the case study earthquake

Learning from Megadisasters: A Decade of Lessons from the Great East Japan Earthquake

March 11, 2021 Tokyo, Japan

Authors: Shoko Takemoto,  Naho Shibuya, and Keiko Sakoda

Today marks the ten-year anniversary of the Great East Japan Earthquake (GEJE), a mega-disaster that marked Japan and the world with its unprecedented scale of destruction. This feature story commemorates the disaster by reflecting on what it has taught us over the past decade in regards to infrastructure resilience, risk identification, reduction, and preparedness, and disaster risk finance.  Since GEJE, the World Bank in partnership with the Government of Japan, especially through the Japan-World Bank Program on Mainstreaming Disaster Risk Management in Developing Countries has been working with Japanese and global partners to understand impact, response, and recovery from this megadisaster to identify larger lessons for disaster risk management (DRM).

Among the numerous lessons learned over the past decade of GEJE reconstruction and analysis, we highlight three common themes that have emerged repeatedly through the examples of good practices gathered across various sectors.  First is the importance of planning. Even though disasters will always be unexpected, if not unprecedented, planning for disasters has benefits both before and after they occur. Second is that resilience is strengthened when it is shared .  After a decade since GEJE, to strengthen the resilience of infrastructure, preparedness, and finance for the next disaster, throughout Japan national and local governments, infrastructure developers and operators, businesses and industries, communities and households are building back better systems by prearranging mechanisms for risk reduction, response and continuity through collaboration and mutual support.  Third is that resilience is an iterative process .  Many adaptations were made to the policy and regulatory frameworks after the GEJE. Many past disasters show that resilience is an interactive process that needs to be adjusted and sustained over time, especially before a disaster strikes.

As the world is increasingly tested to respond and rebuild from unexpected impacts of extreme weather events and the COVID-19 pandemic, we highlight some of these efforts that may have relevance for countries around the world seeking to improve their preparedness for disaster events.

Introduction: The Triple Disaster, Response and Recovery

On March 11th, 2011 a Magnitude 9.0 earthquake struck off the northeast coast of Japan, near the Tohoku region. The force of the earthquake sent a tsunami rushing towards the Tohoku coastline, a black wall of water which wiped away entire towns and villages. Sea walls were overrun. 20,000 lives were lost. The scale of destruction to housing, infrastructure, industry and agriculture was extreme in Fukushima, Iwate, and Miyagi prefectures. In addition to the hundreds of thousands who lost their homes, the earthquake and tsunami contributed to an accident at the Fukushima Daiichi Nuclear Power Plant, requiring additional mass evacuations. The impacts not only shook Japan’s society and economy as a whole, but also had ripple effects in global supply chains. In the 21st century, a disaster of this scale is a global phenomenon.

The severity and complexity of the cascading disasters was not anticipated. The events during and following the Great East Japan Earthquake (GEJE) showed just how ruinous and complex a low-probability, high-impact disaster can be. However, although the impacts of the triple-disaster were devastating, Japan’s legacy of DRM likely reduced losses. Japan’s structural investments in warning systems and infrastructure were effective in many cases, and preparedness training helped many act and evacuate quickly. The large spatial impact of the disaster, and the region’s largely rural and elderly population, posed additional challenges for response and recovery.

Ten years after the megadisaster, the region is beginning to return to a sense of normalcy, even if many places look quite different. After years in rapidly-implemented temporary prefabricated housing, most people have moved into permanent homes, including 30,000 new units of public housing . Damaged infrastructure has been also restored or is nearing completion in the region, including rail lines, roads, and seawalls.

In 2014, three years after GEJE, The World Bank published Learning from Megadisasters: Lessons from the Great East Japan Earthquake . Edited by Federica Ranghieri and Mikio Ishiwatari , the volume brought together dozens of experts ranging from seismic engineers to urban planners, who analyzed what happened on March 11, 2011 and the following days, months, and years; compiling lessons for other countries in 36 comprehensive Knowledge Notes . This extensive research effort identified a number of key learnings in multiple sectors, and emphasized the importance of both structural and non-structural measures, as well as identifying effective strategies both pre- and post-disaster. The report highlighted four central lessons after this intensive study of the GEJE disaster, response, and initial recovery:

1) A holistic, rather than single-sector approach to DRM improves preparedness for complex disasters; 2) Investing in prevention is important, but is not a substitute for preparedness; 3) Each disaster is an opportunity to learn and adapt; 4) Effective DRM requires bringing together diverse stakeholders, including various levels of government, community and nonprofit actors, and the private sector.

Although these lessons are learned specifically from the GEJE, the report also focuses on learnings with broader applicability.

Over recent years, the Japan-World Bank Program on Mainstreaming DRM in Developing Countries has furthered the work of the Learning from Megadisasters report, continuing to gather, analyze and share the knowledge and lessons learned from GEJE, together with past disaster experiences, to enhance the resilience of next generation development investments around the world. Ten years on from the GEJE, we take a moment to revisit the lessons gathered, and reflect on how they may continue to be relevant in the next decade, in a world faced with both seismic disasters and other emergent hazards such as pandemics and climate change.

Through synthesizing a decade of research on the GEJE and accumulation of the lessons from the past disaster experience, this story highlights three key strategies which recurred across many of the cases we studied. They are:

1) the importance of planning for disasters before they strike, 2) DRM cannot be addressed by either the public or private sector alone but enabled only when it is shared among many stakeholders , 3) institutionalize the culture of continuous enhancement of the resilience .

For example, business continuity plans, or BCPs, can help both public and private organizations minimize damages and disruptions . BCPs are documents prepared in advance which provide guidance on how to respond to a disruption and resume the delivery of products and services. Additionally, the creation of pre-arranged agreements among independent public and/or private organizations can help share essential responsibilities and information both before and after a disaster . This might include agreements with private firms to repair public infrastructures, among private firms to share the costs of mitigation infrastructure, or among municipalities to share rapid response teams and other resources. These three approaches recur throughout the more specific lessons and strategies identified in the following section, which is organized along the three areas of disaster risk management: resilient infrastructure; risk identification, reduction and preparednes s ; and disaster risk finance and insurance.

Lessons from the Megadisaster

Resilient Infrastructure

The GEJE had severe impacts on critical ‘lifelines’—infrastructures and facilities that provide essential services such as transportation, communication, sanitation, education, and medical care. Impacts of megadisasters include not only damages to assets (direct impacts), but also disruptions of key services, and the resulting social and economic effects (indirect impacts). For example, the GEJE caused a water supply disruption for up to 500,000 people in Sendai city, as well as completely submerging the city’s water treatment plant. [i] Lack of access to water and sanitation had a ripple effect on public health and other emergency services, impacting response and recovery. Smart investment in infrastructure resilience can help minimize both direct and indirect impacts, reducing lifeline disruptions. The 2019 report Lifelines: The Resilient Infrastructure Opportunity found through a global study that every dollar invested in the resilience of lifelines had a $4 benefit in the long run.

In the case of water infrastructure , the World Bank report Resilient Water Supply and Sanitation Services: The Case of Japan documents how Sendai City learned from the disaster to improve the resilience of these infrastructures. [ii] Steps included retrofitting existing systems with seismic resilience upgrades, enhancing business continuity planning for sanitation systems, and creating a geographic information system (GIS)-based asset management system that allows for quick identification and repair of damaged pipes and other assets. During the GEJE, damages and disruptions to water delivery services were minimized through existing programs, including mutual aid agreements with other water supply utility operators. Through these agreements, the Sendai City Waterworks Bureau received support from more than 60 water utilities to provide emergency water supplies. Policies which promote structural resilience strategies were also essential to preserving water and sanitation services. After the 1995 Great Hanshin Awaji Earthquake (GHAE), Japanese utilities invested in earthquake resistant piping in water supply and sanitation systems. The commonly used earthquake-resistant ductile iron pipe (ERDIP) has not shown any damage from major earthquakes including the 2011 GEJE and the 2016 Kumamoto earthquake. [iii] Changes were also made to internal policies after the GEJE based on the challenges faced, such as decentralizing emergency decision-making and providing training for local communities to set up emergency water supplies without utility workers with the goal of speeding up recovery efforts. [iv]

Redundancy is another structural strategy that contributed to resilience during and after GEJE. In Sendai City, redundancy and seismic reinforcement in water supply infrastructure allowed the utility to continue to operate pipelines that were not physically damaged in the earthquake. [v] The Lifelines report describes how in the context of telecommunications infrastructure , the redundancy created through a diversity of routes in Japan’s submarine internet cable system  limited disruptions to national connectivity during the megadisaster. [vi] However, the report emphasizes that redundancy must be calibrated to the needs and resources of a particular context. For private firms, redundancy and backups for critical infrastructure can be achieved through collaboration; after the GEJE, firms are increasingly collaborating to defray the costs of these investments. [vii]

The GEJE also illustrated the importance of planning for transportation resilience . A Japan Case Study Report on Road Geohazard Risk Management shows the role that both national policy and public-private agreements can play. In response to the GEJE, Japan’s central disaster legislation, the DCBA (Disaster Countermeasures Basic Act) was amended in 2012, with particular focus on the need to reopen roads for emergency response. Quick road repairs were made possible after the GEJE in part due to the Ministry of Land, Infrastructure, Transport and Tourism (MLIT)’s emergency action plans, the swift action of the rapid response agency Technical Emergency Control Force (TEC-FORCE), and prearranged agreements with private construction companies for emergency recovery work. [viii] During the GEJE, roads were used as evacuation sites and were shown effective in controlling the spread of floods. After the disaster, public-private partnerships (PPPs) were also made to accommodate the use of expressway embankments as tsunami evacuation sites. As research on Resilient Infrastructure PPPs highlights, clear definitions of roles and responsibilities are essential to effective arrangements between the government and private companies. In Japan, lessons from the GEJE and other earthquakes have led to a refinement of disaster definitions, such as numerical standards for triggering force majeure provisions of infrastructure PPP contracts. In Sendai City, clarifying the post-disaster responsibilities of public and private actors across various sectors sped up the response process. [ix] This experience was built upon after the disaster, when Miyagi prefecture conferred operation of the Sendai International Airport   to a private consortium through a concession scheme which included refined force majeure definitions. In the context of a hazard-prone region, the agreement clearly defines disaster-related roles and responsibilities as well as relevant triggering events. [x]

Partnerships for creating backup systems that have value in non-disaster times have also proved effective in the aftermath of the GEJE. As described in Resilient Industries in Japan , Toyota’s automotive plant in Ohira village, Miyagi Prefecture lost power for two weeks following GEJE. To avoid such losses in the future, companies in the industrial park sought to secure energy during power outages and shortages by building the F-Grid, their own mini-grid system with a comprehensive energy management system. The F-Grid project is a collaboration of 10 companies and organizations in the Ohira Industrial Park. As a system used exclusively for backup energy would be costly, the system is also used to improve energy efficiency in the park during normal times. The project was supported by funding from Japan’s “Smart Communities'' program. [xi] In 2016, F-grid achieved a 24 percent increase in energy efficiency and a 31 percent reduction in carbon dioxide emissions compared to similarly sized parks. [xii]

Schools are also critical infrastructures, for their education and community roles, and also because they are commonly used as evacuation centers. Japan has updated seismic resilience standards for schools over time, integrating measures against different risks and vulnerabilities revealed after each disaster, as documented in the report Making Schools Resilient at Scale . After the 2011 GEJE, there was very little earthquake-related damage; rather, most damage was caused by the tsunami. However, in some cases damages to nonstructural elements like suspending ceilings in school gymnasiums limited the possibility of using these spaces after the disaster. After the disaster, a major update was made to the policies on the safety of nonstructural elements in schools, given the need for higher resilience standards for their function as post-disaster evacuation centers [xiii] .

Similarly, for building regulations , standards and professional training modules were updated taking the lessons learned from GEJE. The Converting Disaster Experience into a Safer Built Environment: The Case of Japan report highlights that, legal framework like, The Building Standard Law/Seismic Retrofitting Promotion Law, was amended further enhance the structural resilience of the built environment, including strengthening structural integrity, improving the efficiency of design review process, as well as mandating seismic diagnosis of large public buildings. Since the establishment of the legal and regulatory framework for building safety in early 1900, Japan continued incremental effort to create enabling environment for owners, designers, builders and building officials to make the built environment safer together.

Cultural heritage also plays an important role in creating healthy communities, and the loss or damage of these items can scar the cohesion and identity of a community. The report Resilient Cultural Heritage: Learning from the Japanese Experience shows how the GEJE highlighted the importance of investing in the resilience of cultural properties, such as through restoration budgets and response teams, which enabled the relocation of at-risk items and restoration of properties during and after the GEJE. After the megadisaster, the volunteer organization Shiryō-Net was formed to help rescue and preserve heritage properties, and this network has now spread across Japan. [xiv] Engaging both volunteer and government organizations in heritage preservation can allow for a more wide-ranging response. Cultural properties can play a role in healing communities wrought by disasters: in Ishinomaki City, the restoration of a historic storehouse served as a symbol of reconstruction [xv] , while elsewhere repair of cultural heritage sites and the celebration of cultural festivals served a stimulant for recovery. [xvi] Cultural heritage also played a preventative role during and after the disaster by embedding the experience of prior disasters in the built environment. Stone monuments which marked the extent of historic tsunamis served as guides for some residents, who fled uphill past the stones and escaped the dangerous waters. [xvii] This suggests a potential role for cultural heritage in instructing future generations about historic hazards.

These examples of lessons from the GEJE highlight how investing in resilient infrastructure is essential, but must also be done smartly, with emphasis on planning, design, and maintenance. Focusing on both minimizing disaster impacts and putting processes in place to facilitate speedy infrastructure restoration can reduce both direct and indirect impacts of megadisasters.  Over the decade since GEJE, many examples and experiences on how to better invest in resilient infrastructure, plan for service continuity and quick response, and catalyze strategic partnerships across diverse groups are emerging from Japan.

Risk Identification, Reduction, and Preparedness

Ten years after the GEJE, a number of lessons have emerged as important in identifying, reducing, and preparing for disaster risks. Given the unprecedented nature of the GEJE, it is important to be prepared for both known and uncertain risks. Information and communication technology (ICT) can play a role in improving risk identification and making evidence-based decisions for disaster risk reduction and preparedness. Communicating these risks to communities, in a way people can take appropriate mitigation action, is a key . These processes also need to be inclusive , involving diverse stakeholders--including women, elders , and the private sector--that need to be engaged and empowered to understand, reduce, and prepare for disasters. Finally, resilience is never complete . Rather, as the adaptations made by Japan after the GEJE and many past disasters show, resilience is a continuous process that needs to be adjusted and sustained over time, especially in times before a disaster strikes.

Although DRM is central in Japan, the scale of the 2011 triple disaster dramatically exceeded expectations. After the GEJE, as Chapter 32 of Learning From Megadisasters highlights, the potential of low-probability, high-impact events led Japan to focus on both structural and nonstructural disaster risk management measures. [xviii] Mitigation and preparedness strategies can be designed to be effective for both predicted and uncertain risks. Planning for a multihazard context, rather than only individual hazards, can help countries act quickly even when the unimaginable occurs. Identifying, preparing for, and reducing disaster risks all play a role in this process.

The GEJE highlighted the important role ICT can play in both understanding risk and making evidence-based decisions for risk identification, reduction, and preparedness. As documented in the World Bank report Information and Communication Technology for Disaster Risk Management in Japan , at the time of the GEJE, Japan had implemented various ICT systems for disaster response and recovery, and the disaster tested the effectiveness of these systems. During the GEJE, Japan’s “Earthquake Early Warning System” (EEWS) issued a series of warnings. Through the detection of initial seismic waves, EEWS can provide a warning of a few seconds or minutes, allowing quick action by individuals and organizations. Japan Railways’ “Urgent Earthquake Detection and Alarm System” (UrEDAS) automatically activated emergency brakes of 27 Shinkansen train lines , successfully bringing all trains to a safe stop. After the disaster, Japan expanded emergency alert delivery systems. [xix]

The World Bank’s study on Preparedness Maps shows how seismic preparedness maps are used in Japan to communicate location specific primary and secondary hazards from earthquakes, promoting preparedness at the community and household level. Preparedness maps are regularly updated after disaster events, and since 2011 Japan has promoted risk reduction activities to prepare for the projected maximum likely tsunami [xx] .

Effective engagement of various stakeholders is also important to preparedness mapping and other disaster preparedness activities. This means engaging and empowering diverse groups including women, the elderly, children, and the private sector. Elders are a particularly important demographic in the context of the GEJE, as the report Elders Leading the Way to Resilience illustrates. Tohoku is an aging region, and two-thirds of lives lost from the GEJE were over 60 years old. Research shows that building trust and social ties can reduce disaster impacts- after GEJE, a study found that communities with high social capital lost fewer residents to the tsunami. [xxi] Following the megadisaster, elders in Ofunato formed the Ibasho Cafe, a community space for strengthening social capital among older people. The World Bank has explored the potential of the Ibasho model for other contexts , highlighting how fueling social capital and engaging elders in strengthening their community can have benefits for both normal times and improve resilience when a disaster does strike.

Conducting simulation drills regularly provide another way of engaging stakeholders in preparedness. As described in Learning from Disaster Simulation Drills in Japan , [xxii] after the 1995 GHAE the first Comprehensive Disaster Management Drill Framework was developed as a guide for the execution of a comprehensive system of disaster response drills and establishing links between various disaster management agencies. The Comprehensive Disaster Management Drill Framework is updated annually by the Central Disaster Management Council. The GEJE led to new and improved drill protocols in the impacted region and in Japan as a whole. For example, the 35th Joint Disaster simulation Drill was held in the Tokyo metropolitan region in 2015 to respond to issues identified during the GEJE, such as improving mutual support systems among residents, governments, and organizations; verifying disaster management plans; and improving disaster response capabilities of government agencies. In addition to regularly scheduled disaster simulation drills, GEJE memorial events are held in Japan annually to memorialize victims and keep disaster preparedness in the public consciousness.

Business continuity planning (BCP) is another key strategy that shows how ongoing attention to resilience is also essential for both public and private sector organizations. As Resilient Industries in Japan demonstrates, after the GEJE, BCPs helped firms reduce disaster losses and recover quickly, benefiting employees, supply chains, and the economy at large. BCP is supported by many national policies in Japan, and after the GEJE, firms that had BCPs in place had reduced impacts on their financial soundness compared to firms that did not. [xxiii] The GEJE also led to the update and refinement of BCPs across Japan. Akemi industrial park in Aichi prefecture, began business continuity planning at the scale of the industrial park three years before the GEJE. After the GEJE, the park revised their plan, expanding focus on the safety of workers. National policies in Japan promote the development of BCPs, including the 2013 Basic Act for National Resilience, which was developed after the GEJE and emphasizes resilience as a shared goal across multiple sectors. [xxiv] Japan also supports BCP development for public sector organizations including subnational governments and infrastructure operators. By 2019, all of Japan’s prefectural governments, and nearly 90% of municipal governments had developed BCPs. [xxv] The role of financial institutions in incentivizing BCPs is further addressed in the following section.

