or actively advising.

When Prevention Can Kill: Minnesota and the Smallpox Vaccine Program (Case and Epilogue) Following the 2001 terrorist attacks, President Bush launched a program to vaccinate health workers and emergency responders against smallpox. This case describes that effort, placing particular emphasis on the difficulties that emerged in making that program work in Minnesota. (Included in Howitt, Leonard, and Giles, Public Health Preparedness)

Charting a Course in a Storm: US Postal Service and the Anthrax Crisis This case describes how the USPS responded when it was struck by devastating anthrax attacks through the mails. It covers the initial response to protect employees, efforts to keep the mails moving to the greatest extent possible, and early steps toward decontamination of facilities and recovery.  (Included in Howitt & Leonard, Managing Crises; and Howitt, Leonard, and Giles, Public Health Preparedness)

White Powders in Georgia: Responding to Cases of Suspected Anthrax After 9/11 Although no spore of real anthrax showed up in Georgia during the anthrax attack period, the state was inundated with thousands of calls about suspect white powders. The case describes efforts by local and state officials to develop appropriate procedures to triage and prioritize possible cases, conduct tests of possible anthrax, and protect and reassure worried first responders. (Included in Howitt, Leonard, and Giles, Public Health Preparedness)

The West Nile Virus Outbreak in New York City (A, B, and Sequel) Case A tells how in the summer of 1999 New York City public health officials discovered sentinel cases of a hitherto unknown disease and identified it with assistance from the state, CDC, veterinary pathologists at the Bronx Zoo, and university researchers. Case B and the Sequel describe how the city organized a massive mosquito spraying effort, first in a single borough and then citywide. (Included in Howitt, Leonard, and Giles, Public Health Preparedness)

Anthrax Threats in Southern California This case recounts how California officials responded (and over-responded) to an Anthrax hoax in late 1998, as well as how they then developed protocols of response and disseminated them to multiple jurisdictions. (Included in Howitt, Leonard, and Giles, Public Health Preparedness)

Coping with Crisis: Hong Kong Public Health Officials and the "Bird Flu"  In 1997, public health authorities in Hong Kong worked to identify and control a dangerous new flu virus not previously known to infect humans. The case focuses on the authorities' communication with the public, as they sought to quell public fears notwithstanding their own incomplete knowledge of the disease. The case, too, describes the crisis management decision to undertake a massive slaughter of Hong Kong chickens, once they were shown to be the host of the deadly but difficult-to-transmit virus.

The City of Chicago and the 1995 Heat Wave (A and B) During the summer of 1995, more than 700 people died of heat-related illness in Chicago, Illinois. With most deaths occurring before the city recognized that an “epidemic” was going on, this case explores the silent crisis that overtook the city. (Included in Howitt, Leonard, and Giles, Public Health Preparedness)

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How Haiti Was Devastated by Two Natural Disasters in Three Days

By Tim Wallace ,  Ashley Wu and Jugal K. Patel Aug. 18, 2021

Aug. 14 Epicenter

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Aug. 16 Storm path of Grace

A magnitude-7.2 earthquake struck Haiti Saturday morning, killing more than 1,900 and leaving thousands injured and displaced from their homes. As people in the affected regions in the country’s southwest worked to recover with scarce res ources , a severe storm — Grace, then a tropical depression — drenched Haiti in heavy rain on Monday, bringing with it flash floods and the threat of mudslides , which could further delay recovery.

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Storm batters Haiti

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Path of Tropical

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Very strong shaking

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Although some light shaking from the earthquake could be felt as far as Haiti’s capital, Port-au-Prince, 80 miles from the epicenter, major damage was concentrated in the country’s Nippes, Sud, and Grand’Anse departments. When the shaking subsided, vast swaths of Haiti had ever so slightly moved. The map below shows displaced areas in Haiti, evidence of where the earth shifted after the earthquake.

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A number of homes and school buildings were damaged in Les Cayes, a seaport community about 20 miles from the earthquake’s epicenter. Local hospitals were quickly overwhelmed , and a very limited number of doctors and surgeons worked through the night to triage victims. Temporary operating rooms near the main airport in Les Cayes were erected, as people tried to evacuate their loved ones to Port-au-Prince for emergency care.

Even before the quake, living conditions had been unstable for many Haitians as the pandemic added to severe poverty, gang violence and political trauma — the still-unsolved July 7 assassination of President Jovenel Moïse .

The earthquake also destroyed several churches that have served as sources of aid and stability to surrounding communities, especially to those that receive little support from the government.

Among the collapsed buildings in Les Cayes was Hôtel Le Manguier, where rescue teams continued to dig through the rubble and remove debris in the days after the earthquake hit.

Hôtel Le Manguier in Les Cayes

Jan. 24, 2020

Aug. 15, 2021

People in Les Cayes who lost their homes spent Monday night sheltering under plastic sheets in makeshift camps or fleeing flooded refugee camps as the storm passed through.

Jérémie, the capital city of the Grand’Anse department in Haiti, also suffered severe damage. Just five years ago, Jérémie was hit by Hurricane Matthew , which destroyed a wave of development that had brought hotels, cell phone service and new roads to the previously isolated region. Saturday’s earthquake caused destruction that overwhelmed the city’s main hospital and triggered a landslide that cut off access to the road leading to the city.

Like in Les Cayes, several churches in Jérémie were damaged, including the St. Louis King of France Cathedral, a landmark place of worship in the area that had also been damaged by Hurricane Matthew.

St. Louis King of France Cathedral in Jérémie

Aug. 14, 2020

Petit-Trou-De-Nippes

In Petit-Trou-De-Nippes, just five miles from the earthquake’s epicenter, phone lines were down in the area with no news immediately available. Landslides in nearby cities were recorded, according to the National Human Rights Defense Network, leaving parts of the Nippes department accessible only by motorcycle or sea.

Because of an editing error, an earlier version of this article misspelled the given name of the Haitian president who was assassinated last month. He was Jovenel Moïse, not Juvenel.

The Impacts of Natural Disasters: A Framework for Loss Estimation (1999)

Chapter: 4 conclusions and recommendations, 4 conclusions and recommendations.

This report has explained the gaps in our knowledge of natural disaster losses and why these gaps should be filled. Poor knowledge of the resulting economic losses hinders implementation of effective disaster mitigation policies and emergency response programs. Better loss estimates would benefit federal, state, and local governments, insurers, scientists and researchers, and private citizens (both as taxpayers and insurance purchasers).

It is clear that data on economic losses of natural disasters to the nation are incomplete and spread widely across the public and private sectors. Information on both direct and indirect costs is lacking. If data on uninsured direct losses are limited, our understanding of indirect losses is even more incomplete. These indirect losses are clearly difficult to identify and measure. However, in large disasters they may be significant and, within the immediately affected regions, potentially greater than the direct losses due to physical destruction, especially in large disasters.

Losses Versus Costs

In generating a national indicator of disaster damage, the focus should be upon the losses resulting from disasters, rather than costs. Losses encompass a broader set of damages than costs. Losses include direct physical destruction to property, infrastructure, and crops, plus indirect losses that are the consequence of disasters, such as temporary unemployment and lost business. Costs typically refer only to cash payouts from insurers and governments. The term "losses," as defined above, better portrays the true economic impacts of disasters.

Direct Losses: Data Collection, Reporting, and Agency and Organizational Roles

One step toward producing more complete loss estimates would be to assign one agency of the federal government to compile a comprehensive data base identifying the direct costs of natural disasters, as well as the individuals and groups who bear these costs. These data should be collected according to the framework described in Chapter 2 , for each natural disaster exceeding a given dollar loss threshold. The U.S. Department of Commerce's Bureau of Economic Analysis appears to have the capabilities to compile such a data base, with considerable input and assistance from FEMA and other relevant federal agencies. Whatever agency is selected should be given sufficient resources to accomplish this assignment.

The recommended loss estimate data base would be compiled from many sources, including organizations such as Property Claims Services and the Institute for Business and Home Safety (which compile data on paid insurance claims) and other federal, state, and local agencies. The assistance of relevant professional associations, such as the National Association of Insurance Commissioners, should be enlisted to obtain other relevant data. A synthesis report containing data on disaster losses should be published periodically, preferably annually. One way the federal government might make sure it receives at least the state and local data is by amending the Stafford Act, requiring the data to be submitted as a condition for future federal disaster aid.

A related recommendation is for the federal Office of Management and Budget, with advice from FEMA, to develop annual, comprehensive estimates of the payouts for the direct losses (due directly physical damage) made by federal agencies. These data should be divided into at least four categories:

• compensation payments to individuals and businesses (including subsidies on loans to help cover disaster-related expenses);
• response costs;
• losses to government-owned infrastructure (including state and local costs that are reimbursed by the federal government); and,
• payouts from federal disaster insurance programs (with annual premiums shown separately).

These data should be assembled for some historic period in order to provide information of trends of disaster losses and payouts. Such an effort is critical if the federal government and policymakers are to better plan for future disaster-related expenditures, including mitigation programs and activities.

The largest current gap in direct loss data involves uninsured losses borne by businesses and individuals. These data might be obtained through post-event sampling (in large disasters) and extrapolating these losses from other data

bases. Data from loan applicants to the SBA's disaster relief program or data from insurers like PCS would indicate the deductibles paid by insured businesses and individuals.

Indirect Losses: Modeling the Losses and Constructing a Loss Data Base

Indirect losses in natural disasters stem from the consequences of physical damage (direct losses). Physical damages in disasters typically initiate events that alter economic flows. Businesses may be disrupted after a disaster due to damaged infrastructure (power, water, transportation, communications), and many workers may be temporarily unemployed. These indirect losses have not been studied or measured as closely as direct losses, largely because they are notoriously difficult to identify and accurately measure.

Due to the limited sources of indirect loss data, statistical models are often used to compile indirect loss estimates. Though these models may help address problems due to a lack of available data, they must become more reliable if they are to be used as guides in setting mitigation and other hazard-related policies.

If this is to occur, however, accurate, firsthand (primary) data on indirect losses must be available for model calibration and validation. The recommended data collection and coordination program should thus also include surveys for the collection of detailed primary data on indirect economic losses from recent disasters (again, sufficient resources for this effort must be budgeted). Once a sufficiently reliable data base of these indirect losses has been generated, the agency should continue to collect indirect loss data on large disasters—those with model estimates of greater than $10 billion in losses. While the indirect loss data base is being constructed, efforts toward more effective uses of secondary data (data generated for purposes other than indirect loss estimation, such as unemployment insurance payouts) should be continued. We thus recommend that an assessment of methods for estimating indirect losses with secondary data be conducted.

It is important to understand the timing of economic disruptions that trigger indirect losses in order to plan for efficient emergency responses and to assess the cost-effectiveness of alternate mitigation strategies. The committee recommends that a microsimulation model be developed to create a timeline of regional commercial and industrial closures. Other models that should be devised include a formal restoration model and a comprehensive indirect loss model.

Moving Toward Better Knowledge of Disaster Losses

The lack of accurate information on these losses is a barrier to more effective hazard mitigation. As a step toward improving mitigation programs, efforts at centralizing these data and compiling better loss estimates must be strengthened. The federal government and private sector should combine their knowledge and data in providing better estimates of direct losses. The federal government must mount and back a significant data collection and research effort if better estimates of losses due to disasters are to be compiled, especially indirect losses. With a strong commitment, this could be accomplished within the next ten years. Until relatively accurate estimates are available, the true economic losses in natural disasters will remain poorly understood and the benefits of disaster mitigation activities only imprecisely evaluated.

We in the United States have almost come to accept natural disasters as part of our nation's social fabric. News of property damage, economic and social disruption, and injuries follow earthquakes, fires, floods and hurricanes. Surprisingly, however, the total losses that follow these natural disasters are not consistently calculated. We have no formal system in either the public or private sector for compiling this information. The National Academies recommends what types of data should be assembled and tracked.

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• Published: 20 July 2023

Global online social response to a natural disaster and its influencing factors: a case study of Typhoon Haiyan

• Shi Shen   ORCID: orcid.org/0000-0001-9126-229X 1 , 2 ,
• Ke Shi 2 , 3 ,
• Junwang Huang 1 , 2 ,
• Changxiu Cheng 1 , 2 &
• Min Zhao 2  

Humanities and Social Sciences Communications volume  10 , Article number:  426 ( 2023 ) Cite this article

1496 Accesses

1 Citations

Metrics details

• Cultural and media studies

The global public interest in a natural disaster event will help disaster-stricken areas obtain post-disaster international relief and assistance. However, knowledge gaps still exist in regard to global online social responses and their socioeconomic influencing factors. We used big social media data regarding the 2013 Super Typhoon Haiyan to explore global online social responses and to investigate the socioeconomic factors influencing this behavior based on the Geographical Detector (Geodetector) model and geographically weighted regression (GWR) model. The results show that global online social responses have little relation with geographical distance and follow the disaster’s development. In addition to the most response in the disaster-affected countries, Western countries and neighboring countries have more online social response to the disaster than other regions. Among all the influencing factors, economic factors have the strongest effect on public interest both before and after the typhoon’s landfall. Our findings indicate that online social users are of great potential for volunteers and donors.

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Introduction.

Natural disasters frequently occur around the world and bring massive casualties, economic losses, and social disruptions to countries and international society (Shen et al., 2018 ; United Nations Office For Disaster Risk Reduction ( 2020 )). The synergies between disaster risk reduction and sustainable development require global efforts (Aitsi-Selmi et al., 2016 ). Especially for developing countries and less developed regions, international humanitarian assistance and foreign governmental aid are particularly essential for disaster risk reduction and mitigation (Cook et al., 2018 ). In addition to governmental rescuing relief and financial and economic assistance, donations from the foreign private sector are critical resources for disaster-stricken countries (Coppola, 2020 ). Evidence has confirmed the significant impact of disasters on global public interest and responses (David et al., 2016 ; Tan and Maharjan, 2018 ; Kam et al., 2021 ).

Online social responses are the main reflection of public interest, especially on a global scale. Due to the unbelievably rapid progress in information and communication technologies and personal mobile devices, online social networks have become a vital and primary channel of information communication and dissemination (Tang et al., 2021 ). Before, during, and after the occurrence of a disaster event, global social media users are highly active in posting, discussing, and forwarding the situation (Kumar, 2020 ). In this manner, information related to a local disaster event can be enhanced and immediately disseminated to and spread through international society. Consequently, a local natural disaster, especially a catastrophic disaster, will provoke global social responses and attract more significant public interest through online social networks (Ruan et al., 2022 ).

Thus, aid agencies and humanitarian organizations are increasingly aware of the significance of communication during natural disasters, which is conducive to not only the effective implementation of relief but also timely social responses, and it can stimulate the enthusiasm of donors even in remote countries (Kam et al., 2021 ). Understanding global social responses to disaster events and the influencing factors will help integrate global society, enterprises, nongovernmental organizations and other social forces to participate in humanitarian assistance. This is of great significance and key to post-disaster reconstruction for impoverished countries.

Nevertheless, research on global public interest in or online social responses to a disaster is still a challenging issue, and it is even more difficult on a global scale. Existing research on online social responses follows three narratives: the emotions of social media users, the themes of social media posts, and the Twitter communication mechanism.

In a study on users’ emotions after a disaster, Chen et al. ( 2020 ) explored the emotions and Twitter forwarding patterns of affected and nonaffected areas before and after the disaster, revealing the significant impact of public emotional expression on the release and redistribution of disaster-related information. Later, scholars further studied the spatial and temporal distribution characteristics of people’s negative emotions caused by disasters. Garske et al. ( 2021 ) used the global and local Moran’s I to analyze Twitter data with geographic references and negative emotions and found that all negative emotions were clustered in the local space during a disaster. Gruebner et al. ( 2018 ) used an ordinary least squares regression model to evaluate the association between pre-disaster and post-disaster discomfort rates and to detect spatial clusters of negative emotions in various administrative regions of New York City. In addition, some scholars have combined machine learning methods to assess regional post-disaster reconstruction recovery by studying the emotions and perspectives of social media users after a disaster (Yan et al., 2020 ; Contreras et al., 2022 ).

In research on the posting themes of social media users, García-Ramírez et al. statistically analyzed the posting themes of social media users only in the disaster response stage (García-Ramírez et al., 2021 ). Brandt et al. analyzed the posting themes of social media users during the pre-disaster, disaster occurrence, and post-disaster short-term and long-term periods (Brandt et al., 2019 ). Zhang and Cheng used a machine learning method to classify social media data, explored the change process of the public’s discussion topics at different stages during disasters, and analyzed the differences in people’s discussion content under different emotions (Zhang and Cheng, 2021 ). Some scholars have also studied different themes of online social response when disasters occur. Correlation analyses demonstrate that there are differences in themes between different genders and between black and white groups when extreme disasters occur (Yuan et al., 2020 ; Zhu and Liu, 2021 ).