The ongoing nature of these preparedness actions highlights that resilience is a continuous process. Risk management strategies must be adapted and sustained over time, especially during times without disasters. This principle is central to Japan’s disaster resilience policies. In late 2011, based on a report documenting the GEJE from the Expert Committee on Earthquake and Tsunami Disaster Management, Japan amended the DCBA (Disaster Countermeasures Basic Act) to enhance its multi-hazard countermeasures, adding a chapter on tsunami countermeasures. [xxvi]

Disaster Risk Finance and Insurance

Disasters can have a large financial impact, not only in the areas where they strike, but also at the large scale of supply chains and national economy. For example, the GEJE led to the shutdown of nuclear power plants across Japan, resulting in a 50% decrease in energy production and causing national supply disruptions. The GEJE has illustrated the importance of disaster risk finance and insurance (DRFI) such as understanding and clarifying contingent liabilities and allocating contingency budgets, putting in place financial protection measures for critical lifeline infrastructure assets and services, and developing mechanisms for vulnerable businesses and households to quickly access financial support. DRFI mechanisms can help people, firms, and critical infrastructure avoid or minimize disruptions, continue operations, and recover quickly after a disaster.

Pre-arranged agreements, including public-private partnerships, are key strategies for the financial protection of critical infrastructure. The report Financial Protection of Critical Infrastructure Services (forthcoming) [xxvii] shows how pre-arranged agreements between the public sector and private sector for post-disaster response can facilitate rapid infrastructure recovery after disasters, reducing the direct and indirect impacts of infrastructure disruptions, including economic impacts. GEJE caused devastating impacts to the transportation network across Japan. Approximately 2,300 km of expressways were closed, representing 65 percent of expressways managed by NEXCO East Japan , resulting in major supply chain disruptions [xxviii] .  However, with the activation of pre-arranged agreements between governments and local construction companies for road clearance and recovery work, allowing damaged major motorways to be repaired within one week of the earthquake. This quick response allowed critical access for other emergency services to further relief and recovery operations.

The GEJE illustrated the importance of clearly defining post-disaster financial roles and responsibilities among public and private actors in order to restore critical infrastructure rapidly . World Bank research on Catastrophe Insurance Programs for Public Assets highlights how the Japan Railway Construction, Transport and Technology Agency  (JRTT) uses insurance to reduce the contingent liabilities of critical infrastructure to ease impacts to government budgets in the event of a megadisaster. Advance agreements between the government, infrastructure owners and operators, and insurance companies clearly outline how financial responsibilities will be shared in the event of a disaster. In the event of a megadisaster like GEJE, the government pays a large share of recovery costs, which enables the Shinkansen bullet train service to be restored more rapidly. [xxix]

The Resilient Industries in Japan   report highlights how diverse and comprehensive disaster risk financing methods are also important to promoting a resilient industry sector . After the GEJE, 90% of bankruptcies linked to the disaster were due to indirect impacts such as supply chain disruptions. This means that industries located elsewhere are also vulnerable: a study found that six years after GEJE, a greater proportion of bankruptcy declarations were located in Tokyo than Tohoku. [xxx] Further, firms without disaster risk financing in place had much higher increases in debt levels than firms with preexisting risk financing mechanisms in place. [xxxi] Disaster risk financing can play a role pre-disaster, through mechanisms such as low-interest loans, guarantees, insurance, or grants which incentivize the creation of BCPs and other mitigation and preparedness measures.  When a disaster strikes, financial mechanisms that support impacted businesses, especially small or medium enterprises and women-owned businesses, can help promote equitable recovery and help businesses survive. For financial institutions, simply keeping banks open after a major disaster can support response and recovery. After the GEJE, the Bank of Japan (BoJ) and local banks leveraged pre-arranged agreements to maintain liquidity, opening the first weekend after the disaster to help minimize economic disruptions. [xxxii] These strategies highlight the important role of finance in considering economic needs before a disaster strikes, and having systems in place to act quickly to limit both economic and infrastructure service impacts of disasters.

Looking to the Future

Ten years after the GEJE, these lessons in the realms of resilient infrastructure, risk identification, reduction and preparedness, and DRFI are significant not only for parts of the world preparing for tsunamis and other seismic hazards, but also for many of the other types of hazards faced around the globe in 2021. In Japan, many of the lessons of the GEJE are being applied to the projected Nankai Trough and Tokyo Inland earthquakes, for example through modelling risks and mapping evacuation routes, implementing scenario planning exercises and evacuation drills , or even prearranging a post-disaster reconstruction vision and plans. These resilience measures are taken not only individually but also through innovative partnerships for collaboration across regions, sectors, and organizations including public-private agreements to share resources and expertise in the event of a major disaster.

The ten-year anniversary of the GEJE finds the world in the midst of the multiple emergencies of the global COVID-19 pandemic, environmental and technological hazards, and climate change. Beyond seismic hazards, the global pandemic has highlighted, for example, the risks of supply chain disruption due to biological emergencies. Climate change is also increasing hazard exposure in Japan and around the globe. Climate change is a growing concern for its potential to contribute to hydrometeorological hazards such as flooding and hurricanes, and for its potential to play a role in secondary or cascading hazards such as fire. In the era of climate change, disasters will increasingly be ‘unprecedented’, and so GEJE offers important lessons on preparing for low-probability high-impact disasters and planning under uncertain conditions in general.

Over the last decade, the World Bank has drawn upon the GEJE megadisaster experience to learn how to better prepare for and recover from low-probability high-impact disasters. While we have identified a number of diverse strategies here, ranging from technological and structural innovations to improving the engagement of diverse stakeholders, three themes recur throughout infrastructure resilience, risk preparedness, and disaster finance. First, planning in advance for how organizations will prepare for, respond to, and recover from disasters is essential, i.e. through the creation of BCPs by both public and private organizations. Second, pre-arranged agreements amongst organizations for sharing resources, knowledge, and financing in order to mitigate, prepare, respond and recover together from disasters and other unforeseen events are highly beneficial. Third, only with continuous reflection, learning and update on what worked and what didn’t work after each disasters can develop the adaptive capacities needed to manage ever increasing and unexpected risks. Preparedness is an incremental and interactive process.

These lessons from the GEJE on the importance of BCPs and pre-arranged agreements both emphasize larger principles that can be brought to bear in the context of emergent climate and public health crises. Both involve planning for the potential of disaster before it strikes. BCPs and pre-arranged agreements are both made under blue-sky conditions, which allow frameworks to be put in place for advanced mitigation and preparedness, and rapid post-disaster response and recovery. While it is impossible to know exactly what future crises a locale will face, these processes often have benefits that make places and organizations better able to act in the face of unlikely or unpredicted events. The lessons above regarding BCPs and pre-arranged agreements also highlight that neither the government nor the private sector alone have all the tools to prepare for and respond to disasters. Rather, the GEJE shows the importance of both public and private organizations adopting BCPs, and the value of creating pre-arranged agreements among and across public and private groups. By making disaster preparedness a key consideration for all organizations, and bringing diverse stakeholders together to make plans for when a crisis strikes, these strengthened networks and planning capacities have the potential to bear benefits not only in an emergency but in the everyday operations of organizations and countries.

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Additional Resources

Program Overview

• Japan-World Bank Program on Mainstreaming Disaster Risk Management in Developing Countries

Reports and Case Studies Featuring Lessons from GEJE

• Learning from Megadisasters: Lessons from the Great East Japan Earthquake  (PDF)
• Lifelines: The Resilient Infrastructure Opportunity  (PDF)
• Resilient Water Supply and Sanitation Services: The Case of Japan  (PDF)
• Japan Case Study Report on Road Geohazard Risk Management  (PDF)
• Resilient Infrastructure PPPs  (PDF)
• Making Schools Resilient at Scale  (PDF)
• Converting Disaster Experience into a Safer Built Environment: The Case of Japan  (PDF)
• Resilient Cultural Heritage: Learning from the Japanese Experience  (PDF)
• Information and Communication Technology for Disaster Risk Management in Japan
• Resilient Industries in Japan : Lessons Learned in Japan on Enhancing Competitiveness in the Face of Disasters by Natural Hazards (PDF)
• Preparedness Maps for Community Resilience: Earthquakes. Experience from Japan  (PDF)
• Elders Leading the Way to Resilience  (PDF)
• Ibasho: Strengthening community-driven preparedness and resilience in Philippines and Nepal by leveraging Japanese expertise and experience  (PDF)
• Learning from Disaster Simulation Drills in Japan  (PDF)
• Catastrophe Insurance Programs for Public Assets  (PDF)
• PPP contract clauses unveiled: the World Bank’s 2017 Guidance on PPP Contractual Provisions
• Learning from Japan: PPPs for infrastructure resilience

Audiovisual Resources on GEJE and its Reconstruction Processes in English

• NHK documentary: 3/11-The Tsunami: The First 3 Days
• NHK: 342 Stories of Resilience and Remembrance
• Densho Road 3.11: Journey to Experience the Lessons from the Disaster - Tohoku, Japan
• Sendai City: Disaster-Resilient and Environmentally-Friendly City
• Sendai City: Eastern Coastal Area Today, 2019 Fall

[i]   Resilient Water Supply and Sanitation Services  report, p.63

[ii]   Resilient Water Supply and Sanitation Services  report, p.63

[iii]   Resilient Water Supply and Sanitation Services  report, p.8

[iv]   Resilient Water Supply and Sanitation Services  report, p.71

[v]   Resilient Water Supply and Sanitation Services  report, p.63

[vi]   Lifelines: The Resilient Infrastructure Opportunity  report, p.115

[vii] Lifelines: The Resilient Infrastructure Opportunity  report, p.133

[viii]   Japan Case Study Report on Road Geohazard Risk Management  report, p.30

[ix]   Resilient Infrastructure PPPs  report, p.8-9

[x]   Resilient Infrastructure PPPs  report, p.39-40

[xi]   Resilient Industries in Japan  report, p.153.

[xii]   Lifelines: The Resilient Infrastructure Opportunity  report, p. 132

[xiii]   Making Schools Resilient at Scale  report, p.24

[xiv]   Resilient Cultural Heritage  report, p.62

[xv]   Learning from Megadisasters  report, p.326

[xvi]   Resilient Cultural Heritage  report, p.69

[xvii]   Learning from Megadisasters  report, p.100

[xviii] Learning from Megadisasters  report, p.297.

[xix]  J-ALERT, Japan’s nationwide early warning system, had 46% implementation at GEJE, and in communities where it was implemented earthquake early warnings were successfully received. Following GEJE, GOJ invested heavily in J-ALERT adoption (JPY 14B), bearing 50% of implementation costs. In 2013 GOJ spent JPY 773M to implement J-ALERT in municipalities that could not afford the expense. In 2014 MIC heavily promoted the L-ALERT system (formerly “Public Information Commons”), achieving 100% adoption across municipalities. Since GEJE, Japan has updated the EEWS to include a hybrid method of earthquake prediction, improving the accuracy of predictions and warnings.

[xx]  Related resources: NHK, “#1 TSUNAMI BOSAI: Science that Can Save Your Life”  https://www3.nhk.or.jp/nhkworld/en/ondemand/video/3004665/  ; NHK “BOSAI: Be Prepared - Hazard Maps”  https://www3.nhk.or.jp/nhkworld/en/ondemand/video/2084002/

[xxi]  Aldrich, Daniel P., and Yasuyuki Sawada. "The physical and social determinants of mortality in the 3.11 tsunami." Social Science & Medicine 124 (2015): 66-75.

[xxii]   Learning from Disaster Simulation Drills in Japan  Report, p. 14

[xxiii]  Matsushita and Hideshima. 2014. “Influence over Financial Statement of Listed Manufacturing Companies by the GEJE, the Effect of BCP and Risk Financing.” [In Japanese.] Journal of Japan Society of Civil Engineering 70 (1): 33–43.  https://www.jstage.jst.go.jp/article/jscejsp/70/1/70_33/_pdf/-char/ja .

[xxiv]   Resilient Industries in Japan  report, p. 56

[xxv]  MIC (Ministry of Internal Affairs and Communications). 2019. “Survey Results of Business Continuity Plan Development Status in Local Governments.” [In Japanese.] Press release, MIC, Tokyo.  https://www.fdma.go.jp/pressrelease/houdou/items/011226bcphoudou.pdf .

[xxvi]   Japan Case Study Report on Road Geohazard Risk Management  report, p.17.

[xxvii]  The World Bank. 2021. “Financial Protection of Critical Infrastructure Services.” Technical Report – Contribution to 2020 APEC Finance Ministers Meeting.

[xxviii]   Resilient Industries in Japan  report, p. 119

[xxix]  Tokio Marine Holdings, Inc. 2019. “The Role of Insurance Industry to Strengthen Resilience of Infrastructure—Experience in Japan.” APEC seminar on Disaster Risk Finance.

[xxx]  TDB (Teikoku DataBank). 2018. “Trends in Bankruptcies 6 Years after the Great East Japan Earthquake.” [In Japanese.] TDB, Tokyo.  https://www.tdb.co.jp/report/watching/press/pdf/p170301.pdf .

[xxxi]  Matsushita and Hideshima. 2014. “Influence over Financial Statement of Listed Manufacturing Companies by the GEJE, the Effect of BCP and Risk Financing.” [In Japanese.] Journal of Japan Society of Civil Engineering 70 (1): 33–43.  https://www.jstage.jst.go.jp/article/jscejsp/70/1/70_33/_pdf/-char/ja .

[xxxii]   Resilient Industries in Japan  report, p. 145

Premium Content

Japan's 2011 megaquake left a scar at the bottom of the sea. Scientists finally explored it.

A towering cliff in the Japan Trench of the Pacific Ocean “is unlike anything that’s been observed by science before.”

They were enveloped in an oppressive darkness. The sun, miles above, had vanished long ago. Through tiny windows, they could see the seafloor’s sediments glimmering in the submersible’s headlights. Curious fish flitted around their vessel.

Carefully navigating the inky-black waters was a little like “driving in a car at midnight along a mountain road,” says Hayato Ueda , a geoscientist at Niigata University in Japan and one of the sub’s two occupants.

Ueda and pilot Chris May searched the darkness in their claustrophobic vessel, and eventually a lofty geologic monument emerged from the shadows: an 85-foot-tall cliff climbing into the ocean above. The exposed crest of a cataclysmic rift in Earth’s crust, exactly where Ueda predicted it would be, was part of one of the worst disasters in modern history.

This cliff is a scar of the 2011 Tōhoku earthquake that struck off Japan’s eastern shores. That year, on March 11, the magnitude 9.1 temblor deep within the Pacific Ocean unleashed a catastrophic tsunami that hit Japan, killing around 20,000 people and leaving half a million homeless.

In the past decade, scientists have studied the quake by decoding its seismic waves and scanning the depths with sonar. But getting a detailed understanding of what caused the seafloor to convulse required something that initially seemed impossible: examining part of the rupture site in person, within the Japan Trench, almost five miles below the waves.

In 2022 scientists made that ambitious mission a reality. They secured a privately owned deep-sea vessel, the DSV Limiting Factor —a submersible cleared to safely take people down to the crushing, benighted seafloor.

Plunging into the Japan Trench, the divers eventually came upon the incongruous cliff. As reported in a study the journal Communications Earth and Environment , the team determined that this cliff represented the top of a section of a chunk of crust that jumped up by over 190 feet during the 2011 earthquake.

This appears to be “the very absolute tip of the fault that generated that massive earthquake,” says Harold Tobin , director of the Pacific Northwest Seismic Network at the University of Washington, who wasn’t involved in the study. “In this one place, the tip came all the way to the surface and pushed everything up. And they tagged it, they identified it directly in the field. And that’s incredible.”

Uplift features like this have been observed on land, but this is the first time one has been glimpsed by humans in a deep-sea subduction zone trench. This “is unlike anything that’s been observed by science before,” says Christie Rowe , an earthquake geologist at McGill University who was also not involved with the study.

Decoding a disaster

The entire rupture happened over a vast section of the abyss. To have created so much uplift, the fault responsible must have moved about 330 feet near the epicenter during the quake—the largest fault movement of its kind on record. The violently uprooted cliff that resulted was part of the reason the quake generated a calamitous tsunami.  

The location of this megaquake is not so surprising. The Japan Trench is a major quake-making machine ; since 1973, it has produced nine temblors above magnitude 7. This frequent shaking occurs because the trench is a subduction zone, where the colossal Pacific tectonic plate is being forced underneath the Okhotsk microplate.

But even so, the 2011 quake proved surprisingly powerful. It struck a little westward of the Japan Trench, about 18 miles below the seafloor, causing a gargantuan rupture over a 24,000-square-mile area. Seismic waves from the event and sonar-like mapping conducted by ships before and immediately after the quake suggested the fault responsible moved as much as 200 feet—an almost unbelievable amount, and still less than the recent expedition has ascertained.

“The Tohoku earthquake was obviously a massive watershed event. It’s a game changer in lots of ways,” says Tobin. Unraveling that fault’s behavior matters not just to Japan, but to anywhere in the world that will one day experience its own subduction-zone triggered tsunami, including the U.S. Pacific Northwest , which was inundated by a major tsunami three centuries ago.

The geologic jolt in Japan seemed so extreme that scientists wanted to find the physical evidence of it at the site itself. “It’s like what a geologist would do in the field,” says Tobin. “Except this happens to be eight kilometers [five miles] below the surface of the water.” At those high-pressure depths, most submersibles—including robotic ones—would malfunction or implode .

Enter: DSV Limiting Factor . Built by the American manufacturer Triton Submarines , and funded and owned by Victor Vescovo —an investor, former naval officer, and undersea explorer—this highly durable two-person vessel can dive down to 36,000 feet, making it one of the only submersibles capable of the journey into the Japan Trench.

“It’s an incredible submarine,” says Tobin.

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Illuminating the abyss.

In September 2022, drifting on the Pacific’s midnight-blue waves aboard the support boat DSSV Pressure Drop , Ueda and his colleagues perused their geologic and bathymetric charts. “I had to decide the exact point where the submersible will land,” he says. “I carefully read topography from the map and selected the most probable point where the fault features could exist.”

A proverbial X was scored on the map. They were ready. On September 4, Ueda and pilot Chris May climbed into the two cramped seats of the DSV Limiting Factor and began their quest into the depths.

After several hours they reached the seafloor within the Japan Trench. Ueda had visited oceanic depths before, but nothing this deep and dark. Throughout their dive, video cameras mounted on the outside of the submersible recorded their traversal, including the approach to the 85-foot-high cliff that did not exist prior to the 2011 quake.

“On the way floating up to the sea surface, I had much time—I don't remember well, but perhaps two hours or more—to consider about what I saw,” says Ueda. When he rewatched the video recordings from the dive, he became confident: this was something known as fault scarp, a part of the earthquake’s surface breakthrough that causes a change in elevation.

But the cliff was only the very top of the uplift. To properly measure its scale, the submersible had risen to the feature’s peak—and as it ascended, its pressure sensors were used to calculate the height difference between the basin floor at the clifftop. It was 194 feet—the seafloor’s vertical displacement at this location during the 2011 megaquake, according to the study.

The seafloor likely rose along many parts of the gigantic rupture in the Pacific, but this section jumped up by the height of a 14-story building. “That displacement is at least part of the tsunami source,” says Tobin.

Quakes of the deep

Using this new information, the team estimates that the fault slipped by 260 to 400 feet in total at this location during the earthquake—a staggering amount, perhaps twice as much as previously suspected.

It’s a reasonable calculation, says Judith Hubbard , an earthquake scientist at Cornell University not involved with the study. But reconstructing fault geometry on land is troublesome. Doing the same work on the seafloor—doubly so. And in tectonic terms, this part of the crust is a tangled nightmare. “This is a really complicated area. There’s a huge amount of stuff going on,” Hubbard says.

Crucially, though, “they didn’t overdo their claims,” says Tobin, who considers the evidence direct, robust, and elegant. Scientists knew the 2011’s rupture’s slip was tremendous. “This case is as bulletproof as you’re ever going to get,” he says.

The 2011 quake still retains much of its mystery. This site represents just a small section of an expansive rupture, and each part behaved uniquely during the fault’s mighty jolt. “It is difficult to provide an idea or story about the entire disaster,” says Ueda.

But this study has already set a new benchmark for untangling the depths and enigmas of subduction zone megaquakes. “I didn’t know it was technically possible” to make this journey to the seafloor, says Rowe. “I’m super pumped. It’s like being an astronaut.”

This work will bring protective benefits to Japan’s shores, and it will no doubt provide scientific succor to other coastal nations. Over the past two decades, an increasing number of tsunami early-warning systems have been placed in the world’s oceans. They rely on catching the seismic waves from aquatic quakes and quickly analyzing them.

But scientists are sometimes surprised. A tsunami can be forecast, but it can be more significant than predicted, or considerably smaller—or even nonexistent. The most important question is: “How big is that scarp on the seafloor? Because that’s your tsunami trigger,” says Rowe.

Using this study and other research to tweak tsunami forecast models could bolster efforts to save lives in future geologic disasters.

“It was surely fantastic to see such important features that nobody has ever seen. I'm honored to find it,” says Ueda. But he recounts his discovery with a somber note. “It might be this cliff that took more than 20,000 lives.”

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3.9: Case Studies

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• Chris Johnson, Matthew D. Affolter, Paul Inkenbrandt, & Cam Mosher
• Salt Lake Community College via OpenGeology

Video explaining the seismic activity and hazards of the Intermountain Seismic Belt and the Wasatch Fault, a large intraplate area of seismic activity.

North American Earthquakes

Basin and Range Earthquakes —Earthquakes in the Basin and Range Province, from the Wasatch Fault (Utah) to the Sierra Nevada (California), occur primarily in normal faults created by tensional forces. The Wasatch Fault, which defines the eastern extent of the Basin and Range province, has been studied as an earthquake hazard for more than 100 years.

New Madrid Earthquakes (1811-1812) —Historical accounts of earthquakes in the New Madrid seismic zone date as far back as 1699 and earthquakes continue to be reported in modern times [ 11 ]. A sequence of large (M w >7) occurred from December 1811 to February 1812 in the New Madrid area of Missouri [ 12 ]. The earthquakes damaged houses in St. Louis, affected the stream course of the Mississippi River, and leveled the town of New Madrid. These earthquakes were the result of intraplate seismic activity [ 9 ].

Charleston (1868) —The 1868 earthquake in Charleston South Carolina was a moment magnitude 7.0, with a Mercalli intensity of X, caused significant ground motion, and killed at least 60 people. This intraplate earthquake was likely associated with ancient faults created during the breakup of Pangea. The earthquake caused significant liquefaction [ 13 ]. Scientists estimate the recurrence of destructive earthquakes in this area with an interval of approximately 1500 to 1800 years.

Great San Francisco Earthquake and Fire (1906) —On April 18, 1906, a large earthquake, with an estimated moment magnitude of 7.8 and MMI of X, occurred along the San Andreas fault near San Francisco California. There were multiple aftershocks followed by devastating fires, resulting in about 80% of the city being destroyed. Geologists G.K. Gilbert and Richard L. Humphrey, working independently, arrived the day following the earthquake and took measurements and photographs [ 14 ].

Alaska (1964) —The 1964 Alaska earthquake, moment magnitude 9.2, was one of the most powerful earthquakes ever recorded. The earthquake originated in a megathrust fault along the Aleutian subduction zone. The earthquake caused large areas of land subsidence and uplift, as well as significant mass wasting.

Video from the USGS about the 1964 Alaska earthquake.

Loma Prieta (1989) —The Loma Prieta, California, earthquake was created by movement along the San Andreas Fault. The moment magnitude 6.9 earthquake was followed by a magnitude of 5.2 aftershock. It caused 63 deaths, buckled portions of the several freeways, and collapsed part of the San Francisco-Oakland Bay Bridge.

This video shows how shaking propagated across the Bay Area during the 1989 Loma Prieta earthquake.

This video shows the destruction caused by the 1989 Loma Prieta earthquake.

Global Earthquakes

Many of history’s largest earthquakes occurred in megathrust zones, such as the Cascadia Subduction Zone (Washington and Oregon coasts) and Mt. Rainier (Washington).

Shaanxi, China (1556) —On January 23, 1556 an earthquake of an approximate moment magnitude 8 hit central China, killing approximately 830,000 people in what is considered the most deadly earthquake in history. The high death toll was attributed to the collapse of cave dwellings ( yaodong ) built in loess deposits, which are large banks of windblown, compacted sediment (see Chapter 5 ). Earthquakes in this are region are believed to have a recurrence interval of 1000 years. [ 15 ].

Lisbon, Portugal (1755) —On November 1, 1755 an earthquake with an estimated moment magnitude range of 8–9 struck Lisbon, Portugal [ 13 ], killing between 10,000 to 17,400 people [ 16 ]. The earthquake was followed by a tsunami.

Valdivia, Chile (1960) —The May 22, 1960 earthquake was the most powerful earthquake ever measured, with a moment magnitude of 9.4–9.6 and lasting an estimated 10 minutes. It triggered tsunamis that destroyed houses across the Pacific Ocean in Japan and Hawaii and caused vents to erupt on the Puyehue-Cordón Caulle (Chile).

Video describing the tsunami produced by the 1960 Chili earthquake.

Tangshan, China (1976) —Just before 4 a.m. (Beijing time) on July 28, 1976 a moment magnitude 7.8 earthquake struck Tangshan (Hebei Province), China, and killed more than 240,000 people. The high death toll is attributed to people still being asleep or at home and most buildings being made of URM.

Sumatra, Indonesia (2004) —On December 26, 2004, slippage of the Sunda megathrust fault generated a moment magnitude 9.0–9.3 earthquake off the coast of Sumatra, Indonesia [ 17 ]. This megathrust fault is created by the Australia plate subducting below the Sunda plate in the Indian Ocean [ 18 ]. The resultant tsunamis created massive waves as tall as 24 m (79 ft) when they reached the shore and killed more than an estimated 200,000 people along the Indian Ocean coastline.

Haiti (2010) —The moment magnitude 7 earthquake that occurred on January 12, 2010, was followed by many aftershocks of magnitude 4.5 or higher. More than 200,000 people are estimated to have died as a result of the earthquake. The widespread infrastructure damage and crowded conditions contributed to a cholera outbreak, which is estimated to have caused thousands more deaths.

Tōhoku, Japan (2011) —Because most Japanese buildings are designed to tolerate earthquakes, the moment magnitude 9.0 earthquake on March 11, 2011, was not as destructive as the tsunami it created. The tsunami caused more than 15,000 deaths and tens of billions of dollars in damage, including the destructive meltdown of the Fukushima nuclear power plant.

9. Hildenbrand TG, Hendricks JD (1995) Geophysical setting of the Reelfoot rift and relations between rift structures and the New Madrid seismic zone. U.S. Geological Survey, Washington; Denver, CO

11. Feldman J (2012) When the Mississippi Ran Backwards: Empire, Intrigue, Murder, and the New Madrid Earthquakes of 1811 and 1812. Free Press

12. Fuller ML (1912) The New Madrid earthquake. Central United States Earthquake Consortium, Washington, D.C.

13. Talwani P, Cox J (1985) Paleoseismic evidence for recurrence of Earthquakes near Charleston, South Carolina. Science 229:379–381

14. Gilbert GK, Holmes JA, Humphrey RL, et al (1907) The San Francisco earthquake and fire of April 18, 1906 and their effects on structures and structural materials. U.S. Geological Survey, Washington, D.C.

15. Boer JZ de, Sanders DT (2007) Earthquakes in human history: The far-reaching effects of seismic disruptions. Princeton University Press, Princeton

16. Aguirre B.E. (2012) Better disaster statistics: The Lisbon earthquake. J Interdiscip Hist 43:27–42

17. Rossetto T, Peiris N, Pomonis A, et al (2007) The Indian Ocean tsunami of December 26, 2004: observations in Sri Lanka and Thailand. Nat Hazards 42:105–124

18. Satake K, Atwater BF (2007) Long-Term Perspectives on Giant Earthquakes and Tsunamis at Subduction Zones. Annual Review of Earth and Planetary Sciences 35:349–374. https://doi.org/10.1146/annurev.earth.35.031306.140302

Earthquake case studies

Earthquake case studies Below are powerpoint presentations discussing the primary and secondary effects and immediate and long-term responses for both the Kobe, Japan and Kashmir, Pakistan earthquakes.

Effects of the Italian earthquake – http://www.bbc.co.uk/learningzone/clips/the-italian-earthquake-the-aftermath/6997.html Responses to Italian earthquake – http://www.bbc.co.uk/learningzone/clips/the-italian-earthquake-the-emergency-response/6998.html The Kobe earthquake – http://www.bbc.co.uk/learningzone/clips/the-kobe-earthquake/3070.html General effects & responses & Kobe (Rich) & Kashmir (Poor)

O Ltb Eartqaukes Cs from donotreply16 Kobe earthquake (Rich country)

Koberevision from cheergalsal Haiti 2010 – Poor country Picture Facts On 12th January, an earthquake measuring 7.0 on the Richter scale struck close to Haiti’s capital Port-au-Prince The earthquake occurred at a destructive plate margin between the Caribbean and North American Plates, along a major fault line. The earthquakes focus was 13km underground, and the epicentre was just 25km from Port-au-Prince Haiti has suffered a large number of serious aftershocks after the main earthquake

Primary effects About 220,000 people were killed and 300,000 injured The main port was badly damaged, along with many roads that were blocked by fallen buildings and smashed vehicles Eight hospitals or health centres in Port-au-Prince collapsed or were badly damaged. Many government buildings were also destroyed About 100,000 houses were destroyed and 200,000 damaged in Port-au-Prince and the surrounding area. Around 1.3 million Haitians were displaced (left homeless)

Secondary effects Over 2 million Habitats were left without food and water. Looting became a serious problem The destruction of many government buildings hindered the government’s efforts to control Haiti, and the police force collapsed The damage to the port and main roads meant that critical aid supplies for immediate help and longer-term reconstruction were prevented from arriving or being distributed effectively Displaced people moved into tents and temporary shelters, and there were concerns about outbreaks of disease. By November 2010, there were outbreaks of Cholera There were frequent power cuts The many dead bodies in the streets, and under the rubble, created a health hazard in the heat. So many had to be buried in mass graves

Short-term responses The main port and roads were badly damaged, crucial aid (such as medical supplies and food) was slow to arrive and be distributed. The airport couldn’t handle the number of planes trying to fly in and unload aid American engineers and diving teams were used to clear the worst debris and get the port working again, so that waiting ships could unload aid The USA sent ships, helicopters, 10,000 troops, search and rescue teams and $100 million in aid The UN sent troops and police and set up a Food Aid Cluster to feed 2 million people Bottled water and water purification tablets were supplied to survivors Field hospitals were set up and helicopters flew wounded people to nearby countries The Haitian government moved 235,000 people from Port-au-Prince to less damaged cities

Long-term responses Haiti is dependent on overseas aid to help it recover New homes would need to be built to a higher standard, costing billions of dollars Large-scale investment would be needed to bring Haiti’s road, electricity, water and telephone systems up to standard, and to rebuild the port Sichuan, China 2008 – Poor country case study Picture On 12th May at 14:28pm, the pressure resulting from the Indian Plate colliding with the Eurasian Plate was released along the Longmenshan fault line that runs beneath. This led to an earthquake measuring 7.9 on the Richter scale with tremors lasting 120 seconds.

Primary effects · 69,000 people were killed · 18,000 missing · 374,000 were injured · between 5 -11 million people were missing · 80% of buildings collapsed in rural areas such as Beichuan county due to poorer building standards · 5 million buildings collapsed

Secondary effects · Communication were brought to a halt – neither land nor mobile phones worked in Wenchuan · Roads were blocked and damaged and some landslides blocked rivers which led to flooding · Fires were caused as gas pipes burst · Freshwater supplies were contaminated by dead bodies

Immediate responses · 20 helicopters were assigned to rescue and relief effects immediately after the disaster · Troops parachuted in or hiked to reach survivors · Rescuing survivors trapped in collapsed buildings was a priority · Survivors needed food, water and tents to shelter people from the spring rains. 3.3 million new tents were ordered.

Long-term responses · Aid donations specifically money – over £100 million were raised by the Red Cross · One million temporary small were built to house the homeless · The Chinese government pledged a $10 million rebuilding funds and banks wrote off debts by survivors who did not have insurance

The Parkfield, California, Earthquake Experiment

September 28, 2004— m 6.0 earthquake captured.

The Parkfield Experiment is a comprehensive, long-term earthquake research project on the San Andreas fault. Led by the USGS and the State of California, the experiment's purpose is to better understand the physics of earthquakes - what actually happens on the fault and in the surrounding region before, during and after an earthquake. Ultimately, scientists hope to better understand the earthquake process and, if possible, to provide a scientific basis for earthquake prediction. Since its inception in 1985, the experiment has involved more than 100 researchers at the USGS and collaborating universities and government laboratories. Their coordinated efforts have led to a dense network of instruments poised to "capture" the anticipated earthquake and reveal the earthquake process in unprecedented detail.

Moderate-size earthquakes of about magnitude 6 have occurred on the Parkfield section of the San Andreas fault at fairly regular intervals - in 1857, 1881, 1901, 1922, 1934, and 1966. The first, in 1857, was a foreshock to the great Fort Tejon earthquake which ruptured the fault from Parkfield to the southeast for over 180 miles. Available data suggest that all six moderate-sized Parkfield earthquakes may have been "characteristic" in the sense that they all ruptured the same area on the fault. If such characteristic ruptures occur regularly, then the next quake would have been due before 1993.

These pages describe the scientific background for the experiment, including the tectonic setting at Parkfield, the historical earthquake activity on this section of the San Andreas fault, the monitoring and data collecting activities currently being carried out, and plans for future research. Data are available to view in real-time and download.

Scientific Advances

While the greatest scientific payoff is expected when the earthquake occurs, our understanding of the earthquake process has already been advanced through research results from Parkfield. Some of the highlights are described.

Real-time data from instrumentation networks running at Parkfield are available for viewing and downloading.

Parkfield Earthquake Shake Table Exhibit

The Art-Science of Earthquakes by D.V. Rogers November 23, 2009 ( video )

The exhibit was a geologically interactive, seismic machine earthwork temporarily installed in Parkfield in 2008. Rogers presented the history, conceptual premise, documentation of the work, and also put forward the idea of how early 21st century cultural practice could be used to encourage earthquake awareness and preparedness.

Pictures and interactive, 360-degree panorama .

Lessons From the Best-Recorded Quake in History

USGS Public Lecture by Andy Michael October 26, 2006 ( video )

New data from the 2004 Parkfield earthquake provide important lessons about earthquake processes, prediction, and the hazards assessments that underlie building codes and mitigation policies.

Research Scientist: John Langbein , Earthquake Science Center.

Understanding the Impact of Minor Earthquakes A Case Study of the 2024 Santa Rosa, California, Event

O n April 2nd, 2024, the city of Santa Rosa, California, experienced a minor earthquake with a magnitude of 3.2. While the seismic event did not cause significant damage, it serves as a reminder of the ongoing seismic activity in California and the potential risks associated with living in earthquake-prone regions. This article examines the impact of the 2024 Santa Rosa earthquake, delving into the science behind minor earthquakes, the response of local communities, and the lessons learned from the event.

Understanding Minor Earthquakes

Minor earthquakes, such as the one experienced in Santa Rosa, are relatively common occurrences in seismically active regions like California. These events are typically characterized by low magnitudes and shallow depths, resulting in minimal damage to infrastructure and property. However, despite their relatively benign nature, minor earthquakes serve as important reminders of the underlying tectonic forces at work beneath the Earth’s surface.

The 2024 Santa Rosa Earthquake: Magnitude and Impact

The earthquake that struck Santa Rosa on April 2nd, 2024, had a magnitude of 3.2 and occurred at a very shallow depth of 6 miles beneath the epicenter. While the quake was not strong enough to cause significant damage, it was widely felt in the area surrounding Santa Rosa. Residents reported light vibrations and shaking, but no reports of injuries or structural damage were recorded.

Response and Preparedness

In the aftermath of the earthquake, local authorities and emergency responders in Santa Rosa activated response protocols to ensure the safety and well-being of residents. While the event did not require extensive search and rescue efforts, it served as a valuable opportunity for communities to review and reinforce their preparedness measures. Public education campaigns, seismic retrofitting initiatives, and community drills play a crucial role in enhancing resilience and mitigating the impact of future seismic events.

Lessons Learned and Future Mitigation Efforts

The 2024 Santa Rosa earthquake provides valuable insights into the importance of ongoing monitoring, preparedness, and response efforts in earthquake-prone regions. While minor earthquakes may not cause widespread damage, they serve as important reminders of the need for vigilance and resilience. Moving forward, investments in early warning systems, infrastructure resilience, and public awareness campaigns will be crucial in minimizing the impact of future seismic events on communities in California and beyond.

The 2024 Santa Rosa earthquake serves as a testament to the dynamic nature of Earth’s geology and the ongoing threat of seismic activity in California. While minor earthquakes may not garner the same level of attention as major events, they nonetheless highlight the importance of preparedness, response, and mitigation efforts in earthquake-prone regions. By understanding the impact of minor earthquakes and taking proactive measures to enhance resilience, communities can better prepare for the inevitable challenges posed by seismic events in the future.

Case Study: Predicting the Next Big Earthquake

Recent earthquake activity.

USGS Recent Worldwide Earthquake Activity To explore individual earthquakes in more depth, click on the UTC Date-Time field. Show me how Hide Details for accessing USGS Recent Worldwide Earthquake Activity Scroll the list to look over earthquakes that have occurred in the last seven days. To explore individual earthquakes in more depth, follow the COMMENTS links. Scroll to the bottom of the list to view recent Earthquakes plotted on a world map. What is the magnitude of the most recent recorded earthquake? How many earthquakes were recorded for the last seven days? Of those earthquakes, how many were of a magnitude 7.0 or greater? IRIS Seismic Monitor Click on the map to zoom to specific regions. Click on individual earthquakes to see lists of others nearby. Show me how Hide Details for accessing the IRIS Seismic Monitor Click on the map to zoom to specific regions. Click on individual earthquakes to see lists of others nearby. Where are earthquakes concentrated?

Where does Earth Quake?

Predicting the Next Big One!