Regarding the transmission mechanism of Twitter, Takahashi et al. investigated the use of Twitter when and after Typhoon Haiyan hit the Philippines and explored the external factors (using time and geographical location) and internal factors affecting the use of social media (Takahashi et al., 2015 ). However, the area covered by this study covered only affected countries. Kam et al. ( 2021 ) established a model to simulate global attention to disaster events by using the relative search activities of users of Google products, revealing that Western countries play a dominant role in global attention to disasters.

However, there is still a lack of understanding of global online social responses to disaster events and their influencing factors. Applications of social media data in the global public interest to disaster events have not been well investigated due to the lack of typical datasets. To fill this knowledge gap, this study takes the 2013 Super Typhoon Haiyan, a historic deadly disaster in the western Pacific, as a representative case to investigate the spatiotemporal changes in global social responses and to attribute their socioeconomic influencing factors in various countries.

Data and methods

Overview of typhoon haiyan.

Super Typhoon Haiyan is one of the most representative extreme disaster events. As the most powerful typhoon (maximum sustained winds near the center of 315 km/h) ever recorded in the western Pacific, it swept across the Philippines and other regions (Fig. 1 ) and caused severe losses to the Philippines. According to official statistics, the direct economic loss suffered by the Philippines was approximately 8.96 billion Philippine pesos (approximately 1.63 billion current US dollars), and more than 16 million people were affected by the disaster (NDRRMC Philippines, 2013 ). Due to its historic intensity and damage power, Super Typhoon Haiyan attracted global attention and public interest, and information flooded Twitter (David et al., 2016 ; Shen et al., 2021 ).

The points indicate the center of Typhoon Haiyan. The dash line represents the moving route. The dash line boundary shows the influencing range.

The track of Typhoon Haiyan is shown in Fig. 3 . Typhoon Haiyan generated and developed rapidly in the South Pacific Ocean on November 4, 2013. It was officially upgraded to a tropical storm. On November 8, Typhoon Haiyan made landfall along the coast of Guiuan in the Philippines with extremely destructive winds, causing devastating damage to the country. Typhoon Haiyan entered the central South China Sea on November 9, and its intensity significantly weakened. Affected by the circulation and the northeast monsoon, Typhoon Haiyan turned to the north on November 10 and attacked the Hainan Province of China. On November 11, due to the further influence of topography and wind direction, the intensity of Typhoon Haiyan rapidly weakened, and it gradually dissipated.

Data and preprocessing

Tweets were obtained from the Twitter platform using disaster hashtags and web crawler scripts based on the method and data provided in previous publications (Murzintcev and Cheng, 2017 ; Shen et al., 2021 ; Zhang et al., 2021 ). The resulting global tweets related to Typhoon Haiyan from November 4 to 20, 2013, totaled 234,042. The data contain the location attributes or mobile phone location addresses of social media users and were then geo-decoded into points through ArcGIS 10.7 software.

In total, there are 113 countries included in the following attributional analysis of this study (Fig. 2 ). Countries were filtered out based on two criteria: (1) tweets from a country are missing or number less than 30; and (2) the subsequent acquisition of relevant data on influencing factors. In addition, due to a very low penetration rate of Twitter users, China was excluded from this study. Therefore, the countries in the attribution analysis include 8 South American countries, 10 countries in North America, 27 countries in Asia, 33 European countries, 33 countries in Africa, and two Oceania countries (Australia and New Zealand).

Red dots are the global located tweets regarding to Typhoon Haiyan.

The influencing factors and data sources in this study are shown in Table 1 . In the era of globalization, the interpretation of online social responses to disasters is quite complex. They interact with political power, surrounding social relations and cultural significance and the process of global interdependence (Kam et al., 2021 ). Therefore, this paper explores the political, economic, social, cultural, natural and demographic factors that affect the differences in online social responses to a disaster in different countries. All of the specific factors and corresponding indices are 2013 values.

(1) Political dimension: The government effectiveness can be used to measure the quality of public and civil services, independence from political pressure, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies around the world (Sadaf et al., 2018 ). People in high government effectiveness countries have more willingness to express their response to natural disasters.

(2) Economic dimension: The level of economic development can reflect people’s capability to aid foreign countries in a country. The economic development of a country can be measured by per capita GDP. If a country is more connected to other countries, its citizens are more interested in international events. Therefore, exports of goods and services are selected to measure the degree of trade openness.

(3) Social dimension: Transparent countries advocate the construction of a harmonious and mutually supportive society (Raschky and Schwindt, 2012 ). The corruption perceptions index (CPI) can be used to measure the social development of a country and the position of the national public toward foreign aid. Citizens of countries with higher levels of social development may have more empathy and willingness to respond to foreign disasters.

(4) Cultural dimension: Culture affects people’s willingness to call for help and their views on aid, and it is an important background for people to understand and respond to disasters (Hoffman and Oliver-Smith, 2002 ). The level of education explains the level of public awareness of disasters, which is measured by the higher education enrollment rate here (Zhang and Cheng, 2021 ). In addition, different religions have different understandings of humanitarianism, which will also affect people’s response to disasters to some extent. Here, religious influence is measured by the proportion of mainstream religious beliefs (i.e., Christianity, Islam, Buddhism, Hinduism) and the proportion of nonreligious beliefs in a country.

(5) Natural dimension: The risks of natural disasters in a country will affect the sensitivity and attention of domestic people to extreme disaster events. The expected death rate, the affected population rate and the GDP loss rate can be used to measure the comprehensive natural disaster risk level of each country. In addition, distance is an obstacle to generously responding to others’ needs or caring actions, and it is one of the determinants of online social responses to disaster events. The geographical distance between countries and disaster places is expressed by the Euclidean distance from different country capitals to Manila calculated with ArcGIS.

(6) Demographic dimension: The social media user base of a country will affect people’s response to disasters. Due to the lack of precise accounts of Twitter users in each country in 2013, a country’s total population is employed as a proxy index to indicate the number of social media users.

Research workflow

The specific research flow of this study is shown in Fig. 3 . In this study, we used the number of disaster-related tweets posted over a period to represent the online social response to a disaster. Hence, the daily online social response was obtained by calculating the number of daily tweets issued in a region using the zonal statics tool in ArcGIS 10.7.

The research framework of the study.

We calculated the daily global and country-wide online social response to analyze the spatiotemporal evolution of online social responses around the world. Based on its temporal changes, the research period is divided into two stages: before and after the typhoon lands. Then, the online social response changes before and after the typhoon lands are mapped and revealed by calculating the number of tweets and the relative and absolute differences in the two stages.

This study focuses on investigating the influencing factors of online social responses. First, we selected indicators combined with the literature, collected data (details in Section 2.3), conducted a single-factor analysis of the differences in online social responses to disasters in various countries using the Geographical Detector (Geodetector), and then selected the leading factors for the geographically weighted regression (GWR) model for multifactor analysis. Finally, the spatial differentiation of influencing factor coefficients and the variation in the influencing factor coefficients before and after typhoon landing are discussed.

Standard deviation ellipse

In this paper, the standard deviation ellipse is used to quantitatively describe the spatial distribution characteristics of global social media users’ attention to extreme disaster events. This method can explain the spatial distribution characteristics of geographical elements from global and spatial perspectives (Lefever, 1926 ). Its calculation formula is as follows:

In the formulas, x i and y i are the coordinates of the i th subregion. \(\bar{x}\) and \(\bar{y}\) represent the coordinates of the center of gravity of subregion i . n is the total number of subregions.

Geographical detector

There are a large number of factors selected in attribution analysis, and multicollinearity among factors easily occurs; leading factors and invalid factors cannot be filtered out. Therefore, this paper utilized the Geodetector model (Wang et al., 2010 ) to explain the driving forces influencing online social responses to disaster events in various countries. Its calculation formula is as follows:

In the formula, q is the measurement factor of the explanatory power of the independent variable, and the range is [0,1]. L is the independent variable. N and N h are the number of samples in the whole study area and layer h , respectively. σ h 2 and σ 2 are the variances in the Y value of layer h and the whole study area, respectively.

Geographically weighted regression

In this paper, before fitting the GWR model, it is necessary to use spatial autocorrelation to test whether there is an agglomeration of an attribute in space. Moran’s I index is generally used to describe the spatial characteristics of the distribution of an attribute in the study area (Moran, 1950 ). Its calculation formula is as follows:

In the formula, x i is the observed value of country i . W ij is the spatial weight matrix.

GWR is a local form of regression that models the spatial heterogeneity of relations (Brunsdon et al., 1996 ). With the change in local geographical position in space, the estimated parameters are also different. The calculation formula is as follows:

In the formula, y i is the dependent variable of sample i . ( u i , v i ) are the latitude and longitude coordinates of sample i . β 0 ( u i , v i ) is the regression constant of sample i . β k ( u i , v i ) is the coefficient of the k th independent variable of sample i . p is the number of independent variables. x ik is the k th independent variable of sample i . ε i is the random error of the model.

Temporal evolution of global online social responses

Figure 4 shows the temporal evolution of the total number of global daily tweets related to Typhoon Haiyan from November 4 to 20. The number of tweets increased significantly from November 4 to 8 and decreased from November 9 to 20, with a slight increase from November 10 to 11. The trend of global online social response followed the development of Typhoon Haiyan. Typhoon Haiyan generated and developed rapidly in the South Pacific Ocean on November 4, 2013, and was soon upgraded to a tropical storm and quickly attracted widespread interest from social media users around the world. On November 8, Typhoon Haiyan landed along the coast of Guiuan, the Philippines, with the highest wind speed, caused thousands of casualties and heavy economic losses, and attracted the highest global interest. From November 9 to 20, the intensity of Typhoon Haiyan gradually weakened, it moved throughout the Philippines, and global attention to it gradually decreased.

Solid line shows the changes of wind speed around the typhoon center. Dash line demonstrates the daily number of tweets regarding to Typhoon Haiyan.

There are two reasons for the increase in global online social responses from November 10 to 11. First, the weakening Typhoon Haiyan attacked the Hainan Province of China during this period and caused minor casualties, which again attracted global attention. Second, as disaster relief work was in full swing after the disaster, global social media users increased their attention and actively provided humanitarian relief (Zhang and Cheng, 2021 ). In short, the response of global social media users to extreme disaster events will change with the intensity of disasters and the severity of damage caused in the process.

Moreover, we carried out a clustering analysis using the Euclidean distance and the connection between groups to obtain the time clustering tree diagram (Supplementary Fig. S1 ). Based on Supplementary Fig. S1 , online social responses were divided into four categories: November 4–7, November 8, November 9–13, and November 14–20. These four types are closely related to the evolutionary process of Typhoon Haiyan. November 4–7 is the development stage, November 8 is the peak stage, November 9–13 is the extinction stage, and November 14–20 is the complete dissipation stage. Therefore, in the following analysis, the response of social media users to disasters around the world was divided into two stages: before (November 4–8) and after (November 9–20) Typhoon Haiyan’s landing.

Spatial pattern changes in global online social responses

The online social responses around the world to Typhoon Haiyan are roughly the same in the spatial distribution before and after the typhoon’s landing (Fig. 5 ). Users in the Philippines, the main affected country, had the strongest responses to Typhoon Haiyan. Additionally, users in developed countries in Europe and North America paid more attention to Typhoon Haiyan, mainly because citizens in developed countries had greater capabilities, resources, and capital for disaster relief than those in developing countries. After the Philippines, users in the U.S. had the greatest interest in Typhoon Haiyan. The reason may be that the Philippines was once a colony of the U.S., and there were more common political and economic values, as well as stronger social and cultural connections, between these two countries and their citizens. Due to geographical proximity, neighboring countries (e.g., Australia, Vietnam, and Indonesia) paid relatively strong attention to Typhoon Haiyan. Africa and West Asia had the fewest responses to Typhoon Haiyan.

a The global distribution of the amount of tweets before the landfall of Tyhoon Haiyan. b The global distribution of the amount of tweets after the landfall of Tyhoon Haiyan.

By calculating the relative and absolute differences in tweets before and after Typhoon Haiyan’s landing, we analyzed the spatial distribution of online social response differences around the world. As shown in Fig. 6 , most countries had more responses to disaster events after the typhoon made landfall. Among them, Bhutan, Botswana, Luxembourg, Mongolia, Uruguay, and Nigeria saw increases of 39 times, 15 times, 12.5 times, 12 times, 11.6 times, and 11.4 times, respectively, compared to the countries before Typhoon Haiyan’s landing. However, some small countries saw decreases in responses. For instance, in Andorra, Guyana, Seychelles, North Korea, and Papua New Guinea, responses decreased by 50%, 50%, 50%, 43% and 10%, respectively, compared with those before the typhoon’s landing. The online social responses in Liechtenstein, Chad, Antigua and Barbuda even dropped to zero. This may be because these countries have weak national strength and are unable to provide more attention and assistance to the outside world. In addition, some countries restrict the use of Twitter, making it impossible to truly reflect the attention of domestic social media users to Typhoon Haiyan.

a The global distribution of the absolute differences of tweets before and after the landfall of Tyhoon Haiyan. b The global distribution of the relative differences of tweets before and after the landfall.

Factor detection of online social responses to the disaster

The results of single factors affecting online social responses before Typhoon Haiyan’s landing are listed in Table 2 . Overall, economic factors have the greatest influence on the response, while natural, demographic and cultural factors have a relatively weak influence. The social factors are the weakest, while the political factors are not significant. The impact of economic factors may be due to the social and geographical disparities in the use of Twitter, and areas with higher disaster-related Twitter usage are generally communities with better socioeconomic conditions (Zou et al., 2018 ).

Specifically, six factors have significant impacts on online social responses. According to the q values, their explanatory power is, in descending order, export value, the GDP loss rate, total population, per capita GDP, the proportion of nonreligious beliefs, and the CPI. Among them, export value, the CPI, the proportion of nonreligious beliefs, the GDP loss rate, and population are higher than other factors and are all significant at the 0.05 level, indicating that these influencing factors have major explanatory power in regard to the dependent variable.

Table 2 shows that the major factors do not change much after landing. Economic factors still have the greatest influence on the response in various countries, while natural, demographic, cultural, and social factors have a weak influence. All six factors are significant at the 0.01 level. In addition, political factors have the weakest but most significant influence. Based on the q value, the explanatory power of these factors is ranked as follows: export value, per capita GDP, the GDP loss rate, population, the proportion of nonreligious beliefs, the CPI, and the DI.

Comparing the dominant factors before and after the typhoon’s landing, we find that the difference lies in whether political factors are included. Before and during the disaster, social media users focused more on reporting the progress of the disaster, the extent of damage, property losses and other disaster situation facts. Tweets mainly reflected users’ views, attitudes, and thoughts on reports of disaster events and the official emergency response. Therefore, the political effect is relatively weak in the five target layers. In contrast, during the post-disaster period, users focused more on disaster recovery and international humanitarian assistance. Governments in more democratic countries tended to be more inclined to help disaster-hit countries. Moreover, the actions taken by the national government attract the attention and assistance of the local people. Therefore, compared with the stage before and during the disaster, the dominant factor in the post-disaster stage involved political factors.

Attributing multiple factors affecting online social responses before landfall

After the normalization of the dominant factor and the dependent variable, the GWR model was regressed, and the results are summarized in Table 3 . Only one factor was selected as the dominant factor of each target layer to achieve dimensionality reduction of the influencing factor to obtain more accurate research conclusions and avoid the repeatability and inefficiency of the research (Garske et al., 2021 ). Nevertheless, both per capita GDP and export value could represent the economic target layer. The export value was selected since it can also reflect a country’s connection to others. The R 2 values of the two research stages (before and after landing) are 0.94 and 0.90, respectively, indicating that the model fitting effect is good.

As shown in Table 3 , the coefficients of the factors are both positive and negative, indicating that these five influencing factors fluctuate greatly in space and have an unstable influence on the interest of social media users.

Overall, from Fig. 7 , export values and population have a more positive influence on online social responses around the world. CPI and the proportion of nonreligious beliefs generally have a negative influence on the online social response in many countries. The GDP loss rate has a complicated influence on the online social response which has a positive influence in North America, North Africa, and Russia but a negative impact in other regions.

a Export value, b CPI, c proportion of nonreligious beliefs, d GDP loss rate, e population.

Regarding spatial distributions, the coefficients of export value are overwhelmingly positive around the world (Fig. 7a ). This pattern indicates that the economic and trade links between countries have positive impacts on the online social responses to natural disasters. In detail, the coefficients are relatively high in North and South American countries. The coefficients of Western Europe, West Africa, East Africa, Oceania, and Southeast Asia follow. Countries in Eastern Europe and Southern Africa have lower coefficients. In addition, exports of goods and services have only a significant negative effect in Iceland. This exception may be due to its few economic connections to the Philippines.

Figure 7b indicates that the CPI has both positive and negative effects on online social responses in different countries. The countries with higher influencing factor coefficients are mainly distributed in Western Europe, among which Iceland, the U.K., France, and Switzerland have the highest influencing factor coefficients, i.e., 0.13, 0.06, 0.06, and 0.05, respectively. Countries with lower coefficients of the CPI are mainly distributed in North America and northern South America. In addition, the CPI has positive effects in 82.3% of countries, which are mainly distributed in North Africa, Oceania and Europe.

The coefficients of the proportion of nonreligious beliefs have both positive and negative effects on the social response to disasters in different countries (Fig. 7c ). From the perspective of the spatial distribution, the countries with higher coefficients of the proportion of nonreligious beliefs are mainly distributed in southern South America and Central Africa, among which Madagascar, Lesotho, Argentina, Malawi, and Costa Rica have the highest influencing factor coefficients, i.e., 0.07 for all these countries. Countries with lower coefficients of the proportion of nonreligious beliefs are mainly distributed in northern North America and West Africa. In addition, the proportion of nonreligious beliefs showed a negative influence in 34.5% of countries, mainly distributed in North America, western South America, Western Europe, and West Africa. Although the proportion of nonreligious beliefs in these countries is not low, the religious belief in these regions is mainly Christianity. This may be due to the characteristics of Christianity’s religious belief, leading to the positive effect of the proportion of unbelief in these regions.

The GDP loss rate has a positive effect on the responses in most countries (Fig. 7d ), indicating that countries prone to natural disasters and vulnerable carriers are more sensitive to extreme disaster events, and social media users will have more interest in disasters. The countries with higher coefficients of the GDP loss rate are mainly distributed in Eastern Europe, among which Ukraine, Russia, and Lithuania have the highest influencing factor coefficients, i.e., 0.33, 0.32, and 0.31, respectively. Countries with lower coefficients of the GDP loss rate are mainly distributed in Western Europe, among which Switzerland, the Netherlands, France and the United Kingdom have the lowest influencing factor coefficients, i.e., −0.17, −0.15, −0.15, and −0.12, respectively. In addition, the GDP loss rate of 11.5% of countries has negative effects; the countries are mainly distributed in Western Europe, Oceania, and Southeast Asia. It may be that these countries are close to the disaster area and have a relatively high level of natural risk; thus, users in these countries pay more attention to calming people’s emotions rather than the disaster itself.

The population has a generally positive effect on online social responses to disasters in various countries (Fig. 7e ), indicating that countries with a large population base will pay more active responses to extreme disaster events. The countries with higher coefficients of population are mainly distributed in Western Europe, among which Iceland, the U.K., France and Switzerland have the highest influencing factor coefficients, i.e., 3.27, 1.47, 1.47, and 1.23, respectively. Countries with lower influencing factor coefficients are mainly distributed in North and South America, among which Chile, Peru, and Brazil have the lowest influencing factor coefficients, i.e., −0.91, −0.86, and −0.84, respectively.

Attributing multiple factors affecting online social responses after landfall

The GWR coefficients of each influencing factor after landing are statistically summarized in Table 4 and shown in Fig. 8 . The coefficient statistics of export value, the CPI, the proportion of nonreligious beliefs, the GDP loss rate, and population are similar to the situation before landing. The coefficient value of the GE of the increased influencing factor is positive and negative, indicating that the influencing factor also fluctuates in space and its effect is changeable.

a GE, b export value, c CPI, d proportion of nonreligious beliefs, e GDP loss rate, f population.

Figure 8a shows that the influencing factor coefficient of the GE has significant spatial heterogeneity, with values ranging from approximately −0.16 to 0.10. These results indicate that most of GE has positive and negative effects on the attention of social media users to disasters. From the perspective of spatial distribution, the countries with high influencing factor coefficients are Australia (0.10) and European countries, such as Norway (0.98), Denmark (0.96), Sweden (0.94), and Germany (0.92). Countries with lower influencing factor coefficients are mainly distributed in American countries, among which Mexico, Ecuador, Guatemala, El Salvador and Costa Rica have the lowest five influencing factor coefficients, i.e., −0.16, −0.15, −0.14, −0.14, and −0.14, respectively. In addition, the negative effects of GE are mainly distributed in the Western Hemisphere and Western Africa.

The export value is positively correlated with the attention of social media users to the disaster and is generally consistent with the distribution pattern of the influencing factor coefficient of export value before the typhoon made landfall (Fig. 8b ). However, the influencing factor coefficient changed. Chile, Peru, Argentina, Uruguay, and Brazil had the five highest influencing factor coefficients, i.e., 1.24, 1.23, 1.22, 1.21 and 1.19, respectively. Iceland, Indonesia, Malaysia, Ireland, and Cambodia had the lowest five coefficients, i.e., −0.15, 0.02, 0.04, 0.05 and 0.06, respectively.

The CPI is positively and negatively correlated with the attention of social media users to the disaster (Fig. 8c ) and changes with the distribution pattern of the influencing factor coefficient of the CPI before the typhoon’s landing. Canada, U.S., Mexico, Guatemala, and El Salvador had the highest influencing factor coefficients, i.e., 0.19, 0.16, 0.14, 0.13, and 0.13, respectively. Sweden, Finland, Estonia, Latvia and Norway had the lowest influencing factor coefficients, i.e., −0.07, −0.06, −0.06,−0.06, and −0.06, respectively.

The proportion of nonreligious beliefs has both positive and negative correlations to the attention of social media users to the disaster (Fig. 8d ) and is basically consistent with the pattern of the influencing factor coefficient of the proportion of nonreligious beliefs before the typhoon’s landing. However, the influencing factor coefficient changed. Iceland, Brazil, Uruguay, Kenya, and Tanzania had the highest influencing factor coefficients, i.e., 0.025, 0.010, 0.008, 0.008, and 0.008, respectively. Cuba, Jamaica, Mexico, Guatemala, and Honduras had the lowest influencing factor coefficients, i.e., −0.15, −0.14, −0.14, −0.13 and −0.13, respectively.

The GDP loss rate is positively correlated with the attention of social media users to the disaster (Fig. 8e ) and is different from the distribution pattern of the influencing factor coefficient of the GDP loss rate before the typhoon’s landing. The influencing factor coefficient changed. Iceland, Ireland, Canada, the U.S., and Cuba had the highest influencing factor coefficients, i.e., 1.53, 0.47, 0.35, 0.29, and 0.15, respectively. Poland, Denmark, Germany, Moldova, and Sweden had the lowest influencing factor coefficients, i.e., −0.12, −0.10, −0.09, −0.08, and −0.07, respectively.

Population is positively correlated with the attention of social media users to the disaster (Fig. 8f ) and is basically consistent with the distribution pattern of the influencing factor coefficient of population before the typhoon’s landing. However, the influencing factor coefficient changed. Iceland, Canada, Ireland, Portugal, and the U.S. had the highest impact factor coefficients, i.e., 2.35, 1.95, 1.52, 1.11, and 1.09, respectively. Chile, Peru, Argentina, Uruguay, and Brazil had the lowest impact factor coefficients, i.e., −0.99, −0.94, −0.94, −0.93, and −0.84, respectively.

Changes in influencing factors before and after the typhoon’s landing

We further compared and analyzed the changes in the five influencing factors before and after the typhoon made landfall. Figure 9 shows the influencing factor coefficient differences (left panel) and their change directions (right panel) before and after the typhoon’s landing in corresponding countries or regions. The letters P and N indicate positive or negative coefficients, respectively. It is found that the influencing factor coefficients of the export value, the CPI, the proportion of nonreligious beliefs, the GDP loss rate and population increased and decreased significantly. By comparing the changes in the effect direction of the influencing factors before and after the typhoon, this study finds that the effect direction of export value, the GDP loss rate and population basically did not change before and after the typhoon landing, and the effect direction of the CPI and proportion of nonreligious beliefs changed in some countries.

The left panel indicates the change values of influencing factors’ coefficients. The right panel shows the coefficients changes’ direction of corresponding influencing factors. P represents positive coefficients. N represents negative coefficients. a , b Export value, c , d CPI, e , f proportion of nonreligious beliefs, g , h GDP loss rate, i , j population.

The countries with enhanced effects of export value before and after the typhoon’s landing are mainly distributed in Europe, South America, and East and Southern Africa (Fig. 9a, b ). Among these countries, Argentina, Uruguay, Panama, and Costa Rica have significantly enhanced effects, with difference values of export value being 1.14, 1.14, 1.03, and 1.02, respectively. Countries with a weakened effect of export value were mainly distributed in northern South Asia, North America, Central America, Southeast Asia, and Oceania, among which the United States, Cuba, Guatemala, and El Salvador had significantly weakened effects, and the differences in the change in export value were −0.47, −0.16, −0.11, and −0.11, respectively. Figure 9b shows that the effect directions of export value before and after the typhoon’s landing both have a negative effect on social media users’ attention to the disaster in Iceland, and both have positive effects on social media users in other countries.

The countries with enhanced effects of the CPI before and after the typhoon’s landing are mainly distributed in North America and South America (Fig. 9c, d ). The United States, Guatemala, El Salvador, and Cuba have significantly enhanced effects, with difference values of the CPI being 0.21, 0.19, 0.19, and 0.18, respectively. Countries with weakened effects of the CPI are mainly distributed in the Eastern Hemisphere, among which Norway, Sweden, the Netherlands, and Denmark have significantly weakened effects, and the differences in the change in the CPI are −0.10, −0.10, −0.09, and −0.09, respectively. The countries where the effect direction of the CPI changed from positive to negative are mainly distributed in Southern Africa before and after the typhoon’s landing (Fig. 9d ). The countries where the effect direction of the CPI changed from negative to positive were mainly distributed in southern North America and northern South America. The countries for which the CPI maintained the same direction are distributed in Europe, Southeast Asia, Oceania, and Africa.

Figure 9e, f shows that the countries with enhanced effects of the proportion of nonreligious beliefs before and after the typhoon’s landing are mainly distributed in North America, South America, and Africa. The U.S., Iceland, Senegal and Omen have significantly enhanced effects, and the difference values of the proportion of nonreligious beliefs are 0.02, 0.01, 0.01, and 0.003, respectively. Countries with a weakened effect of the proportion of nonreligious beliefs are mainly distributed around the world, among which Jamaica, Mexico, Costa Rica, and Panama have a significantly weakened effect, and the difference in the change in the proportion of nonreligious beliefs are −0.20, −0.20, −0.19, and −0.18, respectively. Figure 9f shows that the countries where the effect direction of the proportion of nonreligious beliefs changed from positive to negative are mainly distributed in Oceania and Southeast Asia before and after the typhoon’s landing. The countries in which the proportion of nonreligious beliefs maintained the same direction are distributed in North and South America, Europe, South Asia and Africa.

Figure 9g, h shows that the countries with enhanced effects of the GDP loss rate before and after the typhoon’s landing are mainly distributed in North and Central America, West Europe, and Northern and Southern Africa. Iceland, Ireland, Canada, and Switzerland have significantly enhanced effects, with difference values of the GDP loss rate being 1.32, 0.31, 0.26, and 0.19, respectively. The GDP loss rate has a weak influence on the countries distributed in Southeast Asia, Europe, Northwestern Africa, and Oceania, such as Lithuania, Ukraine, Austria and Slovenia, with changes of −0.37, −0.37, −0.34, and −0.32, respectively. Meanwhile, the effect direction of the GDP loss rate does not change in most countries before and after the typhoon’s landing (Fig. 9h ), whereas countries in Northern Europe and Eastern Europe changed from positive to negative.

Figure 9i, j shows that the countries with enhanced effects of population before and after the typhoons’ landing are mainly distributed in North and South America, South Asia, and Central Africa. Canada, the United States, Ireland, and Cuba have significantly enhanced effects, with change values of 1.72, 1.54, 1.05, and 0.50, respectively. Countries with weakened effects of population are mainly distributed in Southeast Asia, Oceania, Europe, and Southern and East Africa. Argentina, Uruguay, Iceland, and France have significantly weakened effects with changes of −1.18, −1.17, −0.91, and −0.83, respectively. The effect direction of population remains consistent in most countries before and after the typhoon’s landing (Fig. 9j ). However, several countries in Central America, Argentina, and New Zealand changed from positive to negative.

Western countries are the main foreign group of online social responses to the natural disaster

Western countries are the major foreign group with active online social responses to natural disasters. Similar to recent research on the global public interest in earthquakes, which found that Western countries are dominant (Kam et al., 2021 ), we found that Western countries have stronger online social responses to natural disasters. This result is explained by the strong economic conditions and high trade connections between Western countries and the Philippines. Moreover, our research reveals that countries neighboring the Philippines are a major group. This difference is rooted in the geophysical divergence of typhoons and earthquakes since neighboring countries are more likely to be impacted by typhoons than earthquakes.

Distance has little influence on the online social responses

Geographical distance has a limited influence on online social responses. Previous research found that the distance between the country where news media are located and the country where the disasters occur impacts the likelihood that a disaster will be covered by the media (Berlemann and Thomas, 2019 ). Whereas we found no global significant impact of geographical distance between countries and the country where the disaster occurred. On one hand, the neighboring countries have more responses than African countries, but they are comparable to Western countries. On the other hand, the timing of online social response is consistent with the evolution of Typhoon Haiyan. This nonsignificant spatial disparity and temporal consistency reflect that online social networks have almost eliminated the spatiotemporal barriers of social responses to a disaster.

Economy is the main driver of online social responses

Overall, economic factors have more significant influence on the drivers of online social responses than other factors except the population. As shown by the Geodetector results, economic factors have the strongest explanatory power in regard to the number of tweets posted before the typhoon’s landing, while natural, demographic and cultural factors were weaker, and social factors were the weakest. Political factors were not significant. Economic factors had the strongest explanatory power in regard to the number of tweets posted after the typhoon’s landing, while natural, demographic, cultural and social factors were weaker, and political factors were the weakest. Based on the GWR model before landing, export value is basically positively correlated with the intensity of online social responses, and the factor coefficients are generally high in the west and low in the east.

The coefficient of the CPI has significant spatial heterogeneity, with positive effects in North Africa, Oceania and Europe and negative effects in North and South America, Southern Africa and Southeast Asia. The proportion of nonreligious beliefs has a negative effect in North America, western South America, Western Europe, West Africa and Oceania and has a positive effect in eastern South America, Eastern Europe, Asia and Southern Africa before landing. The GDP loss rate has a positive effect on social media users’ attention to disasters, and 18.58% of countries show a negative effect before landing but 31.86% after landing, Population has a generally positive effect on disaster attention among social media users in countries, with only 18.58% (before landing) and 17.70% (after landing) of countries having a negative effect, mainly in North and South America.

Comparing the coefficient changes in influencing factors, it is found that the influencing factor coefficients of export value, the CPI, the proportion of nonreligious beliefs and the GDP loss rate before and after Typhoon Haiyan’s landing have spatial heterogeneity. By comparing the direction of the influencing factors, we find that the direction of the influencing factor of export value did not change before and after the typhoon’s landing. There are four types of changes in the effect direction of the CPI: from positive to negative, from negative to positive, and no change. There are three types of change in the effect direction of the proportion of nonreligious beliefs, that is, from positive to negative, from negative to positive, and no change. The effect direction of the GDP loss rate and population remains unchanged in most countries.

Implications and limitation

Based on the above results and analysis, this paper reveals that the online social response to a natural disaster is beyond geographical limitation and has a global impact. This online social response will be mainly influenced by economic factors and active in Western countries. It indicates that western active online social media users have the most willingness and economic capacity for foreign aid. Hence, this information is of great potential for governments and humanitarian agencies to expand their pool of potential volunteers and donors.

There are limitations to this study. Due to the lack of relevant data from sources, not all the countries are obtained in our analysis. Hence it will lead to the uncertainty of GWR’s results, whereas due to our limited knowledge, the selection of influencing factors may still involve omitted variables, and only the influence of the target layer and the first-level factors are discussed. Besides, the religious rate data are collected from Wikipedia, which may lead to the uncertainty of our analysis. Whereas the GE is controversial regarding its definition and algorithm, which will also introduce the analysis’ uncertainty. In addition, we noticed that Hainan Province in China, also severely affected by the typhoon, has caused a much weaker global online social response compared to the Philippines. This is also a question worth further exploration in the future.

This study takes Typhoon Haiyan in 2013 as a case study to explore the global online social responses to a natural disaster and to investigate their influencing factors. In conclusion, the global online social responses to Typhoon Haiyan are beyond the geographical limit and consistent with the development of disaster. Except for the Philippines, Western countries and surrounding countries had higher responses than others. Economic factors dominated the online social response. The influences of social, cultural, and demographic factors were relatively weak. Political factors had only a weak impact during the post-disaster stage. Our findings demonstrate active online social groups in Western countries are of great potential for governments and humanitarian agencies to call for foreign volunteers and donors. This information will deepen our understanding of online social behavior and help coordinate the humanitarian assistance of various countries and provide a reference for the efficient and orderly implementation of international rescue work after disasters.