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What causes earthquakes in the Northeast, like the magnitude 4.8 that shook New Jersey? A geoscientist explains

Professor of Geosciences, Buffalo State, The State University of New York

Disclosure statement

Gary Solar does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

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It’s rare to feel earthquakes in the U.S. Northeast, so the magnitude 4.8 earthquake in New Jersey that shook buildings in New York City and was felt from Maryland to Boston on April 5, 2024, drew a lot of questions. It was one of the strongest earthquakes on record in New Jersey, though there were few reports of damage. A smaller, magnitude 3.8 earthquake and several other smaller aftershocks rattled the region a few hours later. We asked geoscientist Gary Solar to explain what causes earthquakes in this region.

What causes earthquakes like this in the US Northeast?

There are many ancient faults in that part of New Jersey that extend through Philadelphia and along the Appalachians, and the other direction, past New York City and into western New England.

These are fractures where gravity can cause the rock on either side to slip, causing the ground to shake. There is no active tectonic plate motion in the area today, but there was about 250 million to 300 million years ago .

The earthquake activity in New Jersey on April 5 is similar to the 3.8 magnitude earthquake that we experienced in 2023 in Buffalo, New York. In both cases, the shaking was from gravitational slip on those ancient structures.

In short, rocks slip a little on steep, preexisting fractures. That’s what happened in New Jersey, assuming there was no man-made trigger.

How dangerous is a 4.8 magnitude earthquake?

Magnitude 4.8 is pretty large, especially for the Northeast, but it’s likely to have minor effects compared with the much larger ones that cause major damage and loss of life.

The scale used to measure earthquakes is logarithmic, so each integer is a factor of 10. That means a magnitude 6 earthquake is 10 times larger than a magnitude 5 earthquake. The bigger ones, like the magnitude 7.4 earthquake in Tawian a few days earlier, are associated with active plate margins , where two tectonic plates meet.

The vulnerability of buildings to a magnitude 4.8 earthquake would depend on the construction. The building codes in places like California are very strict because California has a major plate boundary fault system – the San Andreas system . New Jersey does not, and correspondingly, building codes don’t account for large earthquakes as a result.

How rare are earthquakes in the Northeast, and will New Jersey see more in the same location?

Earthquakes are actually pretty common in the Northeast, but they’re usually so small that few people feel them. The vast majority are magnitude 2.5 or less.

The rare large ones like this are generally not predictable. However, there will likely not be other large earthquakes of similar size in that area for a long time. Once the slip happens in a region like this, the gravitational problem on that ancient fault is typically solved and the system is more stable.

That isn’t the case for active plate margins, like in Turkey , which has had devastating earthquakes in recent years, or rimming the Pacific Ocean . In those areas, tectonic stresses constantly build up as the plates slowly move, and earthquakes are from a failure to stick.

This article, originally published April 5, 2024, has been updated with several smaller aftershocks felt in the region.

• Earthquakes
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What causes earthquakes? The science behind why seismic events like today's New Jersey shakeup happen

By Kerry Breen

Updated on: April 5, 2024 / 6:40 PM EDT / CBS News

A strong earthquake centered outside of New York City  rattled much of the East Coast on Friday morning, followed by several aftershocks.

The earthquake — which the U.S. Geological Survey said was magnitude 4.8  — occurred at about 10:20 a.m. The quake was centered near Whitehouse Station, New Jersey, which is about 40 miles west of New York City, according to the USGS. So far, there have been no reports of injuries or major damage, but many who felt the quake took to social media to describe the unusual experience. 

Here's what to know about earthquake activity on the East Coast, and what can cause such temblors. 

What causes earthquakes?

Earthquakes occur when the plates that make up the Earth's crust move around. These plates, called tectonic plates, can push against each other.

Earthquakes are most common along fault lines, which are fractures that allow the plates to move. 

Earthquakes occur when two plates suddenly slip past each other, setting off seismic waves that cause the planet's surface to shake, according to the USGS.  

What is an earthquake, scientifically speaking?

An earthquake is what happens when the seismic energy from plates slipping past each other rattles the planet's surface. 

Those seismic waves are like ripples on a pond, the USGS said. 

The earthquake will be strongest at its epicenter, the point on the surface directly above where the quake started, and the effects will be diminished as they spread further. In Friday's earthquake, the epicenter was in northern New Jersey, but its effects were felt in New York City,  Philadelphia  and as far away as  Baltimore . 

What caused the East Coast earthquake? 

It's not clear what fault line Friday's earthquake originated on.  

There is a major fault line in New Jersey called the Ramapo Fault, which stems from the Appalachian Mountains, CBS New York reported. There are also at least five smaller fault lines under the island of Manhattan. 

On the West Coast, it can be possible to determine exactly which fault line a quake originated along, the USGS said, because of how well-studied some plate boundaries like the San Andreas fault are. But on the East Coast, the nearest plate boundaries are in the center of the Atlantic Ocean, making it hard to study the area. 

"The urban corridor (between New York City and Wilmington, Delaware) is laced with known faults but numerous smaller or deeply buried faults remain undetected. Even the known faults are poorly located at earthquake depths," the USGS says on its website. "Accordingly, few, if any, earthquakes in the urban corridor can be linked to named faults." 

Are earthquakes common on the East Coast? 

Earthquakes are rarer on the East Coast compared to the West Coast, but they do happen . Moderately damaging earthquakes strike between New York and Wilmington, Delaware, about twice a century, the USGS said, and smaller earthquakes are felt in the region roughly every two to three years. 

While East Coast earthquakes are less common than their counterparts on the West Coast, they tend to be felt over a wide area, as evidenced by Friday's quake, the USGS said. A 4.0 magnitude quake could be felt more than 60 miles from its epicenter, the agency said. 

Will earthquakes happen more frequently?

In January, the USGS estimated that nearly 75% of the U.S. could experience a damaging earthquake in the next century. The prediction is based on research done by dozens of scientists and engineers using seismic studies, historical geological data and new information to identify nearly 500 additional fault lines that could produce damaging quakes. 

It is possible that the central and northeastern Atlantic Coastal region could see more temblors, researchers said. Earthquakes are also likely in California and Alaska, which are historically seismically active regions.  

• New York City
• Earthquakes

Kerry Breen is a reporter and news editor at CBSNews.com. A graduate of New York University's Arthur L. Carter School of Journalism, she previously worked at NBC News' TODAY Digital. She covers current events, breaking news and issues including substance use.

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• Published: 14 December 2023

Using mobile phone data to map evacuation and displacement: a case study of the central Italy earthquake

• Francesca Giardini 1   na1 ,
• Natalia Selini Hadjidimitriou 3   na1 ,
• Marco Mamei 3 , 4   na1 ,
• Giordano Bastardi 3 ,
• Nico Codeluppi 3 &
• Francesca Pancotto 2   na1  

Scientific Reports volume  13 , Article number:  22228 ( 2023 ) Cite this article

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• Engineering
• Natural hazards

Population displacement is one of the most common consequences of disasters, and it can profoundly affect communities and territories. However, gaining an accurate measure of the size of displacement in the days and weeks following a major disaster can be extremely difficult. This study uses aggregated Call Detail Records as an inexpensive and efficient technique to measure post-disaster displacement in four Italian regions affected by repeated earthquakes in 2016–2017. By comparing post-disaster mobile phone count with a forecast computed before the earthquake hit, we can compute an index of change in the presence of mobile phones (MPE). This measure, obtained thanks to advanced analytical techniques, provides a reliable indication of the effect of the earthquake in terms of immediate and medium-term displacement. We test this measure against census data and in combination with other datasets. Looking into available data on economic activities and requests for financial support to rebuild damaged buildings, we can explain MPE and identify significant factors affecting population displacement. It is possible to apply this innovative methodology to other disaster scenarios and use it by policymakers who want to understand the determinants of population displacement.

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Introduction

The United Nations Office for Disaster Risk Reduction (UNDRR) defines a disaster as a “serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts” (UNDRR, n.d.). Quarantelli 1 (p. 682) explores the defining features of disasters, comprising the time frame (disasters are sudden-onset occasions), their disruptiveness towards collective routines, the adoption of unplanned courses of action to adjust to the disruption, and the danger posed to valued social objects. Quarantelli 1 (p. 682) also emphasized that disasters represent vulnerability, reflecting “weaknesses in social structures or social systems” 2 (p. 345). The conception of disasters as a weakness of social systems is firmly grounded in the social sciences tradition 3 . Since the relationships in the social system can cause vulnerability, the definition of disasters originates from the notion of social changes. Similarly, Alexander 4 (p. 29) stresses the impossibility of defining ”disasters with fixed events but by social constructs, and these are liable to change”. Other authors like 5 support this perspective and refers to disasters as social events. Disasters result from a combination of hazards with vulnerability and exposure of people and assets. Vulnerability refers to the conditions which increase the susceptibility of individuals, communities and systems to the hazards’ impact. According to Wisner et al. 6 (p. 11), vulnerability is “...the characteristics of a person or group and their situation that influence their capacity to anticipate, cope with, resist, and recover from the impact of a natural hazard”. Vulnerabilities vary by hazard type as they are contingent on several circumstances and unevenly distributed across individuals, households, communities, and locations 7 , 8 .

Displacement and vulnerability are closely related. Displacement forces villages, towns, or regions to lose inhabitants who need to find alternative accommodations somewhere else 9 . There is extensive literature on the risks to well-being generated by forced displacement (e.g. 10 , 11 , 12 , 13 ), but less is known about the increasing vulnerability of displacement that can unfold over different periods, from a few days to decades, and the displaced population can move very close to their original location, but also very far away. There is no univocal concept of displacement because it usually depends on the kind of crisis, disaster or conflict that triggered it, and the term itself is not uncontroversial 14 . Here we will use the definition developed by the UNDRR: Disaster displacement is one of the most common and immediate impacts of disasters. It refers to situations where people are obliged to leave their homes or places of habitual residence as a result of a disaster or in order to avoid the impact of an immediate and foreseeable natural hazard, including the adverse impacts of climate change, or a disaster triggered by human-made factors, such as large-scale industrial accidents. Displacement triggered by conflict is not considered disaster displacement 15 . If the citizens do not quickly return to their original places of residence and are involved in the reconstruction of the material and social environment, the loss of human, economic and social capital 16 can lead to the abandonment of the area. When individuals, firms and service providers must relocate somewhere else, there are net losses in place of community resilience, with parallel increases in vulnerabilities. Infrastructure resilience, i.e., the extent to which there is a restoration of minimum services and functions in a relatively short amount of time, thus allowing people to return to their place of origin 17 , is a first step towards the restoration of communities. The importance of involving citizens and communities in the reconstruction process after the initial evacuation is clearly visible in the case of the L’Aquila earthquake. Imperiale and Vanclay 18 show how the exclusion of local communities and the top-down planning approach chosen for the reconstruction led to more than 10.000 people still in temporary accommodations 10 years after the earthquake, with many citizens leaving the city and never going back. When population leaves the affected area in a permanent way, this contributes to a decrease in the capacity to cope with additional shocks 16 , 19 . A vulnerable community can suffer from changes in population in response to direct and indirect impacts, and these variations are hardly stable over time. Usually, variations in population size in a given area are different between the aftermath of the disaster (short-term changes), some months later (mid-term changes), and in the long term, when the reconstruction phase has started because the emergency is over (in this study we are not considering permanent relocation, which might be necessary in some cases). This change is seldom visible in official census data because individuals can autonomously find accommodations without informing the authorities when they flee the disaster area. In other cases, the devastation is so profound that the census records are difficult to acquire and update. In this situation, it is fundamental to have an accurate picture of the number of displaced people and where they have relocated. If a system exhibits the capacity to ‘return to equilibrium after a displacement’ 20 , then that system is considered resilient. Displacement is relevant in two of the four priorities outlined in the Sendai Framework for Disaster Risk Reduction 2015-2030 (Sendai Framework), the first agreement of the post-2015 development agenda that provides Member States with concrete actions to protect from the risk of disaster. This study responds to objective (h) in Priority 4 (Enhancing disaster preparedness for effective response and to “Build Back Better” in recovery, rehabilitation and reconstruction), which advocates disaster preparedness and the establishment of rapid and effective responses to disasters and related displacement.

More specifically, this study combines engineering methods to estimate post-disaster displacement using mobile phone data, with a social sciences perspectives on economic factors and community resilience. There are two reasons for this approach.

While there exist alternative approaches to estimate displacement, i.e., using aerial images 21 , night-light satellite images 22 , social network, crowd-sourced and damage assessment data 23 , 24 , 25 , novel data sources can integrate existing information and provide further insights. Mobile phone data (often called Call Detail Records - CDRs) is a natural candidate in this regard as it combines ready available data with an almost complete population coverage.

Information about CDRs (short-term) can then be integrated with other elements, for instance information about the economic structure of the area or about the reconstruction process (mid-term). Understanding how factors other than the sheer magnitude of the disaster might be contributing to displacement is essential for the design of policies that can effectively counteract population displacement and can improve community resilience.

Thanks to this novel combination of data and methods, we aim to answer the following two research questions:

How to quantify population displacement from fully aggregated mobile-phone data? Most of the approaches reported in the literature 26 , 27 , 28 , 29 , 30 , 31 derive displacement information directly from the data (i.e., looking at where each mobile phone moves after the disaster). Using fully-aggregated data, this kind of approach is unusable as privacy regulations do not allow to track phones but only to measure their presence in an area (see Section “ Methods ”). Therefore, in this study we had to develop a novel approach to quantify displacements. While we applied our method to a specific case study - the earthquake that struck Central Italy in 2016- the approach discussed in this study is more general, and can be implemented it in other emergencies once data are available—see Section “ Methods ”.

What is the relationship between population displacement, economic activities and infrastructural damages in the selected case study? After measuring displacement resulting from the Central Italy earthquake, it is interesting to understand the main factors impacting it. Proximity to the earthquake epicentre and the size of damage suffered naturally have a high impact, but there are other important factors. In particular, we focus on the geographical and economic characteristics of the affected areas. Because disasters affect various economic activities in a heterogeneous way, population changes are heterogeneous across municipalities. In our case study, manufacturing or logistics, for instance, require physical infrastructures that are easily damaged by an earthquake. Moreover, these industries present more complex inter-dependencies among production plants and supply chains, all features heavily disrupted by a disaster. Therefore, municipalities with an high number of such companies are likely to suffer more displacement because of the destruction.

In this study, we focus on CDRs (Call Detail Records), which offer a quick way to obtain data about post-disaster mobility. CDRs are automatically and routinely collected by telecom operators and contain approximate information about the location of every mobile phone connected to their network 32 , 33 , 34 , 35 , 36 . It is relevant to notice that while the use of mobile phone data at the individual -level (i.e., tracking individual mobile phones) is very much constrained due to privacy reasons, the use of aggregated and fully anonymized mobile phone data is easily accessible. Most telecommunication operators have commercial offers to provide aggregated and anonymous data about people’s presence and mobility. The main idea is that mobile phones precisely localize a substantial fraction of the population with minimal cost and intervention. Unlike census data or data collected by municipalities at selected time points, CDRs provide a continuous estimate of the situation, allowing researchers to identify changes in relocation patterns and displacement as they happen.

In the literature there are many examples on the use of CDR data in the context of disaster risk reduction. Counter-intuitively, the vast majority of the works presented in the literature focus on individual-level data (that is hard to acquire), while they often disregard aggregated-level data.

Many scholars have focused on describing mobility patterns to understand post-disaster population displacement at different periods in time. In 28 , mobile phone data allowed for predicting people’s mobility during a disaster. The authors analysed data about mobile phones 42 days before and 341 days after the Haiti earthquake. Their results show that the earthquake caused an increase in population movement, an increase in daily distances in the affected areas and more heterogeneity in terms of average travel distance. They also found that people’s movement was more predictable after the disaster because of the high correlation with the places where people had strong social relationships. Similarly, in another work on the Haiti earthquake 29 , authors found that 20% of the population emigrate from Port-au-Prince during the first 19 days after the earthquake. To validate the results, the authors compared with the estimates of the National Civil Protection Agency based on the number of ships and buses leaving the area and the survey performed by the United Nations about migration after the earthquake. Thanks to mobile phone data, the authors found the regions where most of the population migrated, thus offering support to policymakers for planning and relief efforts. Similar analysis performed in the context of the Nepal earthquake in 2015 37 , the landslide in Japan in 2014 26 , or the Mozambique cyclone in 2019 38 show the evolution of population mobility patterns after the disasters and the return patterns to affected areas in the following days and weeks. In a recent study on mobile phone data published by 31 , the authors suggest the importance of inter-city connectivity in post-disaster recovery. Using mobile phone call data records, the authors show that the cities with high inflows and outflows of people before the disaster were more resilient and recovered faster.

Some recent works started comparative analysis among multiple disasters analyzing pre- and post-disaster individual mobile phone trajectories to test the generality of this approach 39 , 40 . In 27 , mobile phone data enabled the study of the population recovery patterns after extreme events. The authors found very similar behaviours in five different locations across world. They explained the differences using income, population, housing damage rate and connectivity with other cities. When they analysed the recovery time distribution, they found that most people returned to their homes within two weeks. Furthermore, they tried to explain the displacement rate based on socioeconomic variables, infrastructure recovery and accessibility. The results showed that, at different times, the infrastructure recovery became more relevant compared to the house’s damage rates.

The works in 41 , 42 analyze aggregated mobile phone data in the context of the earthquake that shook Ecuador on April 2016. The analyzed data contains information about the home area of the monitored phones, which allows estimating displacements more easily. Similarly, the work in 43 is one of the few dealing with aggregated mobile phone data. They identify anomalies in the data collected during multiple events affecting some US states in 2018 (e.g., hurricanes Florence and Michael).

Mobile phone data can indirectly measure the time needed for a given area to recover. The Resilience to Emergencies and Disaster Index proposed by 44 assesses the recovery period of neighbourhoods impacted by a storm to prioritize limited resource use. The authors considered mobile phone data as a proxy of neighbourhood activity, social infrastructure, community connectivity, physical infrastructure, economic strength and environmental conditions as distinct variables and computed the indicator as the normalized weighted sum of these variables. According to 45 , mobile phone data alone does not explain the motivation why people move and relocate in a certain way after extreme events. Thus, it is necessary to perform data fusion on different datasets or to combine big data with smaller datasets, such as surveys. In this regard 30 , analysed migration patterns based on mobile phone data and the national survey that included questions on temporary and seasonal migration. Furthermore, the authors underline the importance of considering short-term and long-term displaced people in the definition of migration. Their results evidence that temporary migration can be detected using mobile phone data.

Our research contributes to this consolidated body of work by providing new insights into data and methods. More specifically:

Unlike most papers discussed so far, we do not use data that can be associated with a single mobile phone because this would posit several limitations in terms of privacy and would hinder the actual applicability and reproducibility of our method and analysis. Our approach allows us to overcome this limitation by using anonymous and aggregated Call Detail Records (CDRs) that telecom companies routinely collect and sell for business/commercial purposes. For this reason, we acquired data from a wide area comprising four regions, 663 municipalities and spanning three years – such a large extent is uncommon in the literature. Such aggregated data required us to develop a novel method to measure population displacement based on non-autoregressive time-series forecasting. Although such a forecasting approach is not novel per-se, the application to mobile phone data is new (see Section “ Methods ”).