Data availability

The global dataset tweets on Typhoon Haiyan and other dataset used in this study is available at: https://doi.org/10.6084/m9.figshare.23617725 . This dataset was proposed in xlsx and csv format and can be processed using Microsoft Excel software and GIS software.

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Acknowledgements

This work is supported by the National Key Research and Development Plan of China (grant numbers 2019YFA0606901) and the National Natural Science Foundation of China (No. 42201498).

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Shen, S., Shi, K., Huang, J. et al. Global online social response to a natural disaster and its influencing factors: a case study of Typhoon Haiyan. Humanit Soc Sci Commun 10 , 426 (2023). https://doi.org/10.1057/s41599-023-01922-5

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Study uncovers lasting impact of natural disasters on college students: 'A spillover effect on pretty much every aspect of the person's life'

A troubling new study found that natural disasters resulting from our warming planet impact college students long after the weather event occurs, even if they're not directly affected. 

What happened?

Research by finance professors at the University of Texas at Dallas and Pennsylvania State University found that students whose families are impacted by extreme weather earn lower grades than their peers, have a greater risk of withdrawing from school, and are more likely to default on their loans post-graduation, as NPR reported . 

Moreover, researchers found that even if students lived halfway across the country from where a disaster struck that affected their families — for example, if a flood destroyed their parents' home — they still struggled to keep up with classes.

Han Xia, a UT Dallas associate finance professor and one of the study's authors, told NPR that underperforming in school and stressing over paying off student loans can have lasting impacts on students. 

"[It] can have a spillover effect on pretty much every aspect of the person's life going forward, including their potential income, their employment opportunities, their opportunities for job advancement, and eventually their finances," he said.

Why is this concerning?

College is increasingly out of reach for many people because of rising tuition costs. 

Watch now: Ecologist cries tears of joy after badly injured bald eagle is released following rehabilitation

As extreme weather events become more common and severe because of human-induced warming , it will only make it more difficult for young people to afford school if their finances are going toward helping their families recover from natural disasters , as is the case for many college-aged adults. 

Extreme weather may also prevent or discourage students from enrolling in college, according to a separate study cited by NPR . The research centered around students in the Houston area who were impacted by flooding from Hurricane Harvey. It found that young people who lived in blue-collar neighborhoods were most likely to pass on college following the storm. 

"That really indicates to me that these are first-generation prospective students who are most affected, in terms of whether or not they decide to go on to college," Emily Gallagher, an assistant finance professor at the University of Colorado Boulder, and an author of the study, told NPR .

Even if students want to keep attending college after a disaster hits, tuition support or other forms of financial assistance are usually not available. So, young people who don't have the resources to pay for classes upfront have to face the difficult choice of taking out more loans or putting their dreams on the back burner. 

What's being done to help?

Vincent Carales, who studies higher education at the University of Houston, told NPR that several universities have made it easier for students to access financial aid and emergency funds earlier than usual following hurricanes . 

Carales said getting funds into students' hands as quickly as possible after natural disasters can improve their chances of staying enrolled in college. 

On a larger scale, scientists are devising creative solutions to slash pollution. For example, a biotech company is testing a bacteria-based technology to transform carbon pollution into products. The Biden administration is also rapidly expanding clean energy projects .

We can make a difference at home by switching to electric appliances , reducing our reliance on plastic products , and supporting sustainable companies. By working together , we can help ensure everyone, including college students , has a bright future.

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Flood vulnerability assessment in rural and urban informal settlements: case study of Karonga District and Lilongwe City in Malawi

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• Published: 24 May 2024

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• Isaac Kadono Mwalwimba 1 ,
• Mtafu Manda 2 &
• Cosmo Ngongondo 3  

Flood vulnerability assessment (FVA) informs the disaster risk reduction and preparedness process in both rural and urban areas. However, many flood-vulnerable regions like Malawi still lack FVA supporting frameworks in all phases (pre-trans-post disaster). Partly, this is attributed to lack of the evidence-based studies to inform the processes. This study was therefore aimed at assessing households’ flood vulnerability (HFV) in rural and urban informal areas of Malawi, using case studies of Traditional Authority (T/A) Kilupula of Karonga District (KD) and Mtandire Ward in Lilongwe City (LC). A household survey was used to collect data from a sample of 545 household participants. Vulnerability was explored through a combination of underlying vulnerability factors (UVFs)-physical-social-economic-environmental and cultural with vulnerability components (VCs)-exposure-susceptibility and resilience. The UVFs and VCs were agglomerated using binomial multiple logit regression model. Variance inflation  factor (VIF) was used to check the multicollinearity of variables in the regression model. HFV was determined based on the flood vulnerability index (FVI). The data were analysed using Multiple Correspondence Analysis (MCA), artificial neural network (ANN) and STATA. The results reveal a total average score of high vulnerability (0.62) and moderate vulnerability (0.52) on MCA in T/A Kilupula of Karonga District and Mtandire Ward of Lilongwe City respectively. The FVI revealed very high vulnerability on enviroexposure factors (EEFs) ( \(0.9\) ) in LC and \((0.8\) ) in KD, followed by ecoresilience factors (ERFs) (0.8) in KD and \((0.6\) ) in LC and physioexposure factors (PEFs) ( \(0.5)\) in LC besides 0.6 in KD for the combined UVFs and VCs. The study concludes that the determinants of households’ flood vulnerability are place settlement, low-risk knowledge, communication accessibility, lack of early warning systems, and limited access to income of household heads. The study recommends that an FVA framework should be applied to strengthen the political, legal, social, and economic responsibilities of government for building the resilience of communities and supporting planning and decision-making processes in flood risk management.

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1 Introduction

Floods are a natural hazard that many communities have to cope with. Climate change and variability have resulted in changes in terms of the frequency and magnitudes of flood-inducing storms in many regions (Hodgkins et al. 2017 ; Kundzewicz et al. 2019 ). The Emergency Events Database (CRED, 2019) reported that around 50,000 people died and approximately 10% of the world population was affected by floods between 2009 and 2019 (Moreira et al. 2021 ). In recent years, the world has deviated from flood hazard control to flood vulnerability assessments (Ndanusa et al. 2022; Ran et al. 2018). This is because the vulnerability of a community partly induces floods to become disasters (Nong and Sathyna 2020 ; Salami et al. 2017 ) and such assessments are important in strategic decision-making and planning (de Risi et al. 2013 ). Consequently, vulnerability assessment has become a primary component of flood hazard mitigation, preparedness and management (Ndanusa et al. 2022). Based on the findings of many studies in the assessment of flood vulnerability, it has been noted that several studies have not combined indicators of UVFs and VCs in their assessments. Those that have combined the indicators (Karagiorgos et al. 2016 ; Mwale 2014 ; Nazeer and Bork 2021 ) have not gone further to propose FVA frameworks to support decision-making, creating a gap which has been addressed in this current study. Anwana and Oluwatobi ( 2023 ) provided a review of the literature on flood vulnerability in informal settlements globally and in South Africa, in particular. Their review found a distinct knowledge gap in flood vulnerability studies. In the Ibadan metropolis area of Nigeria, Salami et al. ( 2017 ) proposed and applied a flood vulnerability assessment framework to provide flood vulnerability assessments of the human settlements and their preparedness to mitigate flood risk. The study established that previous experience of flooding was a key factor in awareness levels, although this awareness was not significantly related to the level of preparedness during flooding. De Risi et al. ( 2013 ) proposed a probabilistic and modular approach to analysing flood vulnerability in informal settlements of Dar es Salaam City in Tanzania. Alam et al. ( 2022 ) conducted a vulnerability assessment based on household views from the Dammar Char in Southeastern Bangladesh by constructing a vulnerability index using quantitative and qualitative data. The study revealed that, on average, the people living in the Dammar Char have a high vulnerability to coastal hazards and disasters. In North-West Khyber Pakhtunkhwa of Pakistan, Nazeer and Bork ( 2019 ) carried out a flood vulnerability assessment through different methodologies of rescaling, weighting and aggregation schemes to construct the flood vulnerability indices. The study found that the weighting scheme had a greater influence on the flood vulnerability ranking compared to data rescaling and aggregation schemes. Oyedele et al. ( 2022 ) analysed vulnerability to flooding in Kogi State of Nigeria as a function of exposure, susceptibility and lack of resilience using 16 sets of indicators. The indicators were normalized and aggregated to compute the flood vulnerability index for the 20 purposively selected communities. The study established that the selected communities had varying levels of risk of flooding, “very high” to “high” vulnerability to flooding. Munyai et al. ( 2019 ) examined flood vulnerability in three rural villages in South Africa’s northern Limpopo Province using a flood vulnerability index. The study revealed that all three villages have a “vulnerability to floods” level, from medium to high vulnerability. While all these studies have assessed flood vulnerability, a framework for guiding its assessment process has been not proposed. The lack of such a framework implies that flood risk reduction is not programmed to address current and future risks. This could be a reason why disaster risk management in Malawi, for example, is described as post-event humanitarian actions and reactive.

The Sentinels-4-African DRR rank Malawi position 11 out of 53 African countries affected by floods from 1927 to 2022 with statistics of 42 events, 948 deaths and 3531, 145 people affected (Danzeglocke et al. 2023). Similarly, the 2011 Climate Change Vulnerability Index by the British Risk Analysis Firm Maplecroft ranks Malawi 15 out of 16 countries with extreme risks to climate change impacts in the world. GOM (2023) indicates that over twenty-five disasters experienced in Malawi have been associated with severe rainfall events in the last decade. For instance, between the periods of 2015–2023, about four major floods induced by tropical cyclones have affected communities. The most destructive was the floods of 11–13 March 2023, influenced by tropical cyclone Freddy (TCF), which killed about 679 people, injured 2178 people, displaced about 563,602 people, and about 511 people were reported missing, including causing several other damages and loss in sectors such as agriculture, infrastructure, food security and health (GOM, 2023). A “state of disaster” was declared on the 13th of March in the districts that were affected by the cyclone namely; Blantyre City and District, Chikwawa District, Chiradzulu District, Mulanje District, Mwanza District, Neno District, Phalombe district, Nsanje district, Thyolo district and Zomba city and district. Relatedly, in January 2022, the passage of a tropical storm named “Ana” over southern Malawi with heavy rainfall caused rivers to overflow, floods and landslides. The flooding affected 19 districts in the southern region and among the heavily affected districts were Chikwawa, Mulanje, Nsanje and Phalombe. The event caused 46 deaths, and 206 injuries, 152,000 people were displaced with several infrastructural damages. The country also experienced the worst cyclone Idai which originated from Mozambique in 2019. This cyclone induced floods which killed 60 people as well as affected 975,000, displaced 86,976 and injured 672 people (PDNA, 2019 ). In January and February 2015, over 1 million people were affected and about US$ 335 million was incurred on infrastructural damage (PDNA, 2015 ). However, floods have been considered largely as a rural manifestation during the past years (Chawawa, 2018 ), with district councils taking the lead in flood management through the development of disaster risk management strategies and policies (Manda and Wanda, 2017 ). This neglect made disaster management policies and strategies to be limited to cities as compared to rural areas. Recently, Lilongwe City has experienced numerous flooding with varying impacts of damage in schools, health centres, shops, houses and loss of lives (LCDRMP, 2017). This increased occurrence and devastating impacts calls for putting measures in place to protect people living in flood-prone areas, including flood risk reduction, prevention, mitigation and management. However, strong measures cannot be put without FVA which is a cornerstone for disaster risk reduction (Munyai et al. 2019 ; Nazeer and Bork 2021 ; Nong and Sathyna 2020 ).

FVA provides a significant opportunity towards identifying factors leading to flooding losses (Lidiu et al. 2018; Nazeer and Bork 2021 ; Ndanusa et al. 2022). FVA is an impetus in which science may help to build a resilient society (Ran et al. 2018; Birkmann et al. 2013 ). In addition, FVA provides metrics that can support decision-making processes and policy interventions (Mwale et al. 2015 ; Ndanusa et al. 2014) and is a proactive task for pre-hazard management and planning activities (Parvin et al. 2022 ). Nazir et al. (2013) argue that FVA provides an association between theoretical conceptions of flood vulnerability and daily administrative processes. Mwale ( 2014 ) holds that vulnerability must be quantified and analysed to identify specific dimensions of vulnerability. Birkmann et al. ( 2013 ) add that the need to understand vulnerability is a primary component of disaster risk reduction at the household and community level and culture of building resilience. Iloka ( 2017 ) highlights that measuring vulnerability helps to determine immediate impacts on lives as well as future impacts of the affected households and communities. The Sendai Framework (2015–2030), an international policy for DRR also emphasises vulnerability assessment as a tool for minimizing the impact of hazards (UNISDR 2017 ). The Sendai Framework posits that vulnerability assessment should be conducted to understand risk in all dimensions of vulnerability, capacity, exposure of persons, hazard characteristics and the environment (UNISDR 2017 ). Birkmann et al. ( 2013 ) suggest that a vulnerability assessment is a prerequisite to reducing any natural hazard's impacts. Therefore, this study was aimed at assessing household flood vulnerability in both rural and urban informal settlements in Malawi. This was achieved by: (1) analysing the variability of households' flood vulnerability (based on physical, social, economic, environmental and cultural factors (2) quantifying household vulnerability to floods in Karonga District and Lilongwe City using multicollinearity analysis of vulnerability factors (physical, social, economic, environmental and cultural) and vulnerability components (exposure, susceptibility and resilience) (3) proposing FVA framework for rural and urban informal settlements, including constructing a multi-hazard vulnerability indicators which is missing in most studies. The study contributes to scanty literature on FVA in developing countries such as Malawi. As many areas of Malawi are flood-prone, the study directly informs decision-making for both preparedness and mitigation measures among the vulnerable communities.

2 Materials and methods

2.1 study approach.

This study carried out flood vulnerability assessment (FVA) using an inductive approach (Abass 2018 ; Kissi et al. 2015 ). The use of an inductive approach allows the study to apply quantitative techniques (Fig.  1 ). These techniques helped to isolate variables and indicators that were significant to contribute to household flood vulnerability.

Methodology layout

2.2 Study area

This study was carried out in Karonga District and Lilongwe City in the northern and central regions of Malawi respectively. Specifically, this study was carried out in Mtandire Ward and Traditional Authority Kilupula in Karonga District and Lilongwe City respectively.

The target flood-prone area of T/A Kilupula in KD was the Lufilya catchment (Fig.  2 ). This study targeted two groups of village headmen (GVH) in T/A Kilupula of the northern part of Karonga district. These include GVH Matani Mwakasangila and Mujulu Gweleweta in Traditional Authority Kilupula. The area of GVH Matani Mwakasangila is found in T/A Kilupula located about 16 km north of Karonga town. GVH Matani Mwakasangila has five Village headmen (VH) namely Eliya Mwakasangila, Matani Mwakasangila, Chipamila, Shalisoni Mwakasangila and Fundi Hamisi. The greater part of the area—Eliya Mwakasangila, Chipamila and Matani Mwakasangila, are bounded by Lake Malawi to the eastern side and the M1 road-Songwe-Tanzania border to the Western side. The other two villages Shalisoni Mwakasangila and Fundi Hamisi are to the Western side of the M1 road. The area has numerous networks of rivers such as Lufilya, Kasisi, Fwira, Ntchowo, and Kasoba.

Map T/A Kilupula in Karonga District showing Villages of Study Area

This catchment of T/A Kilupula was selected based on the frequency of flood occurrence (Table  1 ). Kissi et al. ( 2015 ) indicate that the magnitude of an extreme event is inversely related to its frequency of occurrence. It was also chosen because the nature of their locations is prone to flooding (Mwalwimba 2020 , 2024 ; SEP-2013–2018). This makes the residents vulnerable to flood hazards that cause disaster every year.

The area is dominated by floodplains along the shores of Lake Malawi (SEP-2013–2018). These areas are flat and low-lying areas as such this becomes the pre-requisite to flooding in the event of a heavy downpour (Karonga Met Office 2021). Furthermore, the choice of this area was due to settlement patterns, located in flood plains and issues of culture that have forced the people to occupy dangerous areas and even occupy the protected areas rendering them vulnerable to the effects of flooding (Mwalwimba 2020 ) (Fig.  3 ).

Settlement patterns of households in T/A Kilupula of Karonga District

Lilongwe district hosts the capital city of Malawi. The district became the host of the Capital city in 1975 after it was relocated from Zomba. The district owes its name to the Lilongwe River, which flows across the centre of the district (SEP, 2017–2022). The city has grown tremendously since 2005 when the government relocated all the head offices from Blantyre (SEP 2017 ). This growth has been also amplified by the presence of numerous opportunities in the city like access to socio-economic services and availability of markets for the produced products. This growth has contributed in generating a lot of vulnerable conditions of people to hazards such as floods, accidents, fires, droughts, environmental degradation and disease epidemics (LCDRM 2017) because of increased environmental degradation, and increased conversion of agricultural land into urban infrastructural development. Though hazards in the city overlap and interact in cause and effect, floods are the most frequently occurring hazards that affect many parts of the city (SEP 2017 ). As a category related to water and weather, floods, mostly affect areas like Mtandire (area 56), Kauma, Kaliyeka, Chipasula, Kawale, Nankhaka, Area 22, Kauma, New Shire, Area 25, Kawale, and Mgona in the city (LCDRM 2017) (Fig.  4 ).