In this study, we combine CDRs data with data about economic activities, companies’ information and government funding for reconstruction. Using a large dataset on commercial and industrial companies of any size and sector, we introduce in the model very detailed and relevant information on the kind of economic activities present in a given municipality. Information about the industrial composition at the municipality level is valuable to embed the effects of the disaster into the existing economic setting. As an additional step, we link CDRs with data about the funding requests for the reconstruction of residential, commercial and industrial buildings to understand damages and mid-term trends in people’s returning home. These datasets offer a multifaceted picture of post-disaster damage and displacement and suggest relevant directions for policymakers.

The case study

What is known as the ‘Central Italy earthquake’ is a sequence of different shocks happening over a period of five months in 2016 46 . The first earthquake hit at 3.36 a.m. on August 24, 2016, with a 5.9 magnitude. The affected area, administratively divided under four Italian regions, consists of Abruzzo, Lazio, Marche and Umbria. During three days, above 2000 aftershocks were recorded following the main seismic event, extending along 25 kilometres through Amatrice and Norcia. The largest aftershock occurred near Norcia (5.3 Mw) at 04:33 on August 24, 2016. The Italian Civil Protection reported that 299 people died. On September 5, 2016, the number of people needing assistance was 4807. On October 26 and 30, new violent shocks rock the same area, especially between Umbria and Marche, already markedly affected by the August 24 earthquake. Specifically, the October 26 event consisted of two 5.4 and 5.9 magnitude earthquakes. On October 30, a new shake caused the collapse of buildings 47 . On January 18, four earthquakes of magnitude higher than 5 hit the area, and Lazio and Abruzzo’s regions were the most affected. In particular, a 5.1 event was registered at 10:25, followed by other earthquakes: the second at 11:14 (5.5 Richter magnitude), the third at 11:25 (5.4 Richter) and the fourth at 14:33 (5 Richter). The affected territory is mountainous, with more than 70 per cent of the surface located at an altitude higher than 900 metres. This mountainous configuration means that there is an uneven distribution of urban areas: the majority of the inhabitants live in several small centres surrounded by widespread rural zones within a mountainous landscape, most of which are isolated and accessible only by minor roads 48 . According to Decree-Law 17 October 2016, n. 189, 140 municipalities that fall into the “seismic crater” are entitled to government funding for private and public reconstruction. Overall, this sequence of earthquakes heavily affected the whole area, although the 140 municipalities close to the earthquakes’ epicentres—referred to as the seismic crater, were the most damaged. According to governmental reports 49 , the total population living there before August 2016 was 581.885. Concerning the estimate of the costs of the Central Italy earthquakes, the Italian Government provided them in June 2022. The estimated request for funding at the time was of 22.695 units, corresponding to a value of 7.669.353.627 euros. The expected amount of further requests is 27.350 for 11.794.161.941 euros, reaching 50.045 units in total that need reconstruction 49 .

Our analytical strategy consists of the following steps:

Measuring the impact of the earthquake in the number of mobile phones present in the area. The objective is to evaluate the relationship between this variation and the people’s presence in the area in the short-term. We propose the Mean Prediction Error (MPE) to compute the displacement in population, and assess how strongly this correlates with the observed changes in population according to census data.

Analyzing the relationship between MPE and other important variables, namely reported damages, distance from the epicentre, structural characteristics, and economic activities, to explain variations in population displacement in the mid-term.

Monitoring population via CDRs

In the first set of experiments, we use mobile phone data to quantify people’s displacement after the earthquake. Figure 1 A compares the number of residents in the municipalities of the area under study with the corresponding data from mobile phones (computed as the average number of phones at night – see Section “ Methods ”). There is a clear linear relationship among the two measures ( \(\beta = 1.17\;(0.01)\) , \(R^2=0.95\) ), confirming the representativeness of the mobile data. Figure 1 B compares the percentage change in residents’ population as measured by official statistics between 2017 and 2016 and the change in residents’ population as measured by mobile phone data. In particular, we calculate the number of resident changes as measured by official statistics (OCHG—Official CHanGe) as follows:

Similarly, we calculate the change in residents’ population measured by mobile phone data (PCHG—Phone CHanGe) as:

There is almost no relationship between the two measures. Looking at the small range of variation in official statistics and considering the destruction of some municipalities by the earthquake, we can safely assume that official statistics do not provide an accurate population dynamics estimate. In these extreme situations, people do not promptly communicate their change in residency. However, in the context of earthquakes and natural hazards, a timely representation of people displacement is critical to organize relief activities and prioritizing resources. Therefore our results show the importance of using CDRs per-se in a more precise manner for monitoring population dynamics.

Each dot represents a municipality. Blue dots are outside the earthquake crater area, and orange dots are inside the crater area. ( A ) People count from the Italian census (ISTAT) and mobile phone data. ( B ) Population change from the Italian census (ISTAT) and mobile phone data, the size of dots represent the municipality population.

To understand the impact of the earthquake in the area, we trained a non-autoregressive forecasting model to predict population dynamics as measured by mobile phones (see Section “ Methods ”). We trained the model on individual municipalities with data collected before the first earthquake hit the region (August 24, 2016). Then, we compared the prediction results with actual data, using ‘testing’ data after the first earthquake hit. Figure 2 illustrates the results in Accumoli, one of the municipalities hit the hardest by the earthquake. It is possible to see that forecast (orange) is much higher than the actual data (blue). The idea of this approach is that, since the forecasting algorithm receives only data before the earthquake happened, the forecast results (orange line) can be interpreted as the counterfactual scenario as if the earthquake had not occurred. In the “ Discussion ” Section, we discuss the limitations of this idea. The main advantage of MPE over PCHG is that MPE automatically deals with data seasonality.

Following this approach, for each municipality, we compute the mean (percentage) prediction error (MPE) as:

In this way, if forecast values are higher than actual ones, the MPE is negative and represents a percentage decrease in the population. Since there were multiple earthquakes spanning approximately six months, we further decompose MPE in two time-bound measures.

We compute \(MPE\;INIT\) focusing on ‘testing’ data from 25 August 2016 to 30 October 2016. This time frame coincides with the period between the aftermath of the initial shake (24 August 2016) and the two months after it. We also compute \(MPE\;END\) focusing on ‘testing’ data from 1 April 2017 to 30 October 2017. In this period, the medium-term effects of the earthquake become visible, allowing for an interesting comparison between the two-time frames. We chose these time frames to split between \(MPE\;INIT\) and \(MPE\;END\) according to data availability.

Figure 3 A shows the relationship between MPE and PCHG and the good linear relationship between the two variables( \(\beta = 0.72\;(0.02)\) , \(R^2=0.64\) ). Figure 3 B shows MPE results for all the municipalities. In this case, the variation in mobile phone data proves accurate. The area closer to the epicentre and mostly affected (the so called seismic crater ) has the highest negative MPEs indicating that the municipalities had the highest decrease in population.

Similarly, Figure 3 C shows a map of central Italy (left) and focuses on the earthquake crater area (right). It is possible to see that, in general, the crater area had the most significant reduction in population size, measured as the highest negative MPEs. However, there is high variability, and some municipalities in the seismic area witnessed an increase in their population.

Mobile phone data was collected in the Accumoli municipality (blue). Forecast data based on the pattern exhibited before the earthquakes (orange). Red vertical lines are associated with the four main earthquakes that hit the region. Actual data is 50% lower than forecast ones.

Each dot represents a municipality. Blue dots are municipalities outside the earthquake crater area, and orange dots are those inside the crater area. ( A ) Relationship between PCHG and MPE. ( B ) MPE for all municipalities under study. ( C ) Map, showing MPE for the Central Italy municipalities (left) and zoom in on the crater (right). Maps have been drawn using python libraries: matplotlib 3.5.2 and geopandas 0.2.12 – https://www.anaconda.com .

Population change, economic activities and damage impact

In this second set of experiments, we create linear models to explain the variation of MPE in terms of geographic and economic variables. Given the variability in MPE (and PCGH) observed in Fig. 3 , we created different models to explore the origin of this variability. Our intuition is that other than the earthquake magnitude and associated damages, the kind of economic activities in each municipality might impact population dynamics. For example, since earthquake usually affects built infrastructures, agricultural activities are likely to be less affected than manufacturing or logistics, but also because the latter typically involves more complex inter-dependencies among production plants, supply chains and logistics.

The following analysis provides evidence about this link between economic activities and displacement in municipalities. We developed three models (linear regression) to explain changes in MPE in the affected areas.

The model in Table 1 uses geographic features and earthquake damage features as covariates (independent variables). Geographic features of the municipality are the distance from the epicentre and median altitude (ALT MED). The epicentre distance can be a simple proxy of the effects of the earthquake on a given area (of course, there are many more factors determining the consequences of the earthquake). In addition, given that the seismic area comprises several small centres surrounded by widespread rural and mountainous zones, most of which are isolated and accessible only by minor roads, we considered the municipality’s (median) altitude as a simple proxy for municipality isolation. Earthquake damage features are the number of requests for rebuilding (RCR) from private (RCR PRIV) and public (RCR PUB) entities that are a proxy for earthquake destruction (see Section “ Methods ”).

The model in Table 2 uses geographic features and economic sectors’ features . The considered classification of economic sectors is the ATECO (1-letter code) which contains unique identification codes for every economic activity. To limit the number of variables, we consider only those sectors contributing to at least 1% of the total number of companies and 1% of the total revenue of the area (see Section “ Methods ”, also for comparison with the results with all the sectors considered).

The last model (Table 3 ) combines all the previous covariates: geographic features , earthquake damage features and economic sectors’ features .

In Table 1 , we see a positive relationship between the distance from the epicentre and MPE in all three measures, indicating the accuracy of MPE in capturing the displacement of inhabitants, both in the first response to the event as well as a later one. The number of requests for rebuilding from the public sector (RCR PUB) is significant and negative: the higher the amount of RCR, the lower the MPE (greater negative MPE, e.g., − 50% in cell phone presence), meaning that there is a higher number of displaced people, probably because of the destruction. The number of requests for rebuilding from the private sector is significant (and negative) only for MPE INIT. RCR PRIV is a proxy for the damage to homes and private buildings. This damage implies the immediate displacement of people. Therefore, MPE INIT better captures displacement, while RCR PUB represents public buildings and infrastructures. Finally, MPE END represents the impact on the long-term habitability of the area. In this analysis, we considered the number of requests presented for funding. We conducted a similar study using the initial assessment of the damage of the total amount in Euros of the requests obtaining similar results (see Section “ Methods ”).

In Table 2 , we added the industrial sectors to explore what might affect MPE (a short description of the variables associated with industrial sectors in the regression is in Table 4 ). First, the distance from the epicentre positively affects MPE in all three measures. The altitude of the municipality is significant for MPE END and MPE, indicating that locations with higher altitudes (likely to be more rural and less connected) have a higher decrease in population. Concerning economic sectors, we observe that constructions, hospitality and manufacturing affect MPE INIT. The effect is present, also in MPE END for logistics, manufacturing, whole retail sales and finance, but not for hospitality and construction. These two sectors show no significant impact on MPE END. This effect may be because community resilience does not relate to hospitality and construction. However, these activities relate to the immediate responses to the disaster, such as rescue operations and activities to secure crumbling buildings and houses. Therefore, they can drive an increase in the number of people in the area. The evidence presented here suggests that more modern economic sectors (secondary and tertiary), which require supply chains, logistics and interconnections with other sectors and geographical areas, have been heavily affected by the disaster, and this effect is long-lasting. Furthermore, this suggests the need for more active policy interventions to support the recovery of these industries and economic sectors.

Finally, the coefficients in Table 3 support the previous conclusions. With more variables, the model has a greater explanatory power ( \(R^2\) ). Figure 4 shows the model result with the actual MPE, MPE INIT and MPE END and the predicted ones. The reported graph illustrates the fitting of the models. It is worth explaining that the model is fitted on 123 municipalities (observations) of the seismic crater, while official reports identified 140 in that area. This mismatch is due to missing data for 17 (12%) municipalities. We did not consider these municipalities in the model.

Actual and regressed MPE, MPE INIT and MPE END in the earthquake crater zone using the model with covariates: geographic features , earthquake damage features and economic sectors’ features .

When sizeable portions of the population move out because of a disaster, massive economic and social losses are observed in the affected areas. Being able to quickly and reliably measure these changes inexpensively and unobtrusively is very important. This study shows that aggregated mobile phone data can represent a reliable indication of population variation in the aftermath of an earthquake. Moreover, the combination of CDR with the census, economic and administrative data from four Italian regions affected by repeated earthquakes in 2016 allows us to identify key characteristics that can help to explain displacement dynamics. Our results show that analyzing CDRs can be extremely useful in the aftermath of the disaster, but also that they can be fruitfully related to other data sources related to the reconstruction process.

Mobile phone data (CDRs) can naturally enrich and complement other approaches to estimate displacement (e.g. aerial images 21 , night-lights satellite images 22 , social network, crowd-sourced and damage assessment data 23 , 24 , 25 ) offering ready availability, extremely long monitoring periods, and almost complete coverage of the population. Differently than mobile phone data at the individual level , which are very hard to get due to privacy regulations, aggregated data are readily available without privacy concerns. The method proposed here for the analysis of CDRs data offers the opportunity of knowing soon after the disaster (the time frame depends on data availability) how many people moved out of the affected area (measured by the introduced MPE value).

The approach used to compute this displacement is one of the main novelties of our work. We calculate the value MPE as the mean percentage prediction error between the actual data and a non-autoregressive forecasting model trained with only data collected before the earthquake hit. As the model never receives data during or after the earthquake, we interpret its forecast as the counterfactual scenario as if the earthquake had not happened 50 . We do not claim this to be a full-fledged causal analysis, and we are aware that the results might be partial: there are likely some confounding factors not accounted for by our model. Nonetheless, we are confident that our analysis captures, at least, part of the counterfactual scenario.

This displacement estimate based on mobile phone data can be extremely useful in emergency intervention planning, especially in remote or widely spread areas. In the initial emergency stage, knowing how many people need alternative accommodations, how many should receive which forms of economic support and where there is a high expectation of population change are all relevant variables that can stir the emergency management phase in different directions.

Third, our study shows how fruitful it is to combine CDRs data with existing datasets to explore which characteristics of the disaster and the area can explain variations in the number of residents. In our study, the calculation of MPE and its combination with data about the economic activities in the affected areas shows that some areas are more vulnerable than others, and further studies would help understand better the extent of these vulnerabilities. The need to identify vulnerable areas is especially relevant in a country like Italy that, because of the particular morphology and geophysical context of its territory, is one of the five European countries with the highest probability of a disaster and related economic loss 51 . Readily available information about the impact of an earthquake in terms of immediate displacement can be part of strategic planning for temporary housing that identifies opportunities and constraints for temporary housing before the disaster 52 . Local, regional and national institutions face what has been called the “post-disaster recovery dilemmas”, i.e., the need to balance short-term and long-term needs for vulnerability reduction 52 . There are no obvious solution to such an issue, which greatly varies depending on the situation, but our results can inform policies aimed to support financially and logistically those industries most affected by the disaster, those that are supposedly more relevant for the area’s resilience and the country’s economic growth. Our results are limited in time, location, and kind of disaster, but they can provide a valuable avenue for future research. Even if our methodology needs to be further refined, we point out the relevance of the indications about the relationship between the kind of economic activities and the funding requests. In our data, hospitality is the sector that suffered the least, while the most affected municipalities are those characterized by construction and manufacturing activities. This finding could indicate that, in vulnerable areas, some economic activities could be more resilient to disasters, and their presence can positively affect the reconstruction.

In addition, by using administrative data about the requested funds to rebuild homes and combining it with economic data and MPE, we can offer an overview of the reconstruction process. By calculating MPE, the effective damage and the intention to stay (as the requests for funds necessary to rebuild or repair the damaged houses), we observe that MPE seems to be a good predictor of the latter variable. This finding confirms its efficacy as a medium-term indicator in a situation in which displaced citizens have relocated without registering as residents in another municipality, thus creating difficulties for institutional actors to know the actual magnitude of the displacement and to take adequate measures to assist the population.

For instance, when deciding where to locate aid centres and shelters, it is important to know where most of the population has moved. This data can enrich the vast pool of information (e.g., established emergency plans, conditions of buildings, roads, and transports) to guide and optimize the positioning of such facilities.

A further avenue for future research is using data about the post-earthquake reconstruction process that the Italian government has made available for three different earthquakes that happened in the last 15 years (L’Aquila, Emilia-Romagna and Central Italy). Our results indicate that using data about expected and actual funding requests for rebuilding could offer a valuable indication of displacement and its medium-term consequences. It is worth stressing that physical reconstruction does not necessarily lead to community recovery, and several factors can affect how much communities can “build back better” 18 , 53 . However, our data and analysis do not allow us to draw any conclusions about community recovery. Qualitative and quantitative individual data, like interviews, focus groups and surveys, would be needed to investigate the relationship between infrastructural reconstruction and community restoration.

As a final remark, we want to point out that in this work, we use data from the Telecom operator in 2015-2017, as the earthquake hit the region during that period. Mobile phone data help analyze displacement right after the disaster and in the months and few years after the fact when official data are often missing or incomplete. Longer-term observations (e.g., using data available now - 6 years after the fact) are less useful as official statistics and data about the reconstruction take the lead role in such a time horizon. Nevertheless, the proposed methods can be applied to other disasters in other years without modifications, and data gathered after different kinds of disasters across diverse countries would be necessary to ground the validity of this approach.

Conclusions

From the above analysis, it is possible to draw the following conclusions which answer the stated research questions: (i) completely anonymized and aggregated mobile phone data can monitor population displacement after disaster scenarios. The proposed method computes displacement by comparing the actual mobile phone count with the forecast computed before the earthquake hit (see Fig. 3 ). (ii) Geographic features (epicentre distance and altitude) and damage-related features (public and private RCR) explain part of population displacement (see Table 1 ), (iii) also the industrial base and composition of the regions contribute to explaining part of population displacement (see Table 2 ). A better-performing model combines the above features (see Table 3 ).

Methods and supplementary analysis

In this work, we processed four different sources of data to extract and analyze behavioural patterns before and after the earthquake in central Italy in 2016: (i) aggregated Call Detail Records; (ii) economic activity data; (iii) data about damage and recovery activities and (iv) census and demographic data.

On the one hand, aggregated Call Detail Records are the data at the basis of the proposed approach. They allow us to compute population displacement (MPE) at a fine-grained scale. In principle, they are available in quasi-real-time for emergencies and provide timely and valuable information. On the other hand: economic activity, damage, recovery, census and demographic data are functional to the analysis presented in the results to explain the factors affecting MPE.