Map of Malawi showing the Location of Karonga District and Lilongwe City

Mtandire Ward in Lilongwe City (Fig.  5 ) was chosen because it is an informal settlement, a condition that would likely put residents susceptible to environmental hazards like floods. The records indicate that floods repeated in 2013, 2014, 2015, 2016 and 2017. Data indicates that in February 2017, floods caused a magnitude of the disaster which caused great damage; more than 4000 people were affected including loss of people’s lives. The affected areas were Mtandire, Kauma, New Shire, Area 25, Kawale, Nankhaka and Mgona.

Settlement Patterns in Mtandire Ward of Lilongwe City

2.3 Flood vulnerability

Vulnerability is a complex concept and includes diverse components (Rana et al. 2018). Therefore, vulnerability requires a comprehensive methodology which can help to reveal various components (Moreira et al. 2021 ). Rana et al. (2018) stipulate that there is a lack of integrated methodology that fuses all the components. This study used an indicator-based approach to quantitatively assess household flood vulnerability. As accorded by ISDR (2014), the quantitative approach was useful in establishing indicators of the FVA framework. Kablan et al. ( 2017 ), and Nazeer and Bork ( 2021 ) agree that quantitative indicators are used to predict flood vulnerability. However, variation exists in the selection of the quantitative tools (Kissi et al. 2015 ). For instance, Nazeer and Bork ( 2021 ) applied Pearson’s correlation to predict flood vulnerability. Kissi et al. ( 2015 ) used deductive and inductive approaches to select flood vulnerability indicators. This study used binomial multiple logistical regression to predict household flood vulnerability. The use of this method allowed us to agglomerate the indicators of the UVFs and VCs (Fig.  6 ).

Conceptual framework

2.3.1 Conceptual framework on flood vulnerability

This study developed a conceptual framework based on the understanding that a vulnerability occurs as an intersection of biophysical vulnerability and social vulnerability (Iloka 2017 ; Wisner et al. 2004 , Cutter 2003). This entails that the combination of hazard (floods) and vulnerability to harm society depends on the physical risk and social risk.

This conceptual framework indicates that two forces create vulnerability of households/communities to floods. First, households can be vulnerable to floods when subjected to the underlying vulnerability factors (physical, social, economic, environmental and cultural causes). Each of the causes, physical-social-economic-environmental-cultural, have the indicators that are used to identify households’ vulnerability to floods. Depending on variations that exist among these indicators in terms of their scores, percentages, inertias and probability values, households may be determined and/or predicted their vulnerabilities. The second force is determined by vulnerability components (exposure, susceptibility and resilience) (Kissi et al. 2015 ). Households are vulnerable to floods if they are exposed and susceptible to it and have less resilient to withstand its impacts (Rana et al. 2018). In this study, exposure is portrayed as the extent to which an area that is subject to an assessment falls within the geographical range of the hazard event (Nazeer and Bork 2021 ). This implies that exposure looks at possibility of flooding to impact people and their physical objects (Nazeer and Bork 2021 ) in the location they live. Furthermore, susceptibility means the predisposition of elements at risk (social and cultural) to suffering harm resulting from the levels of fragility conditions (Birkmann et al. 2013 ; Kablan et al. 2017 ; Nazeer and Bork 2021 ). Resilience of households is evaluated based on the capacity of people or society potentially exposed to hazards to adapt, by resisting or changing in order to reach and maintain an acceptable level of functioning and structure (Ndanusa et al. 2022). This is determined by the degree to which the social system is capable of organising itself to increase its capacity for learning from past disasters for better future protection and to improve risk reduction measures as well as to recover from the impact of natural hazard (Birkmann et al. 2013 ; Nazeer and Bork 2021 ). Iloka ( 2017 ) states that low incomes, lack of resources, and unemployment are some of the factors that make vulnerability leading to disasters. This study’s conceptual framework highlights the scenario that the occurrence of hazards (floods) in a community (Lilongwe city and Karonga district) where households are subjected to many characteristics in the vulnerability factors while at the same time the households are exposed and are susceptible to floods, the condition may turn floods to become disasters. It is only when the households have enough resilience and adaptive measures that they can either cope up with or respond quickly to the hazard (floods). Similarly, if the households are not resilient and have fewer adaptive measures, a situation that may increase vulnerability of households to the hazard impact resulting in a devastating disaster. Therefore, lack of adaptive capacity means that the community may be limited to respond to the disaster on time thereby their vulnerability will be always high. This conceptual framework gives a basis that flood vulnerability assessment therefore should examine factors that predict household vulnerability to floods and link them to the composite indicators of vulnerability, including understanding their adaptive capacity that would help them to cope with flood impacts. The assessment, using this framework should analyse several indicators from the underlying vulnerability factors and components of vulnerability to fully identify which of these conditions contribute to vulnerability in a specific location to generate standardised indicators of flood vulnerability assessment.

2.3.2 Indicators of flood vulnerability

Flood vulnerability was explored through the lens of underlying vulnerability factors (UVFs)-physical-social-economic-environmental and cultural (Table  2 ). The physical vulnerability (PV) has been defined as the vulnerability of the physically constructed materials. The indicators were defined as pre-underlying factors that may contribute to the constructed elements (houses & other infrastructures) being vulnerable to flood hazards. Social vulnerability (SV) is looked at by the influences of the variety of social processes which create the vulnerability of households to floods (Joakim 2008 ). Economic vulnerability (EcV) is defined as the influences of economic processes existing in the community i.e. livelihood activities that may or may not contribute to household vulnerability. Environmental vulnerability (EnV) is the vulnerability of the built environment as described by pre-existing conditions like residing in prone areas and use of natural resource base. Cultural vulnerability has been defined as vulnerability influenced by cultural fabric such as beliefs, customs, cultural conflicts and absence of resource ownership.

The vulnerability components (VCs)-exposure-susceptibility and resilience (Table  3 ) were combined by UVFs. Physical and environmental factors linked to exposure (i.e. human settlement damage, house type, location, rivers). Social and cultural factors combined with susceptibility (i.e. community accessibility, flood risk awareness, adaptation mechanisms, warning systems) to determine household vulnerability. Economic factors linked with resilience (i.e. a source of income, the capacity of economic skills and resource skills).

Both the UVFs and VCs were selected based on a thorough review of contemporary frameworks such as Pressure and Released Mode (Wisner et al. 2014 ); Urban Flood Vulnerability Framework (Salami et al. 2017 ); and the Hazard of Place Model (Cutter 1996 ). Since there is no generally acceptable way of selecting vulnerability indicators (Kablan et al. 2014; Nazeer and Bork 2021 ), this study considered the indicators based on a cut-off point of probable value zero to one where zero represents the minimum and one indicates maximum values (Kissi et al. 2015 ; Nazeer and Bork 2021 ; Ndanusa et al. 2022). Data on the UVFs and VCs were collected using a quantitative cross-sectional structured survey questionnaire from 200 and 345 household participants in T/A Kilupula of KD and Mtandire Ward of LC respectively. The questionnaire was programmed in KoBocollect and Android tablets were used to capture the data from household participants. Data were also collected for the elements at risk from each underlying vulnerability component to determine the contribution of vulnerability for the households.

The vulnerability component indicators (Table  3 ) were normalised to have a comparable set of indicators, the study adopted the Min–Max normalisation to convert the values to a linear scale (such as 0–1) (Balica et al 2012 ; Erena et al. 2019; Kissi et al. 2015 ; Nazeer and Bork 2021 ; Ndanusa et al. 2022). Vulnerability increases with an increase in exposure and susceptibility, and it decreases with an increase in Resilience (Kissi et al. 2015 ; Mwale 2014 ; Munyani et al. 2019 ; Nazieer 2021). Therefore, normalisation was based on the assumptions that:

(a) Vulnerability (V) increases as the absolute value of the indicator also increases. In this case, where the functional relationship between the indicator and vulnerability is positive, the normalised indicator is derived using the following equation (Oyedele et al. 2022 ).

(b) Vulnerability (V) decreases with an increasing absolute value of the indicator. Here, when the relationship between vulnerability and the indicator is found to be negative, the data are rescaled by applying the equation (Oyedele et al. 2022 ).

where Xi = normalised value; Xa = actual value; XMax = maximum value; XMin = minimum value for an indicator i (1, 2, 3... n) across the selected communities.

Furthermore, no weight was assigned to the indicators of vulnerability components. The reason for not including weights was that most of the responses during the stakeholders’ engagement were contradictory and highly inflicting. Therefore, to avoid an index value that will mislead the end users, the normalised indicator was aggregated into its respective sub-indices for the final flood vulnerability index. The additive arithmetic function was employed in the aggregation of the indicator into its respective sub-indices (exposure, susceptibility, and lack of resilience) using an equation (Kissi et al. 2015 ; Nazeer and Bork 2021 ; Oyedele et al. 2022 ).

The overall flood value of the vulnerability index was computed with Eq. ( 4 ), an additive function (Nazeer and Bork, 2019 ; Lee and Choi 2018; Oyedele et al. 2022 ).

where SIE means sub-indices exposure, Susceptibility (SIS), and lack of resilience (SILoR) for “n” numbers of indicators in each component of vulnerability.

The study measured the level of vulnerability of the elements at risk in all the underlying vulnerability factors (Table  4 ). These were evaluated based on the constructed scale which modified the Balica et al. ( 2012 ) and was calibrated as (0–0.2) very low vulnerability; (0.2–0.49) moderate vulnerability; (0.5–0.59) vulnerability (0.6–0.79) high vulnerability and (0.8–1) very high vulnerability. However, in the actual data collection tool (household questionnaire survey), Mwalwimba ( 2020 ) measurements scale of “not vulnerable”, “slightly vulnerable”, “vulnerable”, “severely vulnerable” and “do not know” were used and later the percentage obtained during univariate analysis were computed and compared to the weighting scale constructed (Balica et al. 2012 ) (3.10). Ndanusa et al. (2022) argued that a breakdown of the elements at risk poses a serious threat to communities' vulnerability and prosperity. This consequently contributes to the higher vulnerability of the community to hazards.

2.4 Study population and sampling determination

The target flood-prone area of TA Kilupula in KD was selected based on the frequency of flood occurrence. Kissi et al. ( 2015 ) indicate that the magnitude of an extreme event is inversely related to its frequency of occurrence. Whilst, Mtandire Ward in Lilongwe City was chosen because it is an informal settlement. Household participants in Mtandire ward were those specifically in two Group Village Headmen, Chibwe and Chimombo of Senior Chief Ligomeka. These villages are located along the Lingadzi River opposite area 49 (New Gulliver). This study used a total of 10 headmen (VH). The choice of the VH was based on proximity to Lingadzi River. Mtandire has a total population of 66,574 people, but 5000 people are reported to be at risk of floods (MDCP 2010–2021; MPHC 2018). Relatedly, the target population in Karonga district were households of GVH Matani Mwakasangila and Mujulu Gweleweta in Traditional Authority (TA) Kilupula. These household villages share a network of water systems such as Lufilya, Mberere, Ntchowo and Fwira (Mwalwimba 2020 ). This study used a total of 10 village headmen (VH), five from each GVH. The choice of five VH in each GVH was based on the fact that each GVH in T/A Kilupula has a minimum number of five Village Headmen (Karonga Chief Classification 2016). T/A Kilupula has a total population of 78,424 people, with approximately 9500 households at risk of floods (KD-SEP 2013-2018; MPHC 2018).

The sample size (n) for this study was calculated using the formula in Fisher et al. ( 2010 ) as shown in the Eq. ( 5 ). The formula in Eq. ( 5 ) returns the minimum sample size required to ensure the reliability of the results.

In Eq. ( 7 ), Z is the confidence level (1.96 for 95%), p is the proportion of the target households, q = is the alternative (1-P) and d is the power of precision (d = 0.05 at 95%). The formula requires knowing the target population (P) and it also assumes “P” to be 0.5 which is conservative. Therefore, the fact that the number of households prone to floods in T/A Kilupula and Mtandire ward is known, using this formula, 384 and 246 households were obtained from Mtandire ward and T/A Kilupula respectively. The study used 0.5 (50%) to represent “P” in Mtandire Ward and 0.2 (20%) to represent “P’ in T/A Kilupula. The reason for differentiating the “P” was that in the Mtandire ward, the whole area was selected while in T/A Kilupula not all the GVHs were selected and involved in the survey. Furthermore, unlike in T/A Kilupula where the population is sparsely distributed and households were selected based on location to flood-prone areas, in Mtandire ward 50% was used as conservative because of high population density such it was possible to interview many households. During data collection, the researcher managed to collect data from 345 and 200 household participants, representing 90% and 81% of the total sampled in Mtandire ward and T/A Kilupula respectively. The reason for not completing the actual sample size was that the household survey interviewed houses along the buffer zones of Lingadzi and Lufilya rivers and the whole area of the buffer was randomly selected. Therefore, continuing to interview every household in the buffered area would have meant interviewing every household. This would have worked against the rule of simple random sampling strategy and survey ethics (Kissi et al. 2015 ).

2.5 Questionnaire design and administration

This study used a structured household questionnaire survey. This questionnaire captured information that provided the linkages of households’ vulnerability factors, exposure, susceptibility and resilience. Associations of vulnerability factors have been supported in the literature (Kissi et al 2015 ; Mwale 2014 ; Nazeer and Bork 2021 ). Nazeer and Bork ( 2021 ) argue that the issue of double counting of the indicators is an important step to be considered in the formation of composite indicators. The household questionnaire survey was coded in KoBoToolBox. The household questionnaire survey was administered face-to-face with household participants who were above 21 years old. The age parameter was controlled in the KoBoToolBox environment such that the interviewers could not proceed with administering the questionnaire if this question was not answered even if the age entered was below 21. It is also important to note that the attributes of the variable age were not coded because it is a continuous variable hence the ages were manually collected from the participants. Finally, the household questionnaire survey was pretested and piloted in Mchesi and Mwanjasi in LC and KD respectively. Before pretesting and piloting, the research assistants (RAs) were trained to have a common local understanding of the terms that were contained in the questionnaire, specifically vulnerability, floods, resilience, susceptibility, adaptive capacity and exposure.

2.6 Data analysis

To determine variations among the indicator variables of UVFs for the predicted factors, a Minitab statistical test called Multiple Correspondence Analysis (MCA) was computed. MCA produced two outputs called “Indicator Analysis Matrix” and “Column Contribution table”. The column of contribution is used to determine the variations that exist between indicators (Husson 2014). On the other hand, the total inertia in the Analysis of Indicator Matrix (AIM) was averaged for all the five UVFs in LC and KD to obtain a single inertia which was used to determine a multi-correspondence variations of vulnerability factors (MIHVF).The indicators in the assessment that contributed to flood vulnerability were marked with red ink in the measurement scale of important (INT) and very important (VINT). The significance levels between demographics and vulnerability factors were analysed using the single chi-square test and a combined value analysis package. Also, chi-square tests and probability value ( p value) were used to compute significance levels of variables in UVFs and VCs. The formula for chi-square statistics is:

In addition, it follows a with (r−1) (c−1) degrees of freedom. Where

O ij is the observed counts in cell ij; i = 1, 2, 3…..r and j = 1, 2, 3…..c where r is the number of rows and c is the number of columns in an r × c contingency table.

E ij the expected counts in cell ij; i = one, 2, 3…..r and j = 1, 2, 3…..c where r is the number of rows and c is the number of columns in an r × c contingency table.

Those that were significant were computed in the modified binomial multiple logistical regression model using equations. All these were performed in “r” and STATA version 12.

A post-analysis of computed results was carried out using an artificial neural network (ANN). ANN is a machine learning method that stands more independent in comparison than statistical methods (Ludin et al. 2018; Parvin et al. 2022 ). Several studies have used ANN to predict specific events (Mwale 2014 ). Due to its predictive ability, this method was applied in this study as a post-analysis to predict the causes of flood vulnerability of the variables which were statistically tested using a combined P value package between UVFs and VCs. ANN comprises several nodes and interconnected programming elements (Mwale 2014 ; Parvin, et al. 2022 ). It contains input layers, hidden layers and output layers (Ahmadi 2015 ) (Fig.  7 ).