Call detail records

We obtained from TIM, the largest mobile phone operator in Italy at the time of the data collected (30.2% market share 54 ), a large dataset of aggregated CDRs (Call Detail Records) measuring the number of cell phones localized by the network in a given area of the territory. Data covers the whole of Central Italy, divided into a matrix of small square areas of 150 meters side, called pixels. The dataset consists of the number of devices in each pixel every 15 minutes. The localization procedure is proprietary to the telecom operator. Localization is based on signal strength triangulation at the different antennas (see Fig. 5 ). The data contains only people count. It is not possible to reconstruct the movement of individuals. In addition, to remove the possibility of singling out individuals, the value of any pixel with fewer than N individuals (typically N = 4) is set to zero. As the data measures mobile phones from a single operator, we linearly scale numbers to consider the operators’ market share in the area. Nevertheless, our data do not fully compensate for people not having mobile phones (e.g., the presence of children and elderly). Further research would be needed to address this aspect. However, for the presented results, it is worth considering that: (i) Italy is one of the countries with the higher number of mobile phone operations, (ii) our data strongly correlates with census data—see Fig. 1 A- hinting to a limited effect of this bias.

The mobile phone dataset spans the following periods: from 2015-04-01 to 2015-10-30, from 2016-04-01 to 2016-10-30 and from 2017-04-01 to 2017-10-30, to capture presence before and after the earthquake.

To make the dataset more manageable and have areas compared with other datasets, we aggregated data at the municipality level and the 1-hour time interval. This procedure reduces possible biases due to setting small counts to zero, as the aggregation over whole municipalities leads to higher counts.

Overall, we have data for 663 municipalities with 14,420 hourly measures each. Figure 6 shows an example of the data collected for each municipality.

We want to emphasize again that we deployed fully aggregated data. While individual mobile phone traces are very privacy sensitive, as they allow tracking single phones and, therefore, users, aggregated CDRs counting the number of phones in a given area is privacy compliant as the aggregate number does not allow to single out individuals. The operator carried out the entire aggregation process in compliance with privacy regulation (GDPR), and most telecom operators have commercial activities to exploit this kind of data. The possibility to deploy this kind of data is a strength for the actual applicability of our approach. While individual data would require strict scrutiny and control from privacy regulators, telecom operators sell aggregate data. Even more, the value recognition of this data for applications like the one described should push the government and public bodies to create partnerships with a data provider to ensure the prompt availability of this data.

Cell phones are localized by the telecom operator in an area of 150 * 150 meters, called a pixel. Aggregated counts for each pixel is the data provided by the telecom operator in compliance with privacy regulation. Data have been further aggregated at the municipality level to make it more manageable and have areas comparable with other datasets. Maps have been drawn using python libraries: matplotlib 3.5.2 , geopandas 0.2.12 and folium 0.14.0 — https://www.anaconda.com .

Mobile phone collected for the municipality of Accumoli. The time series records the number of mobile phones at an hourly resolution. Red vertical lines are associated with the four main earthquakes that hit the region.

Economic activity data

AIDA, created and distributed by Bureau van Dijk S.p.A., is a database, available upon subscription, containing the financial statements and personal and product data of all Italian companies, both active and bankrupt. For each company, the dataset includes the industry/sector, the number of employees and the legal and operating addresses, which allows for localizing them at the municipality level.

Table 4 shows the overall number of companies present in the seismic area.

In particular, we used this data to compute the industrial composition of each municipality. We considered all the companies active during the earthquake (August 2016). We grouped companies accordingly to their industrial sector as specified by standard ATECO 1-Letter codes (e.g., Agriculture, Arts and Entertainment, Buildings, Commerce). For each group, we computed the total revenue. Finally, we normalized the count to sum to 1. The result represents the industrial composition of a given municipality (e.g., Municipality A has: Agriculture=0.8, Arts and Entertainment = 0, Construction = 0.2). We used the total revenue as a proxy of the relative weight of the company. We run experiments using the total number of employees, obtaining similar results.

Damage and recovery data

Governmental agencies in charge of designing policies for the reconstruction have devised a multi-step approach to collect data about damage and requests for recovery. Reporting these phases is essential to understand the nature of the data.

The Italian Civil Protection, the first responding agency to access the affected area during and after the population’s evacuation, initiated the damage assessment (first phase). The experts of Civil Protection had to fill in a specific administrative file in which they reported whether it was safe to access the building and the level of damage. If the building was unsafe (in Italian inagibile ), the owners could request governmental funds to cover the rebuilding costs. These technical reports provide a first assessment of the damage, and we used them to compute the potential forecast amount of funding requests for the reconstruction. We called them RCR Forecast ( Richiesta Contributo per la Ricostruzione ). These files helped to develop a preliminary assessment of the amount of funding needed for the reconstruction, but this first step must be followed by several others. Only after a second request compiled by another expert in compliance with all the regulations has been submitted by the owner of the damaged building (residential, commercial, or industrial) do the governmental agency and the municipality allocate the necessary funds for reconstruction. Once the expert has submitted the request, the administration evaluates the report and responds. The number of requests for funding presented after these stages is what we define RCR Submitted. It is worth stressing that RCR Forecast and RCR Submitted are not identical. Therefore the two datasets may differ. This difference is relevant because the initial forecast is based only on the assessed damage of a given building, regardless of the owners’ decision to rebuild them. If the damage is considered negligible, homeowners may decide not to proceed with the request for funding because the procedure can be expensive and the fund received are insufficient to fix or renovate the damaged house. In addition, the damage assessment process can take between a few months and some years. In the meantime, individuals can decide to relocate and never come back. In this case, there is no willingness to continue the process to obtain funding, and a discrepancy between the initial damage assessment and the final one might emerge.

https://sisma2016data.it is the open data initiative (supported by the Italian Government) to monitor damage assessment and recovery plans for the earthquake in central Italy in 2016-2017. This dataset provides information about damage assessment and reconstruction, including the expected requests for rebuilding (RCR Forecast) and the actual ones presented by inhabitants (RCR Submitted) for each municipality. RCR Submitted is available both for private and public buildings in terms of absolute numbers of requests and total amount in euros.

RCR Forecast . The forecast number of requested funding (RCR) is calculated based on experts’ first assessment of the damage.

RCR Submitted The number of requests presented, which will be able to access the funding (although not necessarily accepted at the current date). These RCRs can be private (private buildings, including commercial activities) and public (including buildings belonging to the state, like schools, and religious buildings, such as churches). For each category, available data are the absolute number and the requested amount in Euros.

All the measures are normalized based on the number of inhabitants of the relative municipality.

Census and demographic data

We used data from the official census data about the resident population and changes in residency in the municipalities under study. Thanks to the census data estimated by ISTAT between 2002 and 2018, we deployed the municipal demographic balance. We extracted the number of residents and the number of residency changes in 2015, 2016 and 2017 from the database ( https://demo.istat.it/ricostruzione/ ). The database allows the extraction of municipalities for each province and each year. Therefore we aggregated data at the yearly and provincial levels to obtain the entire dataset on population variation.

Resident population from mobile phone

Several studies identified algorithms to extract resident population from mobile phone data 55 , 56 , 57 . The main criteria to consider is the average number of mobile phones at night during weekdays as a proxy for the resident population. We applied this procedure also in this work considering mobile phone data from 1 am to 5 am as “night” data. The resulting estimate is naturally lower than census data as we monitor just one telecom operator, and there might be bias in the sample population (e.g., children who do not have phones). We partially compensate for those biases by scaling our results by the telecom operator market share.

Figure 1 A, presented above, shows the relationship between the resident population monitored by mobile phone data and actual census counts. Figure 7 A shows the number of mobile phones for each municipality before the earthquake. Figure 7 B presents the percentage change in mobile phones before and after the earthquake (PCHG).

( A ) Number of mobile phones for each municipality before the earthquake. ( B ) Percentage change of mobile phones before and after the earthquake (PCHG). Maps have been drawn using python libraries: matplotlib 3.5.2 , geopandas 0.2.12 – https://www.anaconda.com .

Forecasting mobile phone data

We created a forecasting model to predict population dynamics measured by mobile phones. The model trained on individual municipalities and macro-regions (crater and out-of-crater regions) with data before the first earthquake hit the area (24 August 2016). Then we used the model to forecast population dynamics.

The main idea of the adopted forecasting approach is to use non-autoregressive models. In this way, the model never receives information about the earthquake hit. Therefore – although we are fully aware that there might be several confounding factors and this is not a complete causal analysis – we think of the forecast as a counterfactual scenario in which the earthquake did not happen.

The forecasting model uses only features associated with the timestamp: year ( Y ), week of the year ( WoY ), days of the week ( DoW ), and hour ( H ). Features other than a year are considered categorical (and therefore one-hot encoded). The model uses these features to forecast the number of people (cell phones) in the area. We tested both a linear model and a decision tree regressor.

For example, in the linear model, for each municipality and macro-region, we fit a model like the one in the equation below. The training procedure learn parameters \(\alpha\) , \(\beta _i\) , \(\gamma _j\) , \(\delta _k\) using training data before the earthquake. Then, it uses the equation to forecast future time steps. The case of the decision tree regressor is perfectly analogous.

Figure 8 shows the forecast result -linear model - applied to all the regions in and out of the crater area. These in the crater area see an average decline in the mobile phone (MPE) of -6.5%. These out of the crater see an MPE of -1.5%. Results from decision tree regressor exhibit a similar pattern.

Forecast number of phones. ( A ) In crater regions. ( B ) Out of crater regions.

Regression models

We developed several Ordinary Least Squares (OLS) models in our study. All the models try to explain MPE based on multiple data:

We considered the industrial composition of each ATECO 1-Letter code (e.g., Agriculture, Arts and Entertainment, Buildings, Commerce) and the percentage of companies of that code. To limit the number of variables, we consider only those sectors contributing to at least 1% of the number of companies and 1% of the revenue of the area. Other sectors are discarded in Section “ Results ”. Nevertheless, we report the OLS with all the sectors in this Section.

We computed the distance from the epicentre as the length from the closest epicentre of the earthquake.

We considered the median altitude of the municipality as related to the municipality isolation.

We considered information about damage assessment and reconstruction, including the expected requests for rebuilding (RCR Forecast) and the number of the actual request presented by inhabitants (RCR Submitted) for each municipality. RCR Submitted is available both for private and public buildings in terms of absolute numbers of requests and the total amount in euros.

In Section “ Results ”, we discussed the main findings of our model. In this Section, we show results analogous to Table 1 , but using RCR forecast as a measure of damage assessment (see Table 5 ). The conclusions stated in Section “ Results ” hold in this case: RCR forecast contributes significantly and negatively to MPE, the more RCR (i.e., destruction) more the population displacement.

Similarly, we report results using all the ATECO codes. The introduction of the “minor” sectors (i.e., public administration, entertainment, mining, waste management, extraterritorial organization, education, and other services) does not affect the results significantly (see Table 6 ).

Spatial auto-correlation

We tested the spatial auto-correlation for all the main variables involved. The presence of spatial auto-correlation is trivial given the localized nature of the earthquake, as it is also apparent in Fig. 3 C. Figure 9 presents Moran’s analysis of all the main considered variables. The presence of spatial auto-correlation hints at the influence of the earthquake on the monitored variable.

Spatial auto-correlation analysis. Moran’s plot for MPE. In the table Moran’s I for all the main variables.

Predict variation

In this section, we propose an approach complementary to the one presented in the above results to analyze MPE after the earthquake. The idea is to create a set of variables summarizing (and reducing the dimensionality of) the features describing different municipalities. Then, we apply a clustering mechanism to the resulting factors.

We consider and transform the variables relevant to the population variation in the earthquake areas.

The considered features are calculated for each municipality as follows:

The number of companies in the construction sector ( Constructions );

The number of companies in the accommodation and restaurant sector ( Hospitality );

The number of companies in the wholesale and retail trade sector ( Whole/retail sales );

The number of companies in the professional services sector ( Tech services );

The number of companies in the renting sector ( Business support services );

The total number of employees ( Employees );

The mean altitude in meters ( Alt med );

The province ( Province );

The municipality ( Municipality );

Factor analysis has enabled us to transform and reduce the number of selected features. Initially, we considered a higher number of features. The final selection has been performed based on trial and error and according to the proportion of each variable’s variance explained by the factors (commonalities).

We applied Kaiser Varimax rotation 58 on the above set of variables to identify factors that synthesize the considered variables. Based on the results, we identified three factors representing the variables. Figure 10 presents the results: Factor 1 has high factor loadings for Constructions , Hospitality , Whole/retail sales , Tech services , Business support services and Employees . Therefore, we named this factor as Industrial_structure . The second factor formed using the variable Alt_med was named Physical_characteristics . Finally, the third factor is the result of Province and Municipality so that it has been named Administrative_characteristics . The commonalities of each variable are above 77%. Therefore it is possible to conclude that the identified factors reasonably and reliably explain the observed variables. We applied the factors to the entire dataset and created three new variables, summarizing the above features.

Based on the three newly created factors, we implemented an agglomerative cluster analysis to identify three clusters of municipalities. Figure 10 shows that one group of municipalities is concentrated in the crater area even though we did not deploy the distance from the epicentre to create the clusters. Furthermore, three have a substantial variation in the number of mobile phones. The similarity of this map with Figs. 3 C and 7 B confirm the impact of industrial, physical and administrative features to explain and predict mobile phone variation (MPE).

(left) Results of the factor analysis using a Varimax rotation of the axes. (right) Map representing the three clusters of municipalities created based on the new factors. Map has been drawn using python libraries: matplotlib 3.5.2 , geopandas 0.2.12 – https://www.anaconda.com .

Data availability

Data and code to reproduce our experiments is available at https://github.com/mmamei/earthquake2016 .

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Acknowledgements

We would like to thank Telecom Italia (TIM) for mobile phone data access. Work supported by: the University of Modena and Reggio Emilia FAR Project: Data driven methodologies to study social capital and its role for economic growth.

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These authors contributed equally: Francesca Giardini, Natalia Selini Hadjidimitriou, Marco Mamei and Francesca Pancotto.

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Department of Sociology, University of Groningen, Grote Rozenstraat, 31, 9712 TG, Groningen, The Netherlands

Francesca Giardini

Dipartimento di Studi Linguistici e Culturali, Università di Modena e Reggio Emilia, Largo S. Eufemia, 19, 41121, Modena, Italy

Francesca Pancotto

Dipartimento di Scienze e Metodi dell’Ingegneria, Università di Modena e Reggio Emilia, Via Amendola, 2 - Pad. Morselli, 42122, Reggio Emilia, Italy

Natalia Selini Hadjidimitriou, Marco Mamei, Giordano Bastardi & Nico Codeluppi

Artificial Intelligence Research and Innovation Center - AIRI, Università di Modena e Reggio Emilia, Via Vivarelli, 10, 41125, Modena, Italy

Marco Mamei

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F.G., N.H., M.M., F.P. conceived the experiments, N.H., M.M., G.B., N.C. conducted the experiments and wrote code, F.G., N.H., M.M., F.P. analysed the results. All authors reviewed the manuscript.

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Giardini, F., Hadjidimitriou, N.S., Mamei, M. et al. Using mobile phone data to map evacuation and displacement: a case study of the central Italy earthquake. Sci Rep 13 , 22228 (2023). https://doi.org/10.1038/s41598-023-48130-4

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With a total solar eclipse set to pass through the United States on Monday, it is easy to imagine a linkage between unusual events in the heavens and on Earth. But geoscientists were cautious about making such a connection.

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Katrina Miller is a science reporting fellow for The Times. She recently earned her Ph.D. in particle physics from the University of Chicago. More about Katrina Miller

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“Recovering, not recovered” Hospital disaster resilience: a case-study from the 2015 earthquake in Nepal

Maria moitinho de almeida.

Centre for Research on the Epidemiology of Disasters (Cred), Institute of Health and Society, Université catholique de Louvain, Brussels, Belgium

Disasters are an increasing threat to human health, but we know little about their impact on health services, particularly in low and middle-income settings. ‘Resilient hospitals’ have been increasingly recognized as a cornerstone of disaster management. While various frameworks of hospital resilience exist, they emerged from pre-disaster considerations, and do not incorporate evidence from post-disaster settings.

This dissertation investigated the impact of a large-scale sudden onset disaster in a tertiary hospital in Nepal, and explored its resilience mechanisms.

Methodology

This consists of an in-depth case-study combining quantitative data from routinely generated hospital records and qualitative data from semi-structured interviews with hospital staff. We used both advanced statistical methods and mixed inductive and deductive coding to analyze the data.

Most of the admitted earthquake victims required surgical interventions and long hospitalizations, considerably straining the hospital. For six weeks, the average number of daily admissions decreased. During this period, the share of injury-related admissions was particularly high, and such admissions were particularly long compared to the baseline. Admissions due to other conditions relatively decreased and were shorter. We found that the hospital’s resilience was highly dependent on emerging adaptations, in addition to the pre-existing disaster plan. Individual resilience of staff also played a major role, and was influenced by senses of safety, meaningfulness, and belonging.

Hospitals should prepare resources and plan for their known disaster risks, but should also allow for a certain flexibility to innovative adaptions to emerging, unforeseen challenges. Challenges faced by hospital workers should not be undermined, and addressing them will increase hospital resilience.

Disasters are not natural. They consist of serious disruptions in the ‘functioning of a community or a society’ [ 1 ], and they result from interactions between a hazard, which can be natural, and the community’s vulnerability and coping capacity. Earthquakes are an example of large-scale, sudden-onset disasters, because they occur quickly and unexpectedly, and they can be so destructive that affected communities need external assistance [ 1 ]. Millions of earthquakes occur every year around the globe, but only small part of these are sufficiently strong to be measured. Of these, a very small fraction actually has human impact [ 2 ]. Between 2000 and 2019, 552 earthquakes with human impact were captured in the international disaster database (EM-DAT), having affected about 118million people [ 3 ]. Earthquakes are not distributed equally across the globe, and Asia is disproportionately affected, having hosted two thirds of earthquakes that occurred in the last 20 years [ 3 ].

Earthquakes carry important human health consequences. A major predictor of mortality is the built environment, and head and trunk injuries are typically deadlier than injuries to the limbs [ 4 , 5 ]. The number of injured victims can be very high after earthquakes, making hospital care an important aspect of disaster response. However, hospitals face several challenges and their ability to provide care can be seriously disrupted after large-scale sudden-onset disasters. They can suffer from building damage or even collapse, hospital staff can be affected or unable to reach the workplace, patients themselves may be unable to access the hospital, and there is a sudden increase of healthcare demand [ 6 ].

Resilience of health services

It is hence important that hospitals are able to absorb the shock of disasters, retain their essential functions, surge their capacity to provide emergency care, and recover to their original or to a new adaptive state. In other words, resilient [ 7 ]. But resilience is a maturing concept and its definition remains elusive in multiple fields of knowledge [ 8 ].

With regard to health, resilience is often linked to a community’s or a system’s capacity to cope with and manage health risks while maintaining essential functions of health systems [ 9 ]. Health systems and health services are complex systems and can be influenced by external shocks that disrupt their functioning [ 10 ]. Health System Resilience can be presented in a ‘shock cycle’ that consists of four phases [ 8 ]:

• System preparedness to shocks
• Shock onset and alert
• Shock impact and management
• Recovery and learning

For a national or regional health system to be resilient, the health facilities that compose it must also be independently resilient, increasing the complexity of health system resilience. Different frameworks have separately attempted to define health system or health service resilience, and the fact that many have emerged in parallel further challenges operationalizing the concept of resilience. Table 1 [ 11 ] presents an overview of existing frameworks of hospital and health system resilience.

Overview of major hospital and health system resilience frameworks [ 11 ]

Hospital resilience was the first resilience concept to emerge in the domain of health systems and services. More comprehensive than ‘preparedness’ and ‘safety’, the concept of hospital resilience includes the actual disaster scenario. The engineering sciences were the first to study this phenomenon, and used the 4 R framework to conceptualize hospital resilience [ 14–16 ]. This framework consists of ends of resilience (Robustness and Rapidity) and means of resilience (Redundancy and Resourcefulness). Robustness is the strength to withstand a given level of stress without suffering degradation or loss of function. Rapidity means that priorities are met

and goals are achieved in a timely manner, in order to contain losses, recover

functionality, and avoid further disruption. Redundancy is the ability to replace disrupted elements, and Resourcefulness is the capacity to mobilize and use resources, including through coordination [ 14–16 ].

But these concepts needed to be easily interpreted by hospital managers and health decision-makers, which led to the research conducted by Zhong and colleagues [ 7 , 17 , 18 ]. They found that a hospital’s resilience depended on structural and non-structural components, as well as emergency medical functions and disaster response capacity [ 7 ] and, they identified four primary domains where the 4 R dimensions could be applied: (i) hospital safety and vulnerability, (ii) disaster preparedness and resources, (iii) continuity of essential services, and (iv) recovery and adaptation. The empirical work that followed to develop a hospital resilience assessment tool included expert consultations [ 18 ] and a pilot test in 41 tertiary hospitals in China [ 17 ]. A more recent systematic review identified a set of hospital resilience indicators that could also be linked to the 4 R dimensions [ 19 ].

However, while research is ongoing and advancing this unique field of knowledge, many questions remain. First, we know little about how disasters affect the functioning of hospitals, particularly in low and middle-income settings. Second, while hospital resilience frameworks exist, they were not developed from actual disaster contexts. This means that we don’t know whether they actually picture what happens after disasters occur. It is a critical limitation as resilience is more comprehensive than just preparedness, and includes phases such as response and recovery. These important research gaps hinder optimal disaster management, and addressing them can substantially reduce the human impact of disasters.

Aim and objectives

This dissertation investigated the impact of an earthquake on the functioning of a tertiary hospital in Nepal, and explored hospital resilience mechanisms. The specific objectives were:

• To study the clinical and demographic profile of earthquake victims who were admitted in the hospital, and what influenced their length of hospital stay (LOS)
• To compare hospital admissions before and after the earthquake, and estimate the effect of the earthquake on admissions
• To assess the impact of the earthquake on the hospital functioning, and explore resilience as experienced by hospital staff.

This dissertation consisted of an in-depth case-study that combined quantitative and qualitative methodologies, allowing to explore a complex phenomenon in its real-life context [ 20 ]. It is based on a series of three articles, each focusing on a different study addressing a specific objective. Table 2 presents an overview of the studies, their methodologies, and their relation to the health system resilience shock cycle. Study I and II both used complementary datasets containing information about patient admissions in the hospital. Study III collected qualitative information through 18 in-depth interviews with hospital staff from different professions and seniority levels; 7 of those interviews were conducted in Nepali with an interpreter present, and the transcripts were later translated into English. For this study, we used a mixed deductive and inductive approach, using the 4 R resilience framework [ 14–16 ], which was theoretical framework of this thesis.

Overview of studies composing the dissertation presented in this article

The complementary use of different sources and type of data is believed to increase the internal validity of a case-study, and helps create a holistic picture of the topic under study. Using mixed-methodology in health service research and humanitarian contexts also allows to gain a comprehensive view of complex phenomena [ 21 ]. Understanding contextual factors is also critical in case-studies, and this is more successful with field work and associated immersion, observations and interactions [ 22 ]. In this case, field work was an essential aspect to develop the studies presented in Table 2 .

The focus of this work was a reference tertiary hospital in Nepal, a lower middle-income country landlocked between India and China that faced political instability for decades until 2008. Nepal is considered at high risk for humanitarian crises and disasters [ 23 ], and the entire territory is regularly affected by disasters with reported human impact [ 3 ]. While maternal and child mortality indicators have seen steady improvements over the years [ 24 ], several health challenges persist in Nepal. For instance, hospital care is only free for people in verified poverty situations and other vulnerable groups [ 25 , 26 ], leaving a substantial share of the population paying out-of-pocket for health care. On Saturday, 25 April 2015, a high-magnitude earthquake Nepal, followed by many aftershocks, a major one on May 12 th . This series of earthquakes killed nearly 9,000 people and injured another 22,000 [ 27 ]. Almost one third of the country’s population was affected by the disasters, and about 84% of the health services in the affected districts were destroyed or damaged [ 28 ]. The study hospital, located in the capital city Kathmandu, was built with earthquake-resistant standards and had a disaster plan in place [ 29 , 30 ], having played a major role in the earthquake response [ 31 ].

Study I: what is the profile of earthquake victims who needed hospital admission?

We studied the profile of 501 earthquake victims who were admitted in the study hospital [ 32 ]; 254 (51.2%) were women and 17.2% (n = 85) were children aged 0–14 years. Nearly half (n = 195, 48.9%) had a lower limb injury as main diagnosis of admission, and two thirds (n = 226, 65.7%) needed orthopedic surgery. Fractures represented 65.8% of all injury cases (n = 288). The most common cause of admission were femur and lower leg fractures, accounting for 26% of all earthquake victim admissions. For diagnoses not belonging to the injury group, the most common cause of admission was coded as ‘post-surgical states’. Date of admission ranged between 0 and 166 days after the first earthquake of April 25 th ; the peak occurred five days after the earthquake with 77 admissions. In 37 cases (7%), death was reported as an outcome.

The median length of stay in the hospital was 10 days, and the mean was 14.7 days. We first conducted bivariate log-rank tests, and found that demographic variables were not associated with length of hospital stay. We calculated individual hazard ratios for the variables that showed a significant association with length of stay, and they are presented in Table 3 . Longer hospitalizations were associated with lower limb and trunk injuries, crushing injuries, and undergoing an amputation or plastic surgery.

Measures of association (unadjusted hazard ratios) of different characteristics with hospital length of stay

HR: hazard ratio (unadjusted); 95%CI: 95% confidence interval; Z: Z-score; p: p-value; Ref: reference category. Significant p-values (lower than 0.05) are presented in bold. This table is adapted from [ 32 ].

Consistent with the literature, the majority of the victims who made it to the hospital and were hospitalized had orthopedic injuries and underwent surgical intervention. This is probably because such injuries are more survivable, as opposed to injuries to the head, chest or abdomen [ 5 ]. However, in this study earthquake victims have particularly long hospitalizations; information from the other two studies provides additional insights into this finding. Another finding that merits attention is the fact that children are underrepresented in this sample in comparison to the population distribution in Nepal at the time of the earthquake, which was estimated at 33.4% [ 24 ]. This can be because the earthquake occurred on a Saturday during the day, and children were not in school neither sleeping, so they were not as affected as they could have been. But another plausible explanation is the fact that children have growing bones which are more resistant than adult bones, and when they sustain a fracture they don’t need surgery as often as adults do [ 33 ]. This would mean that many fractures in children did not warrant inpatient treatment, and are hence not reflected in admission data.

Study II: how were hospital admissions affected with the earthquake?

We included 9,596 admissions occurring between March 15 th and 17 August 2015, and defined four periods of analysis: a pre-earthquake baseline (pre-EQ), acute (EQ1), post-acute (EQ2), and post-earthquake period (post-EQ). EQ1 and EQ2 were three-week intervals after the April 25 th earthquake. The rationale for this approach is explained elsewhere [ 34 ].

Overall, the most common causes of admission were injuries, pregnancy-related conditions, diseases of the digestive system, respiratory diseases, genitourinary diseases, and factors influencing health status and contact with health services. The post-EQ period contained 49% of all admissions, followed by the pre-EQ period with 26%. Women accounted for 56% of all admissions, while children under 15 years of age represented 17% of all admissions.

Average length of stay (LOS) was significantly longer in EQ1 than during pre-EQ (9.80 vs. 7.05, respectively, p < 0.001). This was particularly the case for injury-related admissions, where LOS increased by 57.3% (CI: 37.0–80.7; p < 0.001), whereas LOS for respiratory diseases was 21.6% shorter in EQ1 (CI: 7.1–34.6; p = 0.008).

In EQ1, the odds of injury admissions increased (aOR = 5.33, CI: 4.44–6.40), while they decreased for the majority of other diagnoses. Pregnancy-related admissions relatively decreased in EQ1 and remained low until post-EQ. The total number of admissions dropped in EQ1 and EQ2, and returned to pre-EQ trends in post-EQ. We estimate that there were in total 381 fewer admissions in this six-week period (CI: 206–556).

These results consolidate the findings from the previous study. The injury patterns seen after the earthquake in our study hospital required particularly long hospitalizations compared to before the earthquake. This may be related to injury characteristics, associated with high-energy trauma, and to the fact that this is a reference hospital and this is probable a selected sample of more severe cases. Two other findings merit attention: the relative but sustained decrease of respiratory and pregnancy-related conditions. In fact, previous work has reported an increase of respiratory diseases after earthquakes [ 35 , 36 ]. One explanation is the fact that respiratory conditions sustained would not require hospitalization, and would rather be reflected in outpatient care. Indeed, a study in a hospital near Kathmandu found an increase in emergency department visits due to respiratory diseases [ 37 ]. Such an explanation is not plausible for pregnancy-related admissions, and it may indicate that pregnant women are not receiving skilled care and deliveries are not conducted in health facilities. We elaborate on this finding in light of the qualitative results and the broader literature in the general discussion section.

Study III: how did staff experience hospital resilience?

Following recommendations for health service research [ 38 ], we used a mixed deductive and inductive approach to analyze the data from the 18 interviews, with the starting themes from the 4 R resilience framework. The context of the interviews and characteristics of the interviewees are detailed elsewhere [ 39 ]. We categorized the burden to the hospital into material challenges, challenges to health service provision, challenges to management and coordination, and emotional and physical impact on individuals. Material challenges included shortages of medicines and of surgical and rehabilitation equipment. The high influx of injured victims created challenges to health service provision, as the capacity to treat trauma conditions was overwhelmed. Challenges to management and coordination occurred for a variety of reasons, but one aspect is that the earthquake occurred on a Saturday, senior staff were absent, and junior staff who were present were hence less likely to know the disaster plan. Individual staff experienced an increased workload in difficult conditions, while they were also concerned with their personal and family situations.

Ends of resilience

In terms of robustness, the hospital maximized capacity to provide emergency care, interrupting routine or elective activities. But questions regarding maintenance of quality of care arose, as well as concerns that patients were discouraged to travel for deliveries and other essential care.

During that time, we were not focusing on quality of care. (…) We had a lot of wound infections, we were not taking care of sterility properly … We just needed to provide care, we were focusing on life-saving and limb-saving activities.

We identified three stages of hospital rapidity. Critical rapidity was the time needed for the hospital to start essential work and assist injured victims while also self-organizing.

We tried to manage the pharmacy without a software system, but for two days, we failed. We were almost out of stock after two days. Then we started to ask for medicine supply from different agencies, from the government …

After this reorganization, stabilizing rapidity allowed the hospital to address earthquake-related surges in a new, stable rhythm, until routine activities restarted and the hospital reobtained a ‘normal look’.

(…) That made us feel like “ok we are back into function”: no patients treated on the ground, all patients treated in the wards.

After routine activities restarted, time was still needed to recover to a new, non-emergency phase and feeling. We found that recovery rapidity was subjective and person-specific, with many interviewees struggling to explain their experiences of ‘recovery’ – some even mentioned they were still recovering, and not recovered .

Means of Resilience

The hospital found suitable alternatives to many disrupted elements. An example of its redundancy is that it established linkages with ‘step-down centers’ to refer patients no longer requiring advanced hospital care, which liberated beds to accommodate severe cases.

Looking at resourcefulness, the pre-existing disaster plan and trainings were important, but many if not the majority of adaptations were spontaneous, compensating for a perceived lack of coordination. Many new partnerships or collaborations were established with external organizations, health services were rearranged, and staff changed their tasks or assumed new roles to adapt to emerging situations.

At a disaster time, everyone needs to know their job. But I did not know my job: the scenario drove me to that job.

Individual resilience

During our analysis, it became evident that hospital staff were essential to the resilience of the hospital as a whole. But the resilience of staff as individuals could not be analyzed in light of the 4 R framework, which is designed for systems. We identified three major determinants of hospital staff resilience: safety, meaningfulness, and sense of belonging. Feeling safe allowed staff to continue working despite recurrent aftershocks, and seemed to influence full recovery. Meaningfulness helped making sense of the tireless work, the putting family second, the constant fear. Interviewees who did not feel their experiences were meaningful were more often frustrated, or felt trapped in their work. In general, interviewees felt that family cohesiveness in Nepal was an important aspect, allowing them to leave their loved ones with extended families or with friends or neighbours. This contributed to cultivating a sense of belonging to a supportive community.

We were terrified, but we knew that we were safe in ICU because that building was safe.

After the second day I shifted my family to uncle’s house (…). They had like a family get-together. And I was free to work.

Earthquake impact

In our study, earthquake victim admissions were particularly long (mean = 14.7 days; median = 10) compared to studies in other hospitals, either in Nepal [ 40 ] (median = 8 days) or in China after the 2008 earthquake [ 41 ] (mean = 7 days). A study in Italy after the L’Aquila earthquake in 2009 found that the average length of hospital stay (LOS) of admitted earthquake victims was 12.11 days; LOS was significantly associated with age, in a sample where 57% of patients were older than 60 years [ 42 ]. In our sample, only 29% of patients were older than 50 years, and age was not associated with length of stay, suggesting a different cause for the long hospitalizations. Study II showed that in the three weeks after the earthquake, injury admissions were significantly longer than before the earthquake. This long LOS is probably related to the fact that we focused on a reference hospital that received more severe cases, and that people from remote districts reached the hospital with considerable delay, probably with more advanced conditions. Previous research on earthquake injuries found that length of hospital stay was associated with the level of resource use [ 41 ], suggesting that our study hospital was particularly strained during the earthquake response.

Was the hospital overwhelmed? Our studies show some nuances. The total number of admissions decreased in the six weeks after the earthquake. Pressure points were elsewhere, not reflected in hospital admissions. Reports show that a total of 1,723 injured victims were treated at TUTH [ 43 ], but less than a third were admitted. The cases that needed admission presented a specific profile: from a share of 11.1% of all admissions in the pre-earthquake period, injuries represented 38.5% of all admissions in the 3 weeks after the earthquake (aOR = 5.3, p < 0.001). The average length of stay also significantly increased during the same period, and mostly due to injury-related admissions, which were significantly longer. The majority of admitted earthquake victims required a surgical intervention (69%, n = 345), with many needing reinterventions. Staff reported that operation theatres were constantly occupied with earthquake-related surgeries, and new intensive care beds had to be set up. To deal with this sudden increase of demand for surgical care, non-urgent activities were put on hold. As explained in a conceptual model by von Schreeb et al., the need for hospital care due to injuries is concentrated in the days after a sudden-onset disaster, while other elective and less urgent conditions are deferred [ 44 ]. We lack information on which exact activities were cancelled or postponed, and we are hence unaware of which were time-sensitive conditions, like cancer surgical care. We can only assume that this was the case for all non-urgent care, which would then substantially aggravate the negative consequences of the earthquake. Compensating for this interrupted care I quite a complex endeavor. A study from the COVID-19 pandemic modeled that that clearing the backlog of elective surgical care after the first lockdown could last up to 45 weeks if the surgical volume increased by 20% [ 45 ].

A concerning finding from study II is the sustained decrease of pregnancy-related admissions. This was confirmed in Study III by perceptions of hospital staff, who believed this reduction was due to insufficient delivery beds and preference of pregnant women to use services closer to their residence. One study shows, after the earthquake, women in rural Nepal preferred to deliver at home rather than at a health facility, seriously challenging referral in case of complications [ 46 ]. This finding is not unique to the Nepal earthquake context, and there is evidence of reduced pregnancy-related admissions after disasters in other settings [ 47 ], or of worse maternal outcomes in general [ 48 , 49 ]. Possible explanations in the literature include reduced access to hospitals and health facilities [ 47 ], the death of many skilled attendants [ 49 ], and the lack of specific provisions for women and children in disaster plans [ 48 ].

Hospital function and resilience

Studies I and II attempted to measure the burden to the hospital and the changes in function, and were complemented by the qualitative information obtained in Study III. These findings can be put in perspective with the conceptual models proposed by von Schreeb et al. (2008) and Zhong et al. (2014) [ 7 , 44 ], and may contribute to future studies that attempt to measure hospital resilience, or the lack of it. Study III is one of the first to use the well-established 4 R system resilience framework as a starting point to explore mechanisms of hospital resilience in a post-disaster setting, as experienced by its staff. We captured a richness of experiences and complexity of events that the 4 R framework failed to reflect. For instance, the importance of emerging adaptations even when a disaster plan exists is not really featured in this framework. This can be a consequence of the fact that the majority of the literature, both empirical and theoretical, is actually generated from pre-disaster contexts. Although recent work highlights the need of ‘adaptive flexibility’ [ 17 ] or ‘adaptive capacity’ [ 50 ], these concepts remain vaguely defined in the scientific literature. Moreover, while previous studies have demonstrated the important role of staff experiences in hospital disaster response [ 51 , 52 ], ours is the first to identify individual resilience of hospital staff in the frontlines as an important contributor to hospital resilience. In our study, we identified three major determinants of hospital staff resilience: meaningfulness, sense of safety, and sense of belonging. The importance of staff feeling safe in hospital disaster response had already been identified in a study after Typhoon Haiyan [ 51 ]. In line with the literature, making sense of a difficult experience is important for hospital staff to believe efforts were worthwhile; and the safety recurrently mentioned by health and humanitarian workers in times of emergencies [ 53 ,54, 54 ].

Global implications

Several recommendations can be made for global disaster and hospital management practices. While important, structural resistance alone is not sufficient to ensure resilience of health services; functional aspects, if not well managed, can create major bottlenecks.

Earthquake-prone countries should have strategies that ensure sufficient equipment to treat high numbers of orthopaedic injuries, and that strengthen surgical capacity in peripheral services. To achieve this, diplomatic agreements with neighbouring countries may be required in order to improve efficiency. If able, tertiary hospitals should provide advanced care to disaster victims, but disruption of non-earthquake specialized care should be minimized.

After any type of disaster, the population and frontline workers experience great levels of suffering and stress that can seriously impact their mental health and ability to reach their full potential in the future [ 55 ]. Hospital disaster plans should have specific provisions to ensure appropriate and skilled support to their own staff. Strategies that contribute to staff wellbeing in times of disaster response also increase hospital resilience. This could be leveraged in international initiatives such as the ‘Hospitals Safe from Disasters’ Campaign [ 56 ] or the HOPE network [ 29 ].

Finally, the perspectives from different stakeholders should be fed into hospital disaster plans when they are being designed. This can greatly increase adherence to such plans and their effectiveness.