Example of ANN using MLP

The multivariate level used the multiple binomial logistical regression model (Eq.  6 ) (Israel 2013) to predict household flood vulnerability. It utilised a paired comparison model (Hamidi et al. 2020; Chen et al. 2013), in which each UVF was linked with a selected vulnerability component (exposure, susceptibility and resilience). This link is accorded in the studies of Wallen et al. (2014) and Mwale ( 2014 ). This model generated significant levels of physical exposure, social-susceptibility, eco-resilience, enviro-exposure and cultural-susceptibility. Then, the Flood Vulnerability Index (FVI) was applied to determine which factor contributes to vulnerability (Balica et al. 2012 ; Kissi et al. 2015 ). The FVI uses a probability range of 0–1 (Balica et al. 2012 ) where 0 means not vulnerable and 1 means more vulnerable. Using Eq.  1 , the paired attributes were run in r environment through the modified binomial logit multiple regression (Eq.  6 ). However, it would have been significant to use logit-ordered regression since the vulnerability has a certain order (Kissi et al. 2015 ; Hamidi et al., 2020).

where \({y}_{j}\) is a response variable (i.e., as selected from exposure, susceptibility and resilience) \({\beta }_{i}\) is intercepted (values generated by the equation after extraction in r- environment, \({\delta }_{i}\) is predictor variable (selected from physical, social, economic, environmental and cultural), \({O}_{i}\) operator (i.e., measurement scale, less important and very important which considered by the model), \({\epsilon }_{j}\) is an error. This equation was applicable for all the \(UVFs,\) thus parameters in the \(UVFs\) were predicted separately based on the \(VCs\) to which they were associated. The link of UVFs and VCs in the regression model was computed in an implicit relationship showing the predictor and response variables (Table  5 ).

The binomial logit regression model was used based on three assumptions which implied that:

The indicators for UVFs should be measured as a proportional value of household participants involved during the survey. The percentage values should be generated using a scale range with the operator of “ less important ”; “ important ” and “ very important ” to contribute to flood vulnerability”. However, for flood vulnerability determination, a cut-off point should be placed at greater or equal to 50% for each indicator from the operator of the scale range of “important” and “very important”. In this case, all the values generated in the scale of “less important” as responded by the participants should be left out during determination and selection.

The linkage of UVFs and VCs should be based on statistical tests using P-values or correlation (r) or simply any statistical test applicable to the researcher. The values that are significant at a certain confidence level (i.e. 0.05 in this study) should be selected to be included in the framework for specific combinations like Physical Exposure Factors (PEFs), Socio-Susceptibility Factors (SSFs), Eco-Resilience Factors (ERFs), Enviro-Exposure Factors (EEFs) and Cultural-Susceptibility Factors (CSFs). Furthermore, those values significant at an appropriate confidence level should be considered as factors generating flood vulnerability in the studied areas.

Multicollinearity of the UVF and VC variables should be checked using variance independent factor (VIF) to assess the level of correlation in the regression model. It is assumed that a variable with VIF ≥ 10 has higher variance inflation in influencing other response variance and is redundant with other variables. As such, that variable should be dropped. In this study, the VIF process was done in SPSS.

Flood vulnerability index (FVI) was used in the determination of household flood vulnerability based on the output of the analysis of the results. A summarized comparison flood vulnerability index (FVI) probability scale 0 to 1 (Balica et al. 2012 ) has been presented in Table  6 .

Results were presented on spatial distribution maps, computed in ArcGIS 10.8 Desktop. Shapefiles for Malawi administrative boundaries were downloaded from MASDAP (Malawi Spatial Data Application Portal). Then Excel was used to generate the tabulated information and pie charts and later exported the output to ArcMap. The Maps were coloured to show the contribution of each variable to households' flood vulnerability (Fig.  8 ).

Vulnerability levels

3 Results and discussions

3.1 variability of underlying vulnerability factors.

The results of Multiple Correspondence Analysis (MCA) output have been outlined in Tables 7 , 8 , 9 , 10 and 11 , with those with higher quality value (Qual.), inertia, correlation (Corr.) and contribution (Contr.) marked with red ink to depict variation in flood vulnerability.

The results in Table  7 show all the physical variables marked by red ink have larger quality values in Mtandire Ward of LC. However, the results in T/A Kilupula of KD show the greater quality value in the scale of “VINT” for indicator values of poor construction standards for houses (0.551) and lack of construction materials (0.708). Furthermore, the results also indicate a higher correlation (corr.) for poor construction standards for houses in the scale value of “INT” and ‘VINT, accounting for a higher amount of inertia in both rural and urban areas. Construction of roads and other infrastructures (0.234) account for a high contribution to the inertia in Mtandire Ward of LC while poor construction of housing standards account for a higher inertia value (0.201) in both Mtandire Ward of LC and (0.313) and in T/A Kilupula of KD (Table  7 ). The results further established that physical elements at risk on the scale of “severe vulnerable” have the vulnerability thresholds of 0.5 and 0.6 in Mtandire ward and T/A Kilupula respectively.

The results of MCA show a significant contribution of vulnerability with a quality value in the category of social security on the scale of INT (0.506) and VINT (0.500). The results further show a significant contribution of vulnerability in the category of inavailability of health services (0.513) in the scale of INT in LC. In T/A Kilupula of KD, the results show significant quality values on lack of capacity to cope (0.821) in the scale of INT, social security and human rights in the scale of INT and VINT (Table  7 ). While the results of the inert values in Mtandire Ward of LC do not deviate much from the expected, in T/A Kilupula of KD the inert value of lack of capacity to cope (0.124) in scale of INT and social security (0.117) in a scale of VINT deviate from the expected value. The results also indicate a higher correlation (corr.) social security (0.504) and human rights (0.648) and unavailability of health services (0.506) in Mtandire Ward of LC while lack of capacity to cope (0.790) and social security (0.560) have higher Corr in T/A Kilupula of KD accounting higher amount of inertia to contribute to vulnerability. The results further show all the indicator variables in the scale of “INT) contribute higher to the inertia in Mtandire Ward of LC while only lack of capacity to cope (0.2613) and social security (0.2141) contribute higher to the same in T/A Kilupula of KD (Table  8 ).

The results in Table  9 show that lack of markets (0.574) and poverty (0.513) in the scale of “INT” have higher quality value in Mtandire Ward of LC while lack of credit unions and lack of markets showed higher quality value in T/A Kilupula of KD. These results suggest that lack of markets, poverty and lack of credit unions contribute more to household vulnerability to floods than lack of alternative livelihoods. The results further show that all the indicator variables in Mtandire Ward of LC have an inertia value at the expected rate of less than 10% while in T/A Kilupula of KD lack of credit unions (0.103), lack of markets (0.499) and poverty (0.123) display values that deviate from the expected. Similarly, the results show a weak correlation (less than 1) for all the economic indicator variables in Mtandire Ward of LC and only lack of markets (0.499) is close to 1 in T/A Kilupula of KD thereby contributing highly to the inertia. The lack of credit unions and lack of markets account for a high contribution to the inertia, thereby suggesting a high contribution to flood vulnerability. The results also found that the economic elements at risk have a higher vulnerability value in T/A Kilupula (0.55) compared to Mtandire ward (0.33) on the scale of severe vulnerable.

The results in Table  10 show that except for poor land management in T/A Kilupula of KD for scales of INT and VINT, environmental mismanagement and inappropriate use of resources have larger quality values in Mtandire Ward of LC and T/A Kilupula of KD. No indicator variable depicted the unexpected inertia value in Mtandire Ward of LC and T/A Kilupula of KD. In LC, the results further revealed that the correlation is higher for environmental mismanagement (0.524) in the scale of INT, poor land management is also higher in both scales and inappropriate use of resources (0.518) in the scale of INT. However, extensive paving (0.674), environmental mismanagement (0.557) and poor land management (0.677) have higher correlation values close to one. Environmental mismanagement (0.169), poor land management (0.202; 0.104) and inappropriate use of resources (0.152; 0.105) account for high contribution to the inertia in Mtandire Ward of LC while extensive paving (0.1721) and environmental mismanagement (0.137; 0.101) account for higher contributions in T/A Kilupula of KD (Table  10 ). It was also found that environmental elements at risk are more vulnerable in T/A Kilupula of Karonga on a scale of “slightly vulnerable” (Fig. 4.39) compared to the Mtandire ward of Lilongwe City.

The results in Mtandire Ward of LC showed that lack of safety measures (0.551) and lack of personal responsibility (0.632) have high-quality values above the cut-off of 50% while in T/A Kilupula of KD traditional beliefs (0.508), settlements conditions (0.579), lack of safety measures (0.596) and lack of personal responsibility (0.636) have high-quality values. No indicator variable depicted the unexpected inertia value in Mtandire Ward of LC and T/A Kilupula of KD. The results further revealed no strong correlation (close to 1) in Mtandire Ward of LC to contribute to inertial variability. Nevertheless, in T/A Kilupula of KD, the results showed a strong correlation between traditional beliefs (0.506) and poor settlement conditions (0.576). This suggests people living in Mtandire Ward are not aware that they live informally. It was noted that Mtandire Ward is not properly defined as it is part of the Lilongwe City or Lilongwe District. While results show no higher value for contribution (Contr) in Mtandire Ward of LC, traditional beliefs (0.187), settlement conditions (0.199) and language of communication (0.1526) account for high contribution to the inertia in KD (Table  11 ).

Cumulatively, the results of the MCA for all indicators in the category of quality value \(\ge\) 0.50 (50%) revealed an average of “high vulnerability” (0.62) in T/A Kilupula of KD and “moderately vulnerability” (0.52) in Mtandire Ward of LC. Based on individual factors, the results found high physical vulnerability in both T/A Kilupula (0.61) and Mtandire Ward (0.65), high social vulnerability in T/A Kilupula (0.68) compared to moderate social vulnerability in Mtandire Ward (0.58), high economic vulnerability in T/A Kilupula (0.60) compared to moderate economic vulnerability in Mtandire Ward (0.51), high environmental vulnerability in both T/A Kilupula (0.67) and Mtandire Ward (0.68) and moderate cultural vulnerability in T/A Kilupula (0.54) compared to very low cultural vulnerability in Mtandire Ward (0.16).

3.1.1 Artificial neural network: multi-layer Perceptron (MLP)

The results of the ANN in multi-layer perceptron (MLP) to show the relationship between the indicators used in the UVFs and those in the VCs are presented in Tables 12 , 13 , 14 , 15 and 16 .

The results of exposure linked with physical factors reveal that there is a strong relationship between house type with PCS in T/A Kilupula of KD, while in Mtandire Ward of LC the relationship is not very strong (−9.116) (Table  12 ). The relationships of house type with CRFs imply that these contribute to household flood vulnerability. Lack of construction materials (PCMs) has a strong network value in T/A Kilupula of KD compared to Mtandire Ward of LC with a negative value (Table  12 ). The results reveal that houses made up of bamboo followed by those made up of mudstone are strongly associated with PCS in T/A Kilupula of KD. The results further show that houses made up of unburnt bricks are strongly associated with poor settlement conditions in Mtandire Ward of LC. Lack of construction materials has a strong relationship in T/A Kilupula of KD than in Mtandire Ward of LC. Similarly, CRF and AI have a strong relationship with house material type in Mtandire Ward of LC thereby contributing to high household flood vulnerability in LC.

In Table  13 , the results revealed that sex is significant with social vulnerability factors (0.0539), physical vulnerability factors (0.0371), economic vulnerability factors (0.0562) and cultural vulnerability factors (0.0594) in KD while only environmental factors are significant with sex (0.0331) in LC. The result further revealed that marital status is significant with physical vulnerability factors in T/A Kilupula of KD (0.0265), environmental factors (0.0383) and economic factors (0.0497) in Mtandire ward of LC while in T/A Kilupula (0.0526) with cultural factors (Table  13 ). In terms of education, the results established that social factors (0.001), environmental factors (0.0064) and economic factors (0.0235) are significant to education in Mtandire ward of LC while economic factors (0.0378) are significant in T/A Kilupula of KD (Table  13 ). Finally, the results show that cultural factors (0.0075) and economic factors (0.0106) are significant to occupation in T/A Kilupula and Mtandire ward respectively (Table  13 ).

The results show positive and negative outcome of LOC in T/A Kilupula of KD and Mtandire Ward of LC respectively (Table  14 ). These results point to the fact that lack of capacity to cope contributes to household vulnerability in T/A Kilupula of KD than in Mtandire Ward of LC. The results further show that LAL and LS have positive values both in Mtandire Ward of LC and T/A Kilupula of KD, but with greater contribution to household flood vulnerability in Mtandire Ward of LC. Finally, the results reveal that AHS has positive and negative value in T/A Kilupula of KD and Mtandire Ward of LC. This result indicates that AHS contribute to household flood vulnerability in T/A Kilupula of KD compared to Mtandire Ward of LC (Table  14 ).

The results of ANN revealed that all the UVFs for economic factors have positive values in Mtandire Ward of LC and T/A Kilupula of KD, but with higher values in Mtandire Ward of LC. Lack of income generating activities was revealed to be higher both in Mtandire Ward of LC and T/A Kilupula of KD. These results imply that the NCU, LAL, PO and LGA contribute to household flood vulnerability in Mtandire Ward of LC and T/A Kilupula of KD (Table  15 ).

The results of geography linked with environmental factors reveal that there is strong relationship between them, all with a value greater than “0” in Mtandire Ward of LC compared to T/A Kilupula of KD (Table  15 ). The results show that poor land management (PLM) has strong network value (9.554) in Mtandire Ward of LC and (0.951) in T/A Kilupula of KD, followed by RPA in Mtandire Ward of LC (3.839). These results point to the fact that the CL, RPA, EMS, PLM and IUR contribute to households flood vulnerability in LC and KD, with higher contribution in Mtandire Ward of LC (Table  16 ).

The results of communication linked with cultural factors revealed a strong relationship between in the sets of the combined indicators, all with value greater than “0” in Mtandire Ward of LC compared to T/A Kilupula of KD (Table  16 ). The results show that traditional beliefs (TB) have strong network value (79.789) in T/A Kilupula of KD compared to a network value of 7.872 in Mtandire Ward of LC followed by cultural conflicts with value of 11.864 in T/A Kilupula of KD compared to a value of 6.426 in Mtandire Ward of LC (Table  17 ).

3.1.2 Relationships between vulnerability factors and components

This section combined underlying vulnerability factors (UVFs) and vulnerability components (VCs) to determine indicators that integrate the two parameters to determine households’ vulnerability. The analysis was carried through bivariate statistical test after normalisation of indicators of UVFs and VCs (Table  18 ). The results between physical factors and exposure variables reveals significant relationships between proximity to rivers and settlements (0.0380) in KD, house type (0.0001) in LC and roofing material (0.0072) in Lilongwe and (0.0364) in KD.. The results reveal that all the susceptibility factors are significant to social factors. This result indicates that the susceptibility variables contribute to generate households’ vulnerability to floods in Mtandire ward of LC and T/A Kilupula of KD. The results show that communication accessibility, access to healthcare, access to water, and sanitation contribute to vulnerability to floods in LC and KD are all significant at P-value 0.05 in both Mtandire Ward and T/A Kilupula (Table  16 ). The results reveal that all the resilience variables are significant to economic factors in KD while only income of household head is significant in LC. This result indicates the resilience variables contribute to generate households’ economic vulnerability to floods in T/A Kilupula district than in Mtandire Ward (Table  18 ). The results reveal that some exposure variables combined with environmental variables contribute to household’s flood vulnerability. While geography contributes to very high vulnerability of households to floods in T/A Kilupula of KD (0. 0084), the same is not the case in Mtandire Ward of LC (0.864). House type contributes to very high vulnerability of households to floods in Mtandire Ward of LC compared to T/A Kilupula in KD while roofing material contributes to generate vulnerability in both Mtandire Ward of LC and T/A Kilupula of KD (Table  17 ). The combined results of susceptibility variables with human/cultural factors reveal that communication accessibility contributes to flood vulnerability in Mtandire Ward of LC (0.0002) and not in T/A Kilupula of KD (0.5136). The results further indicate that limited education facilities as well as health facilities contribute to vulnerability in T/A Kilupula of KD and not in Mtandire Ward of LC at p-value 0.05 (Table  18 ).

3.2 Quantification and prediction of household vulnerability

The binomial Logit Multiple Regression was computed in r to generate five scores outlined in the Eqs. 12 to 15 .

3.2.1 Computation of socio-susceptibility score

The underlying social vulnerability factors (SVFs) linked with communication accessibility (ca) in the susceptibility indicators generated the output of socio-susceptibility score (Eq.  12 ).

where S = Susceptibility, ca = communication accessibility, HR = human rights, HS = health services sint = scale of less important, svint = scale of very important.

The above output (Eq.  12 ) linked the susceptibility indicators (communication accessibility) with social variables. Therefore, to compute the scores in Lilongwe City (Mtandire Ward) and Karonga District (T/A Kilupula), the percentage values generated using descriptive statistics from the scale of “important” and “very important” were separately inputted in the equation (Eq.  12 ).

3.2.2 Computation of physio-exposure score

The underlying physical vulnerability factors (PVFs) linked with housing material types (hmt) in the exposure indicators generated the output of physio-exposure score (Eq.  13 ).

where E = Exposure, hmt = housing material type, PC = Poor construction, CM = Construction materials, CR = Construction of roads, sint = scale of less important, svint = scale of very important.

The output (Eq.  13 ) linked the exposure indicators (housing material type) with physical variables. Therefore, to compute the scores in Lilongwe City (Mtandire Ward) and Karonga District (T/A Kilupula), the percentage values generated using descriptive statistics from the scale of “important” and “very important” were separately inputted in the equation (Eq.  13 ).

3.2.3 Computation of eco-resilience score

The underlying economic vulnerability factors (EVFs) linked with income of household head (ihh) in the resilience indicators generated the output of eco-resilience score (Eq.  14 ).

where R = Resilience, ihh = income of household head, PV = Poverty, AL = Alternative livelihoods, sint = scale of less important, svint = scale of very important.

The output (Eq.  14 ) linked the resilience indicators (income of household head) with economic variables. Therefore, to compute the scores in Lilongwe City (Mtandire Ward) and Karonga District (T/A Kilupula), the percentage values generated using descriptive statistics from the scale of “important” and “very important” were separately inputted in the equation (Eq.  14 ).

3.2.4 Computation of enviro-exposure score

The underlying environmental vulnerability factors (EVFs) linked with geography (ge) in the exposure indicators generated the output of enviro-exposure score (Eq.  15 ).

where E = Exposure, Ge = Geography, CL = Cultivated land, EM = Environmental mismanagement, PLM = Poor land management, AUR = Inappropriate use of resources, sint = scale of less important, svint = scale of very important.

The output (Eq.  15 ) linked the exposure indicators (geography) with environmental variables. Therefore, to compute the scores in Lilongwe City (Mtandire Ward) and Karonga District (T/A Kilupula), the percentage values generated using descriptive statistics from the scale of “important” and “very important” were separately inputted in the equation (Eq.  15 ).

3.2.5 Computation of cultural-susceptibility score

The underlying cultural vulnerability factors (CVFs) linked with inaccessibility of communication (ic) in the susceptibility indicators generated the output of cultural-susceptibility score (Eq.  15 ).

where S = Susceptibility, cb = cultural behaviour, LN = local norms, sint = scale of less important, svint = scale of very important.

The output (Eq.  16 ) linked the susceptibility indicators (cultural behaviour) with cultural variables. Therefore, to compute the scores in Lilongwe City (Mtandire Ward) and Karonga District (T/A Kilupula), the percentage values generated using descriptive statistics from the scale of “important” and “very important” were separately inputted in the equation (Eq.  16 ).

The score measure of UVF (physical, social, economic, environmental and cultural) against VCs (exposure, susceptibility and resilience) generated a single value according to the association which was as follows: Physical with exposure factors (PEFs), Social with susceptibility factors (SSFs), economic with resilience factors (ERFs), environmental with exposure factors (EEFs) and cultural with susceptibility factors (CSFs). This association further generated value that was divided by the total sample size 345 and 200 household participants in Lilongwe City and Karonga District and multiplied by the 100 percent to obtain a percentage value of each category in the calibrated formula, for example:

Then the percentage result obtained in equation (Eq.  17 ) for each factor was further divided by 100% to generate the vulnerability level (extent of vulnerability) of each factor (i.e., V L PEFs). This computed arbitrary value was compared to the FVI to predict the extent of vulnerability per factor, for example:

where V L PEFs means the extent (level) of vulnerability to Physio-Exposure factors. This formula was applied to all the combined categories (i.e., SSFs, ERFs, EEFs and CSFs) by substituting the category that was required to be worked out in the equation to obtain the value that was used to determine vulnerability. Finally, the result was used to predict vulnerability in terms of “high vulnerability” and “very high vulnerability” per the FVI scale range. Ordinal categories for the indicators of vulnerability determinants (less important, important and very important) and indicators of elements at risk (not vulnerable, small vulnerable, vulnerable, highly vulnerable and very highly vulnerable) were used for selection of variables.

Finally, the relationship (using Eq.  18 ) generated results in the category of the physio-exposure factors (PEFs), social susceptibility factors (SSFs), eco-resilience factors (ERFs), enviro-exposure factors (EEFs) and cultural-susceptibility factors (CSFs) (Fig.  8 ).

The results of PEFs fall in scale range of “vulnerability” in Mtandire Ward of Lilongwe City (0.52) compared to “high vulnerability” in T/A Kilupula of Karonga District (0.64). This means that while it contributes to vulnerability in both areas, it is much higher in T/A Kilupula of KD compared to the Mtandire ward of LC. The results of the digitized flood maps overlayed with surveyed households’ showed that most houses that are highly vulnerable to floods are between a distance of 0.06–0.12 km to Lingadzi river in Mtandire ward of LC and 0.198–0.317 km along the buffer zones of Lufilya river in T/A Kilupula of KD (Figs.  9 and 10 ).

Map of Mtandire showing households/buildings about Wetlands and drainage systems

Map of T/A Kilupula showing households/buildings about Wetlands and drainage systems

4 Discussion

Though variations exist in the causes of vulnerability, the results of this study have demonstrated that the vulnerability of households to floods in rural and urban informal settlements is very high based on a lack of building materials, proximity to catchments, and limited communication among other factors. Similar, to this finding Alam et al., ( 2022 ) also found a high vulnerability value of 0.7015 for rural people living in the Dammar Char in Southeastern Bangladesh compared to urban areas. While, Alam et al. ( 2022 ), did not specify the causes of such high vulnerability, this study attributes the high vulnerability to the aspect of lack of construction materials, distance to markets and transport cost that people have to incur to access construction materials in rural areas. These causes agree with the findings of Qasim et al. ( 2016 ) in which vulnerability to flooding was attributed to poor/lack of materials used to construct houses. The results also revealed that poor construction of infrastructural facilities falls in the scale of “high flood vulnerability in both LC and KD. This implies that substandard construction of infrastructure such as houses contributes to vulnerability. This finding is supported by literature that substandard infrastructures contribute to flood vulnerability (Salami et al. 2017 ). Furthermore, the ANN results in MLP revealed a strong association of physical vulnerability factors (lack of construction materials, construction of infrastructures, and ageing infrastructures) with housing type. This implies that they contribute to generating vulnerability because people live in substandard houses. This finding confirms the result finding of Movahad et al. (2020) and Aliyu Baba Nabegu (2018) who indicated that people are vulnerable to floods because they usually live in substandard housing conditions which become prone to floods.

The SFFs generated a vulnerability value (0.61) for people living in T/A Kilupula in Karonga District compared to a low vulnerability value (0.2) for people living in Mtandire Ward in Lilongwe City. The above findings indicate that key factors for households’ flood vulnerability are associated with knowledge of building codes and standards. This means that the culture of shelter safety is lacking and that there is a lack of knowledge of the type of houses that they can build to resist floods and any other type of natural hazards. These could be attributed to dynamic pressures influencing households’ vulnerability to floods. That’s to say, people do have enough resources, decision-making, and societal skills to access housing materials that can help them build strong houses. In this situation, the programming of flood risk management and in general DRM mitigation, preparedness and recovery measures should focus on reducing the pressures by strengthening households’ knowledge and building standards. This can be achieved through designing mitigation measures that address the root causes that contribute to increased vulnerabilities in the pre-flood and post-flood phases rather than focusing too much on the trans-flooding phase. In terms of social-susceptibility vulnerability, the results found that the SSFs that contribute to generating vulnerability both in T/A Kilupula of KD and Mtandire Ward of LC are lack of access to health services, human rights, limited institutional capacities and lack of awareness. However, the binomial logistical regression of the SFFs generated a vulnerability value (0.61) for people living in the studied area of KD compared to a low vulnerability value (0.2) of people living in the studied area of LC. This finding differs from the findings of Munyai et al. ( 2019 ) in Muungamunwe Village in South Africa, which found that the value of FVI social was 0.80 higher than all the factors assessed. However, it is noted that the later study did not comprehensively link various factors between UVFs and VCs to determine the degree of contribution to vulnerability. The results further imply that the socio-susceptibility factors contribute to higher vulnerability in rural areas than in urban areas. This finding is supported by the study of Mwale ( 2014 ) in which social susceptibility was categorised from “high to very high vulnerability” among the communities in rural Lowershire of Chikwawa and Nsanje Districts of Malawi.

The ERFs contribute to “very high vulnerability” in Karonga (0.8) and “high vulnerability” in Mtandire Ward of Lilongwe City (0.6). The high vulnerability is linked to factors such as poverty, lack of alternative livelihoods, and lack of income-generating activities. Similar to these results, the study of Mwale ( 2014 ) also established a predominantly very high economic susceptibility based on causes such as a lack of economic resources, an undiversified economy and a lack of employment opportunities among communities in the lower Shire Valley of Malawi. Despite the results revealing the same outcome, the earlier study linked economics with susceptibility measures while this study agglomerated economics with resilience measures. The existing variation placed some causes in different association order. For example, poverty in the study of Mwale ( 2014 ) was categorised as a social susceptibility indicator, while in this study it was used as the eco-resilience measure. The understanding of this study is that poverty is a measure of the income level of a household. That is to say, a household with enough income will be less poor thereby becoming more resilient and vice versa. Therefore, poverty was classified as a cause of “high vulnerability” both in Mtandire Ward of LC with a value of 0.73 and T/A Kilupula of KD with a value of 0.68. On the other hand, the lack of alternative livelihoods contributes to “vulnerability” in Mtandire Ward with a value of 0.54 while ‘high vulnerability” in T/A Kilupula with a value of 0.71). These findings point out the notion that programming current and future flood disaster mitigation plans and vulnerability reduction measures requires the formulation of relevant financial and economic measures which may contribute to poverty alleviation in the community and society.

The EEFs revealed “very high vulnerability” in both Mtandire Ward of LC (0.8) and T/A Kilupula of KD (0.9). The EEFs revealed “very high vulnerability” of EEFs (0.8) in Mtandire Ward and (0.9) in T/A Kilupula. Except for the pressure on cultivated land in Mtandire Ward, all underlying environmental vulnerability factors (UEVFs) contribute to vulnerability in both rural and urban areas. This result points out that pressure on land is an environmental indicator that predicts households’ vulnerability to floods in rural areas (T/A Kilupula) and not in urban areas (Mtandire Ward). The high vulnerability depicted by the EEFs is a total indication that households are more vulnerable due to the built environment. This could be attributed to the fact that people have allowed development in areas where danger exists due to the lack of policy and legal systems to help and guide government and enterprises in disaster risk management. This argument is supported by literature that development in dangerous areas increases peoples’ exposure to danger (Birkmann et al. 2013 ; Nazeer and Bork 2021 ). Barbier et al. (2012) support that environmental damage affects the well-being of the local people since it leads to soil degradation which eventually causes low food production. To this end, laws and policies to regulate development and habitation in risk areas should be seamlessly programmed into the current and future flood mitigation and preparedness plans at all levels.

Finally, the CSFs revealed a low vulnerability in both Mtandire Ward of LC (0.34) and T/A Kilupula of KD (0.39) (Fig.  6 ). In the FVI scale, the SSFs and CSFs contribute to low vulnerability in Mtandire Ward of LC while only the CSFs contribute to low vulnerability in T/A Kilupula of KD (Fig.  3 ). The CSFs show a value of 0.34 in Mtandire Ward and 0.39 in T/A Kilupula, indicating that it contributes to low vulnerability in both areas. However, it was established that household flood vulnerability in T/A Kilupula is high due to other factors such as cultural beliefs of conserving their ancestors’ graveyards and land ownership issues . In support of this result, Iloka ( 2017 ) found that a system of beliefs regarding hazards and disasters contributes to vulnerability. The findings of the author further established that cultural issues do not assist households to be resilient to floods. In Mtandire Ward of LC, it was observed that land use and human occupancy in risk areas contribute to household flood vulnerability. Furthermore, it was reported that rich people have occupied places which are not habitable thereby changing the course of the Lingadzi River. Further to this, youths have resorted to destroying the banks of the river due to a lack of economic activities and high unemployment. It was noted that people do not fear or abide by city regulations because there is no punishment that they receive from city councils.

4.1 FVA indicators for rural and urban informal settlements

Based on the results, and to provide proper flood mitigation and programming of current and future challenges in flood management, this study constructed the FVA framework as a combination of variables from the UVFs and VCs (Fig.  11 ). On the one hand, the physio-exposure indicators (PEIs) relate to the housing and infrastructure in the physical vulnerability factors (PVFs). These should be evaluated based on exposure with its operator house material and type to understand how they contribute to vulnerability (Eq.  13 ). In Fig.  11 , those that intersect (housing typology (HT), poor construction of standards (PCS), lack of building materials (LBM) and loss of physical assets (LPA) and infrastructural standards) are the PEIs for both rural and urban areas. While location (LC) and growth of informal settlement (GIS) are PEIs for rural and urban areas respectively. On the other hand, the enviro-exposure indicators (EEIs) relate to environmental causes such as land use planning and management. These were quantified based on exposure variables, specifically location (Eq.  15 ). In the Fig.  11 , environmental mismanagement (EM), proximity to rivers (PR), poor land management (PLM), inappropriate use of resources (IUR) and siltation of rivers (SR), river catchment morphology (RCM) flooding risk location (FRL) intersect, implying that they are the EEIs for both rural and urban informal areas. Those outside the intersection apply specifically as EEIs conforming either in Lilongwe including waste management (WM), land use planning (LUP) or in Karonga, cultivated land (CL) and topography (TP).

FVA framework

This study derived the physio-exposure indicators (PEIs) and enviro-exposure indicators (EEIs) by agglomerating them with the exposure factors (housing material and geography respectively). This demonstrates the notion that flood risk is a product of exposure to the hazard (flood) and vulnerability. Literature reveals that exposure entails the probability of flooding affecting physical objects-buildings and people (Mwalwimba 2024 ; Balica et al. 2012 ; Nazeer and Bork 2021 ) due to location. Since location is an exposure variable, defined by the geographical position to which the assessment was done (Nazeer et al. 2022), this study relates the physical causes to that location/geography to predict household vulnerability and thereby all the significant indicators were grouped as physio-exposure factors (PEFs) to give rise to the PEI. Also, significant indicators were grouped as enviro-exposure factors (EEFs) and referred to as the EEIs in Fig.  11 . The PEIs and EEIs correlate with the indicators propagated in the hazard of place model (Joakim 2008 ), which relates the vulnerability determinates to biophysical vulnerability i.e. geography, location and proximity.

The amount of social risk experienced by the household was understood by agglomerating socio-susceptibility indicators (SSIs). The SSIs relate to the linkage of social causes with access to communication as a susceptibility variable (Eq.  12 ). Susceptibility deals with elements that influence an individual or household to respond to the hazard itself. In Fig.  11 , the SSIs, lack of access to health services (LHS), communication accessibility (CA), access to training and advocacy (ATA) and level of sanitation (LS) are indicators that intersect, implying they apply to both rural and urban informal areas. However, lack of human rights (LHR) and level of waste management and drainage systems (LWDS) are SSIs in rural and urban respectively. Relatedly, cultural-susceptibility indicators (CSIs) link cultural causes with access to communication in the susceptibility category. Susceptibility deals with elements that influence an individual or household to respond to the hazard itself. In Fig.  11 , lack of personal responsibility, lack of adherence to regulations, lack of institutional support and flood perception are indicators that intersect, implying that they are the CSIs for both rural and urban areas. However, cultural beliefs and myths about floods should be indicators to be evaluated specifically in rural areas, while power conflicts, limited DRR strategies and lack of cooperation should be used to assess vulnerability in urban areas, though they can apply to rural areas too. So, access to communication is a susceptibility condition which may result in making households vulnerable to floods because they cannot anticipate the impending flooding. Hence this study related social and cultural causes with access to communication to develop a combination of socio-susceptibility factors (SSFs) and cultural-susceptibility factors (CSFs). Qasim et al. ( 2016 ) stated that certain beliefs and poverty play a role in the lack of resilience among communities. Birkmann et al. ( 2013 ) and Kablan et al. ( 2017 ) stated that susceptibility relates to the predisposition of the elements at risk in social and ecological spheres. Hence, most of the susceptibility factors relate to social and cultural causes because they are all an integral part of humanity's suffering if conditions do not support them to withstand and resist the natural hazard impacts.

The eco-resilience indicators (ERIs) should put much emphasis on economic causes of vulnerability. Economic indicators such as limited access to alternative livelihoods and poverty contribute to generating vulnerability. These indicators may or may not be affected by the resilience of households to the shock. As such, resilience is measured based on the ability of the households to cope with the event. As such, key factors to measure resilience include access to resources, improved livelihoods and access to income among others. The framework therefore strongly overlaps economic causes with resilience factors to assess the vulnerability of households to floods. In Fig.  11 , poverty (PO), limited livelihoods (LVs), lack of income of household head (LIHH), and loss of economic assets (LEA) are indicators that intersect, implying that they are eco-resilience indicators (ERIs) that can be used for vulnerability assessment in both rural and urban areas. The ERIs for only rural lack of markets (LM), limited credit unions (LCU) and reduction in agricultural land (RAL) while in urban informal settlements, they include lack of employment opportunities (LEO). Birkmann et al. ( 2013 ) stipulated that resilience comprises pre-event risk reduction, time-coping, and post-event response actions. Therefore, this study relates the economic causes of resilience to give rise to the eco-resilience indicators (ERIs) (Fig.  11 ).

The adaptive capacity provides key adaptive measures that can be incorporated to deal with vulnerability conditions generated from each intersected category. The adaptive measures relating to housing strategies can be utilised to minimised flood impact on households under the physio-exposure factors in the category of the PEIs are strengthening the availability of building materials (SULBM), enforcement of building codes and standards (EBCS) and empowering locals on flood resilient structures (ELFRS). Similarly, social organisational measures can be utilised to minimise socio-susceptibility factors relating to SSIs. The adaptive capacities that can contribute to reducing vulnerability in the category are the ability to make decisions (AMD), the ability to organise and coordinate (AOC) and communal strategic grains for resilient buildings (CSGRB). In addition, the economic measures can be utilised to minimise flood impacts relating to eco-resilience factors for the category of ERIs and they include saving agricultural produce (SAP), strengthening diversification (SD) strengthening livelihoods opportunities (SLO) can be used as adaptive capacity under this category. In terms of exposure, households to adapt to flood impact can use land management measures. These practices include: elevating house location (EHL), afforestation and re-afforestation (AR) and building dykes and embankments (BDE) can be used as adaptive capacity under this category. Finally, households can minimise the cultural-susceptibility factors that generate their vulnerability through the application of warning systems for impending flooding (WS) and the use of indigenous and scientific knowledge (ISK). This is contrary to the PAR model (Wisner et al., 2004 ) and Urban Flood Vulnerability Assessment (Salami et al. 2017 ), which did not elaborate the adaptive strategies. However, the FVA relates well with the ISDR framework (2004) on adaptive capacity because the ISDR (2004) emphasizes disaster risk reduction through adaptive responses such as awareness knowledge, development of public commitment, application of risk reduction measures, early warning and preparedness (Mwale 2014 ).

5 Assumptions of the FVA framework

Assumptions are key to the realisation of the results. They are critical for achieving the successful implementation of an intervention. In this regard, the fact that the FVA framework provides the indicators which can be used to assess flood vulnerability in rural and urban informal settlements, the following eleven assumptions are vital to achieving the results:

The UFV should be constituted by physical, social, economic, environmental and cultural factors while the VC is composed of exposure, susceptibility and resilience to determine flood vulnerability. The selection of variables for these key components should consider vulnerability in a combination of physical and social sciences.

The UVFs and VCs should be linked to generate Physio-Exposure Factors (PEF), Socio-Susceptibility Factors (SSF), Eco-Resilience Factors (ERF), Enviro-Exposure Factors (EEF) and Cultural-Susceptibility Factors (CSF) to determine flood vulnerability or any particular hazard.

The generated indicators in the PEF, SSF, ERF, EEF and CSF should lead to the production of physio-exposure indicators (PEIs), social susceptibility indicators (SSIs), eco-resilience indicators (ERIs), enviro-exposure indicators (EEIs) and cultural-susceptibility indicators (CSIs), which in turn should capture indicators for FVA framework (Fig.  11 ).

A comprehensive flood vulnerability assessment framework that can give rise to multi-hazard vulnerability assessment should deviate from the common systematisations of vulnerability by using one set of variables. A combination of UVFs and VCs should be used to generate a wide range of issues and variables.

The linkage between the factors that amplify vulnerability and those that can enhance vulnerability reduction should be demonstrated through adaptive capacity and disaster risk reduction measures and incorporated in the framework. Those that cannot be quantified should be supported by qualitative methods.

The linkage of the UVFs and VCs as a key explanation of the generation of vulnerability should be emphasised and the conceptual framework for FVA should provide clear connectivity of the variables of the UVFs and VCs.

The variables for UVFs (physical, social, economic, environmental and cultural) should be measured as the absolute proportion value of household participants involved during the survey. The percentage values should be generated using a scale range with operators of “ less important ”; “ important ” and “ very important ” to contribute to flood vulnerability”. However, for flood vulnerability determination, a cut-off point should be placed at greater or equal to 0.5 (50%) for each indicator from the operator of the scale range of “important” and “very important”. In this case, all the values generated in the scale of “less important” as responded by the participants should be left out during determination of flood vulnerability.

The selected variables UVFs indicators (at 50%) should be tested using the variables of VCs (exposure, susceptibility and resilience) in the order stipulated in 2 and 3 through statistical tests using P-values or correlation (r) or simply any statistical test applicable by the researcher. The values that are significant at a certain confidence level (i.e. 0.05 in this study) should be selected to be included in the framework for specific combinations like PEFs, SSFs, ERFs, EEFs and CSFs (Fig.  11 ). Furthermore, those values significant at an appropriate confidence level should be considered as factors generating flood vulnerability.

Household vulnerability to floods should be predicted based on logistical regression test between the UVFs for all the operators of less important, important and very important and the VCs indicators (in exposure, susceptibility and resilience). The selection of the VC indicators should be based on those that were significant during the statistical test. Furthermore, variance independent factor (VIF) should be used to check the multicollinearity of the indicators for computation in the regression model.

Demographic characteristics should be statistically tested to determine their significant level of P-value 0.05 with the underlying vulnerability factors (UVFs) to explain who is vulnerable to what. However, because other explanations might be hidden in a quantitative assessment, a qualitative –in-depth assessment must be done to understand those hidden issues per se. In so doing, the assessment would be informative in identifying the factors that give rise to the pressures that generate vulnerable conditions in society for different groups.

Adaptive capacity should be assessed both quantitatively and qualitatively since it is a component of vulnerability reduction. This entails that if adaptive capacity is sufficient, it is likely that households' response to floods would be high and vulnerability is also likely to reduce and vice versa.

5.1 FVA application and comparability

The FVA should be applied as a pre-hazard, trans-hazard and post-hazard (flood) tool. In the pre-hazard category, all the proposed indicators should be used to determine vulnerable conditions which may (or may not) put some households at risk of flood disaster in the event of a flood occurrence. In the trans-hazard, the FVA indicators should be used to determine the vulnerabilities of households to identify the households that have been affected by floods as part of the disaster response and recovery process. In so doing, the FVA indicators should be used as a means of establishing strategies for disaster response and recovery as part of building back better. As a post-hazard tool, indicators should be used to determine the vulnerabilities that contributed to a disaster situation. Users should prioritize these indicators as a means of building DRR for disaster rehabilitation and reconstruction. In this case, the FVA framework contrasts itself to available tools such as the Unified Beneficiary Register (UBR) and Hazard Rapid Assessment (HRA) which largely are implemented only after the hazard in Malawi. Furthermore, it separates indicators that generate vulnerability in subsectors, but most available frameworks do not portray this separation. Therefore, participating enterprises can implement the FVA framework based on the needs of the assessment. The FVA framework can be implemented through hydrological assessment, flood modelling, quantitative, qualitative, GIS and remote sensing methodologies, giving opportunity to multiple users. The framework emphasizes UVFs (physical, social, economic, environmental and cultural) and VCs (exposure, susceptibility and resilience) as intersection constructs of flood vulnerability in urban and rural areas of Malawi and other places where it can be applied. It provides very simplified indicators of assessing flood vulnerability at local and national levels, deviating from the generalised frameworks that look at a wider scale like the PAR model (Wisner et al., 2004 ). More importantly, the framework provides tailor-made indicators thereby localizing the assessment of flood vulnerability in Malawi. This framework gives indicators that can be easily measured and evaluated at any level using different tools (statistical applications) thereby giving empirical scientific data on floods. The framework is coined strategically for researchers to utilise it in measuring the vulnerability of a single underlying factor of interest (i.e., physical vulnerability or social vulnerability etc.). It also gives simplified indicators that can be utilised by policy and decision-makers for planning interventions. The framework provides a good alignment of adaptive capacity to underlying vulnerability factors and components. In this case, the framework integrates DRR into vulnerability reduction strategies. Unlike the PAR model (Wisner et al., 2004 ) which does not explain exactly the measures of vulnerability reduction, this framework, through the integration of adaptive capacity, has filled up this gap. Finally, the framework intersects the significant factors of vulnerability in a set theory analysis giving new thinking in outlining FVA indicators in Malawi and beyond. The framework goes beyond the Community-Based Disaster Risk Index (CBDRI) by Bollin et al. (2003) which provides a proper link of indicators between vulnerability factors and components. For example, the CBDRI considers vulnerability components as structure, population, economy, environmental and capacity measures (Mwale et al. 2015 ) yet alone these could be grouped as conditions that generate flood vulnerability as tested in the FVA framework.

From the findings of this study, the FVA is comparable with various contemporary disaster management frameworks such as the PAR Model (Wisner et al. 2014 ), the Hazard of Place Framework (Cutter 1996 ), the Sustainable Livelihood Model (2004), the Community-based Disaster Risk Management Model (Kelman 2010 ), Turner et al. (2003) framework and the International Disaster Risk Reduction Framework (ISDR 2004). Therefore, based on the indicators intersected in Fig.  11 (such as housing conditions, access to information, access to resources, poor land use, social networks, and location), the FVA framework correlates well with most of the indicators stipulated in Hazard of place model (Cutter 1996 ), PAR model (Winser et al. 2014 ), Urban Flood Vulnerability Assessment Framework (Salami et al. 2017 ), ISDR framework (2004). However, the FVA framework has provided simplified indicators of flood vulnerability assessment because the indicators are simple to be used by experts and non-experts whether they are in urban or rural areas. They can be easily understood by ordinary users and policymakers. Furthermore, the indicators can be used for multi-hazards vulnerability assessment, since the H and F in the constituted equation can be changed based on hazard. In this case, the FVA Framework is widening vulnerability assessment beyond a focus on floods. The FVA, therefore, eliminates the gaps that most studies in literature mainly focus on, single hazards, ignoring the multi-hazard assessment (Kamanga et al. 2020 ). The FVA includes variables that can be measurable through quantitative and ANN (machine learning platform) thereby expanding the process of vulnerability analysis.

The FVA separated the indicators that generate vulnerability in different subsectors of UVFs and VCs. This separation deviates from most of the contemporary frameworks. Joakim ( 2008 ) noted that most contemporary frameworks fail to portray the linkages and networks that exist with the layers or sections leading to the vulnerability. For example, the PAR (Wisner et al. 2014 ) model provides a generalised causation of vulnerability. It portrays the progression of vulnerability from root causes to unsafe conditions, but it fails to explicitly acknowledge the linkages that exist within each progression (Joakim 2008 ). The FVA has provided a straightforward linkage of indicators by systematizing and assessing vulnerability in different subsectors. Similarly, the International Strategy for Disaster Reduction (ISDR) (2004) framework, the Hazard of Place Framework (HOP) (Cutter 1996 ), Borgardi, Birkmann and Cadona (BCC) (2004) and the Turner et al. (2003) framework, all have methodological difficulty of translation of some concepts into practice (Mwale 2014 ). This methodological variation, further makes the contemporary frameworks to be difficult to incorporate different links that exist between vulnerability factors. Mwale ( 2014 ) argues that the HOP framework does not provide a causal explanation of the vulnerability, instead variables are selected the way they are. Joakim ( 2008 ) further noted that the applicability of the HOP framework is a Canadian context, giving an impression that some indicators might manifest themselves differently in small political, economic and social processes. However, the HOP framework in some instances, relates very well with FVA, particularly the inclusion of perceptions, emphasis on understanding the underlying vulnerability factors, and inclusion of mitigation and adaptive capacity in the analysis of vulnerability. It is also highlighted that Turner II et al.’s (2003) framework is too theoretical and lacks specificity (Mwale 2014 ). This means that the framework is not simple and easy to use. The ISDR (2004) does not link the preparedness response system and thereby not explicit on how vulnerability can be reduced. Also, the use of one-dimensional indicators is demonstrated in the Turner II et al. (2003) framework which defines vulnerability in terms of exposure, susceptibility and responses. For this part, the ISDR (2004) defines vulnerability in the realms of social, economic, environmental and physical (Mwale 2014 ), missing the aspects of exposure, susceptibility and resilience. Above all, most of these frameworks have neglected to agglomerate the UVFs and VCs in their analysis and development of vulnerability frameworks. These FVA have attempted to fill these gaps, giving vulnerability assessment a new direction. In Malawi and SSA in general, Mwale et al. ( 2015 ) in a study of contemporary disaster management framework quantification of flood risk in rural lower Shire Valley, Malawi found medium, high and very high flood vulnerability in the same construct of indicators of the FVA framework. This implies that the FVA indicators are locally comparable and can be used for the decision-making process. The FVA indicators are more practical and can ably enhance community and household resilience. These indicators can thus be applied in promoting the resilience of communities to mitigate flood risks and key components for planning and decision-making processes.

6 Conclusion

This study carried out flood vulnerability assessment (FVA) using quantitative methods by utilising MCA, ANN (machine learning) and multiple logistical regression. The high flood vulnerability and lack of adaptive capacity among the households and communities in rural and urban informal settlements is an indication that catchment management in most areas remains a challenge to the water sector, disaster professionals and other players. This study highlights place settlement (proximity to catchments), low-risk knowledge, limited access to communication, poor sanitation, limited institutional capacity, and lack of alternative livelihoods as key drivers of flood vulnerability. These, among others, prevent households near the catchments from living in harmony and at peace with their water resources catchments. As the FVA framework specifies the indicators that contribute to flood vulnerability in rural and urban informal settlements, it is important to consider shifting towards investing in the adaptive capacity of communities along the catchments for better resilience building. The FVA framework considered adaptive capacity to mean actions taken by households to manage their catchments and livelihoods before, during and after floods. The adaptive measures entail the level of resilience households would be (or would not be) to floods. This study considered it crucial to constitute this framework in this manner to provide a roadmap for identifying the underlying causes of household levels of vulnerability to floods. This flood vulnerability assessment framework is applicable for both rural and urban and could be fit for purpose in sectors such as climate change, water resources management, disaster risk management, disaster risk reduction, integrated water management, food security, health, environmental management, engineering etc. The government might find the framework significant to establish clear regulations and accountability mechanisms to ensure that their involvement genuinely contributes to sustainable and equitable outcomes. Enterprises would find the framework useful for mapping vulnerability to natural hazards to address current and future risks in communities, including building community resilience and a line of separation with government.

7 Recommendations

The FVA framework is the first attempt to agglomerate operators in the UVFs and VCs through a multicollinearity analysis in a logit multiple regression to give rise to indicators in the PEIs, SSIs, ERIs, EEIs and CSIs for flood vulnerability assessment. The framework emphasises both understanding the conditions that generate vulnerability and those that can reduce vulnerability. Therefore, the study emphasises that the Malawi government through the Department of Disaster Management Affairs (DODMA) should strengthen disaster risk reduction by maintaining (1) political responsibility through the formulation of public policies with a clear understanding of people’s vulnerabilities (2) Legal responsibility through incorporating the framework as a way of perfecting the legal system, enforce the laws and establish laws that are not a centric symbol of disaster enterprise (3) Social responsibility through applying the framework to harmonise systems to be fair and just, without treating others in a sense of societal leniency, greenwashing practices and prioritisation of profit over environmental and social responsibility (4) Economic responsibility through utilising the framework to formulate relevant financial and economic measures i.e. disaster risk funds, to make disaster funds not to base on the declaration of a disaster.

Similarly, mapping vulnerability to natural hazards in urban areas should be enhanced to provide data necessary for developing disaster risk awareness and communication strategies vital to strengthening urban risk knowledge of natural hazards. The framework should be applied in promoting the resilience of communities to mitigate flood risks and can be a key component for planning and decision-making processes both in rural and urban areas. Finally, this study focused on one rural area and one urban informal area, so there is a need for district-wide or city-wide study and/or there is a need for study in urban between planned settlement and unplanned traditional housing areas (UTHA).

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The publication of this research received financial support from the NORHED Project on Climate Change and Ecosystem Management in Malawi and Tanzania (#63826) and the Centre for Resilient Agri-Food Systems (CRAFS) under the Africa Centres of Excellence (ACE2) programme.

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Mwalwimba, I.K., Manda, M. & Ngongondo, C. Flood vulnerability assessment in rural and urban informal settlements: case study of Karonga District and Lilongwe City in Malawi. Nat Hazards (2024). https://doi.org/10.1007/s11069-024-06601-5

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