Relevance for the COVID-19 pandemic

As the results of this work were being finalized, the world was hit by a pandemic disease caused by a novel coronavirus. As of early March 2021, more than 116 million people have been infected with this virus, and nearly 2.6 million have died from the novel coronavirus disease (COVID-19) [ 57 ]. Hospital and health system resilience became extremely relevant as we witnessed the collapse of critical care facilities and the prolonged interruption of non-emergency care, in rich and poor settings. It became evident that health facilities cannot face crises without engaging and collaborating with other actors in health systems, highlighting the complexity of health system and health service resilience. The pandemic also emphasized that crisis response plans could be obsolete if there is no adequate follow-up, and institutions must adapt as the situation evolves.

Societies also became more aware of the important role of frontline workers such as hospital staff during crisis response. As shown in our study, their resilience is critical for the resilience of a health service and a society as a whole. In the first wave occurring in early 2020, adequate personal protective equipment was lacking, putting staff at great risk of acquiring a severe infection, and transmitting it to their household members. This is a serious challenge to staff safety [ 54 ], and influences their ability to work, even if unconsciously. But during this period, countless global movements of solidarity were occurring, celebrating the courage of healthcare workers and valuing their work, ultimately contributing to a sense of belonging. However, as the situation is getting more and more protracted with recurrent waves of infection, ‘pandemic fatigue’, or ignoring preventive measures, is a dangerous threat to resilience of healthcare workers. The lack of collaboration from the general public can make staff feel their efforts are in vain and that the community is overlooking their needs.

Conclusions

Our findings empirically support conceptual models of disaster impact on hospital care and show concrete, measurable changes in hospital function. This is the first research work to further explore the suitability of the 4 R framework on hospital disaster resilience through empirical post-disaster data collection and analysis. We argue that resilience is only evident after a disaster occurs, when unplanned adaptations emerge and individual staff face unique challenges. We recommend additional case-studies to quantify the short and long-term impacts of different disaster types on hospitals in different contexts, and to identify the main concepts that can be measured and used to predict resilience before a disaster happens. By producing evidence from different events and contexts, we will be able to differentiate contextual factors that influence resilience from other factors that are more easily modifiable. These could be further elaborated through the co-construction of a hospital resilience assessment tool where diverse stakeholders are engaged.

Acknowledgments

I am thankful to my supervisors and to my colleagues in Nepal: Prof. Debarati Guha-Sapir Prof. Isabelle Aujoulat, Prof. Deepak Prakash Mahara, Dr. Sunil Singh Thapa, and Mr. KC Kumar. I would like to acknowledge the members of my steering committee, who followed my work over three and a half years, and the opponents of my dissertation, Prof. Johan von Schreeb (Karolinska Institutet) and Prof. Ali Ardalan (Tehran University of Medical Sciences and WHO/EMRO). Finally, I am grateful to all my co-authors and to the hospital staff who participated in the interviews and who assisted in the data collection.

Maria Moitinho de Almeida wrote this PhD review article based on key findings from her dissertation and from three articles that she wrote as first-author.

Funding Statement

This PhD thesis was supported by the USAID/OFDA, the Special Research Funds of UCLouvain, the Horlait-Dapsens medical foundation, and the Education, Audiovisual, Culture Executive Agency.

Responsible Editor

Julia Schrders

Disclosure statement

The author declares that she does not have any competing interest.

Ethics and consent

All of the studies from this dissertation were approved by the Institutional Review Committee of the Tribhuvan University’s Institute of Medicine, Kathmandu, Nepal. An informed consent was not necessary for Studies I and II as they used secondary data. Written informed consents were obtained from participants in Study III.

Paper context

Resilient hospitals are essential to reduce the health consequences of disasters, but few studies examine the disaster impact on hospitals, and research on hospital resilience is mostly from pre-disaster conceptualizations. This article shows the complex and nuanced impact of a high-magnitude earthquake on a tertiary hospital in Nepal, and documents the resilience of the hospital as experienced in the frontlines.

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Haiti Earthquake 2010

Haiti earthquake case study.

A 7.0 magnitude earthquake .

The earthquake occurred on January 12th, 2010, at 16.53 local time (21.53 GMT).

The earthquake occurred at 18.457°N, 72.533°W. The epicentre was near the town of Léogâne, Ouest department, approximately 25 kilometres (16 mi) west of Port-au-Prince, Haiti’s capital. The earthquake’s focus was 13km (8.1 miles) below the Earth’s surface.

Haiti is situated at the northern end of the Caribbean Plate, on a transform (slip/conservative) plate boundary with the North American Plate. The North American plate is moving west. This movement is not smooth, and there is friction between the North American Plate and the Caribbean Plate. Pressure builds between the two plates until released as an earthquake.

A map to show the location of Haiti in relation to tectonic plates. Source BBC.

The epicentre of the earthquake was 16km southwest of Port-Au-Prince. The earthquake was caused by a slip along an existing fault (Enriquillo-Plaintain Garden fault).

A map to show the location of the epicentre of the earthquake

Primary Effects

As of February 12th 2010, an estimated three million people were affected by the quake; the Haitian Government reports that between 217,000 and 230,000 people died, an estimated 300,000 were injured, and an estimated 1,000,000 were made homeless. They also estimated that 250,000 residences and 30,000 commercial buildings had collapsed or were severely damaged.

Secondary Effects

• Two million people were left without water and food.
• Regular power cuts occurred.
• Crime increased – looting became a problem and sexual violence escalated.
• People moved into temporary shelters.
• By November 2010 there were outbreaks of cholera.

Immediate Responses

• Due to the port being damaged, aid was slow to arrive.
• The USA sent rescue teams and 10,000 troops.
• Bottled water and purification tablets were provided.
• 235,000 people were moved away from Port-au-Prince to less-damaged cities.
• £20 million was donated by The UK government.

Long-term Responses

• As one of the poorest countries on Earth, Haiti relied on overseas aid.
• Although the response was slow, new homes were built to a higher standard. Over one million people still lived in temporary shelters one year after the earthquake.
• The port needed rebuilding, which required a large amount of investment.

So, why did so many people die in the Haiti earthquake? There are several reasons for this:

• The earthquake occurred at shallow depth – this means that the seismic waves must travel a smaller distance through the Earth to reach the surface to maintain more energy.
• The earthquake struck the most densely populated area of the country.
• Haiti is the poorest country in the Western Hemisphere
• The buildings in Port-Au-Prince and other areas of Haiti were generally in poor condition and were not designed or constructed to be earthquake-resistant.
• Three million people live in Port au Prince; most live in slum conditions after rapid urbanisation.
• Haiti only has one airport with one runway. The control tower was severely damaged in the earthquake. The port is also unusable due to damage.
• Initially, aid had been piling up at the airport due to a lack of trucks and people to distribute it. Water and food have taken days to arrive, and there is not enough to go around.
• Rescue teams from around the world took up to 48 hours to arrive in Haiti due to the problems at the airport. As a result, local people have had to use their bare hands to try and dig people out of the rubble.
• There has been a severe shortage of doctors, and many people have died of injuries like broken limbs.

 The BBC News website has a comprehensive overview of the earthquake here . In addition, the BBC has produced an excellent article titled Why so many people died in the Haiti earthquake? and provides comparative data with similar earthquakes.

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Bhuj Earthquake India 2001 – A Complete Study

Bhuj earthquake india.

Gujarat : Disaster on a day of celebration : 51st Republic Day on January 26, 2001

• 7.9 on the Richter scale.
• 8.46 AM January 26th 2001
• 20,800 dead

Basic Facts

• Earthquake: 8:46am on January 26, 2001
• Epicenter: Near Bhuj in Gujarat, India
• Magnitude: 7.9 on the Richter Scale

Geologic Setting

• Indian Plate Sub ducting beneath Eurasian Plate
• Continental Drift
• Convergent Boundary

Specifics of 2001 Quake

Compression Stress between region’s faults

Depth: 16km

Probable Fault: Kachchh Mainland

Fault Type: Reverse Dip-Slip (Thrust Fault)

The earthquake’s epicentre was 20km from Bhuj. A city with a population of 140,000 in 2001. The city is in the region known as the Kutch region. The effects of the earthquake were also felt on the north side of the Pakistan border, in Pakistan 18 people were killed.

Tectonic systems

The earthquake was caused at the convergent plate boundary between the Indian plate and the Eurasian plate boundary. These pushed together and caused the earthquake. However as Bhuj is in an intraplate zone, the earthquake was not expected, this is one of the reasons so many buildings were destroyed – because people did not build to earthquake resistant standards in an area earthquakes were not thought to occur. In addition the Gujarat earthquake is an excellent example of liquefaction, causing buildings to ‘sink’ into the ground which gains a consistency of a liquid due to the frequency of the earthquake.

India : Vulnerability to earthquakes

• 56% of the total area of the Indian Republic is vulnerable to seismic activity .
• 12% of the area comes under Zone V (A&N Islands, Bihar, Gujarat, Himachal Pradesh, J&K, N.E.States, Uttaranchal)
• 18% area in Zone IV (Bihar, Delhi, Gujarat, Haryana, Himachal Pradesh, J&K, Lakshadweep, Maharashtra, Punjab, Sikkim, Uttaranchal, W. Bengal)
• 26% area in Zone III (Andhra Pradesh, Bihar, Goa, Gujarat, Haryana, Kerala, Maharashtra, Orissa, Punjab, Rajasthan, Tamil Nadu, Uttaranchal, W. Bengal)
• Gujarat: an advanced state on the west coast of India.
• On 26 January 2001, an earthquake struck the Kutch district of Gujarat at 8.46 am.
• Epicentre 20 km North East of Bhuj, the headquarter of Kutch.
• The Indian Meteorological Department estimated the intensity of the earthquake at 6.9 Richter. According to the US Geological Survey, the intensity of the quake was 7.7 Richter.
• The quake was the worst in India in the last 180 years.

What earthquakes do

• Casualties: loss of life and injury.
• Loss of housing.
• Damage to infrastructure.
• Disruption of transport and communications.
• Breakdown of social order.
• Loss of industrial output.
• Loss of business.
• Disruption of marketing systems.
• The earthquake devastated Kutch. Practically all buildings and structures of Kutch were brought down.
• Ahmedabad, Rajkot, Jamnagar, Surendaranagar and Patan were heavily damaged.
• Nearly 19,000 people died. Kutch alone reported more than 17,000 deaths.
• 1.66 lakh people were injured. Most were handicapped for the rest of their lives.
• The dead included 7,065 children (0-14 years) and 9,110 women.
• There were 348 orphans and 826 widows.

Loss classification

Deaths and injuries: demographics and labour markets

Effects on assets and GDP

Effects on fiscal accounts

Financial markets

Disaster loss

• Initial estimate Rs. 200 billion.
• Came down to Rs. 144 billion.
• No inventory of buildings
• Non-engineered buildings
• Land and buildings
• Stocks and flows
• Reconstruction costs (Rs. 106 billion) and loss estimates (Rs. 99 billion) are different
• Public good considerations

Human Impact: Tertiary effects

• Affected 15.9 million people out of 37.8 in the region (in areas such as Bhuj, Bhachau, Anjar, Ganhidham, Rapar)
• High demand for food, water, and medical care for survivors
• Humanitarian intervention by groups such as Oxfam: focused on Immediate response and then rehabilitation
• Of survivors, many require persistent medical attention
• Region continues to require assistance long after quake has subsided
• International aid vital to recovery

Social Impacts

• 80% of water and food sources were destroyed.
• The obvious social impacts are that around 20,000 people were killed and near 200,000 were injured.
• However at the same time, looting and violence occurred following the quake, and this affected many people too.
• On the other hand, the earthquake resulted in millions of USD in aid, which has since allowed the Bhuj region to rebuild itself and then grow in a way it wouldn’t have done otherwise.
• The final major social effect was that around 400,000 Indian homes were destroyed resulting in around 2 million people being made homeless immediately following the quake.

Social security and insurance

• Ex gratia payment: death relief and monetary benefits to the injured
• Major and minor injuries
•  Cash doles
• Government insurance fund
• Group insurance schemes
• Claim ratio

Demographics and labour market

• Geographic pattern of ground motion, spatial array of population and properties at risk, and their risk vulnerabilities.
• Low population density was a saving grace.
• Extra fatalities among women
• Effect on dependency ratio
• Farming and textiles

Economic  Impacts

• Total damage estimated at around $7 billion. However $18 billion of aid was invested in the Bhuj area.
• Over 15km of tarmac road networks were completely destroyed.
• In the economic capital of the Gujarat region, Ahmedabad, 58 multi storey buildings were destroyed, these buildings contained many of the businesses which were generating the wealth of the region.
• Many schools were destroyed and the literacy rate of the Gujarat region is now the lowest outside southern India.

Impact on GDP

• Applying ICOR
• Rs. 99 billion – deduct a third as loss of current value added.
• Get GDP loss as Rs. 23 billion
• Adjust for heterogeneous capital, excess capacity, loss Rs. 20 billion.
• Reconstruction efforts.
• Likely to have been Rs. 15 billion.

Fiscal accounts

• Differentiate among different taxes: sales tax, stamp duties and registration fees, motor vehicle tax, electricity duty, entertainment tax, profession tax, state excise and other taxes. Shortfall of Rs. 9 billion of which about Rs. 6 billion unconnected with earthquake.
• Earthquake related other flows.
• Expenditure:Rs. 8 billion on relief. Rs. 87 billion on rehabilitation.

Impact on Revenue Continue Reading

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Privacy Overview

What to know about the crisis of violence, politics and hunger engulfing Haiti

A long-simmering crisis over Haiti’s ability to govern itself, particularly after a series of natural disasters and an increasingly dire humanitarian emergency, has come to a head in the Caribbean nation, as its de facto president remains stranded in Puerto Rico and its people starve and live in fear of rampant violence. 

The chaos engulfing the country has been bubbling for more than a year, only for it to spill over on the global stage on Monday night, as Haiti’s unpopular prime minister, Ariel Henry, agreed to resign once a transitional government is brokered by other Caribbean nations and parties, including the U.S.

But the very idea of a transitional government brokered not by Haitians but by outsiders is one of the main reasons Haiti, a nation of 11 million, is on the brink, according to humanitarian workers and residents who have called for Haitian-led solutions. 

“What we’re seeing in Haiti has been building since the 2010 earthquake,” said Greg Beckett, an associate professor of anthropology at Western University in Canada. 

What is happening in Haiti and why?

In the power vacuum that followed the assassination of democratically elected President Jovenel Moïse in 2021, Henry, who was prime minister under Moïse, assumed power, with the support of several nations, including the U.S. 

When Haiti failed to hold elections multiple times — Henry said it was due to logistical problems or violence — protests rang out against him. By the time Henry announced last year that elections would be postponed again, to 2025, armed groups that were already active in Port-au-Prince, the capital, dialed up the violence.

Even before Moïse’s assassination, these militias and armed groups existed alongside politicians who used them to do their bidding, including everything from intimidating the opposition to collecting votes . With the dwindling of the country’s elected officials, though, many of these rebel forces have engaged in excessively violent acts, and have taken control of at least 80% of the capital, according to a United Nations estimate. 

Those groups, which include paramilitary and former police officers who pose as community leaders, have been responsible for the increase in killings, kidnappings and rapes since Moïse’s death, according to the Uppsala Conflict Data Program at Uppsala University in Sweden. According to a report from the U.N . released in January, more than 8,400 people were killed, injured or kidnapped in 2023, an increase of 122% increase from 2022.

“January and February have been the most violent months in the recent crisis, with thousands of people killed, or injured, or raped,” Beckett said.

Armed groups who had been calling for Henry’s resignation have already attacked airports, police stations, sea ports, the Central Bank and the country’s national soccer stadium. The situation reached critical mass earlier this month when the country’s two main prisons were raided , leading to the escape of about 4,000 prisoners. The beleaguered government called a 72-hour state of emergency, including a night-time curfew — but its authority had evaporated by then.

Aside from human-made catastrophes, Haiti still has not fully recovered from the devastating earthquake in 2010 that killed about 220,000 people and left 1.5 million homeless, many of them living in poorly built and exposed housing. More earthquakes, hurricanes and floods have followed, exacerbating efforts to rebuild infrastructure and a sense of national unity.

Since the earthquake, “there have been groups in Haiti trying to control that reconstruction process and the funding, the billions of dollars coming into the country to rebuild it,” said Beckett, who specializes in the Caribbean, particularly Haiti. 

Beckett said that control initially came from politicians and subsequently from armed groups supported by those politicians. Political “parties that controlled the government used the government for corruption to steal that money. We’re seeing the fallout from that.”

Many armed groups have formed in recent years claiming to be community groups carrying out essential work in underprivileged neighborhoods, but they have instead been accused of violence, even murder . One of the two main groups, G-9, is led by a former elite police officer, Jimmy Chérizier — also known as “Barbecue” — who has become the public face of the unrest and claimed credit for various attacks on public institutions. He has openly called for Henry to step down and called his campaign an “armed revolution.”

But caught in the crossfire are the residents of Haiti. In just one week, 15,000 people have been displaced from Port-au-Prince, according to a U.N. estimate. But people have been trying to flee the capital for well over a year, with one woman telling NBC News that she is currently hiding in a church with her three children and another family with eight children. The U.N. said about 160,000 people have left Port-au-Prince because of the swell of violence in the last several months. 

Deep poverty and famine are also a serious danger. Gangs have cut off access to the country’s largest port, Autorité Portuaire Nationale, and food could soon become scarce.

Haiti's uncertain future

A new transitional government may dismay the Haitians and their supporters who call for Haitian-led solutions to the crisis. 

But the creation of such a government would come after years of democratic disruption and the crumbling of Haiti’s political leadership. The country hasn’t held an election in eight years. 

Haitian advocates and scholars like Jemima Pierre, a professor at the University of British Columbia, Vancouver, say foreign intervention, including from the U.S., is partially to blame for Haiti’s turmoil. The U.S. has routinely sent thousands of troops to Haiti , intervened in its government and supported unpopular leaders like Henry.

“What you have over the last 20 years is the consistent dismantling of the Haitian state,” Pierre said. “What intervention means for Haiti, what it has always meant, is death and destruction.”

In fact, the country’s situation was so dire that Henry was forced to travel abroad in the hope of securing a U.N. peacekeeping deal. He went to Kenya, which agreed to send 1,000 troops to coordinate an East African and U.N.-backed alliance to help restore order in Haiti, but the plan is now on hold . Kenya agreed last October to send a U.N.-sanctioned security force to Haiti, but Kenya’s courts decided it was unconstitutional. The result has been Haiti fending for itself. 

“A force like Kenya, they don’t speak Kreyòl, they don’t speak French,” Pierre said. “The Kenyan police are known for human rights abuses . So what does it tell us as Haitians that the only thing that you see that we deserve are not schools, not reparations for the cholera the U.N. brought , but more military with the mandate to use all kinds of force on our population? That is unacceptable.”  

Henry was forced to announce his planned resignation from Puerto Rico, as threats of violence — and armed groups taking over the airports — have prevented him from returning to his country.  

Now that Henry is to stand down, it is far from clear what the armed groups will do or demand next, aside from the right to govern. 

“It’s the Haitian people who know what they’re going through. It’s the Haitian people who are going to take destiny into their own hands. Haitian people will choose who will govern them,” Chérizier said recently, according to The Associated Press .

Haitians and their supporters have put forth their own solutions over the years, holding that foreign intervention routinely ignores the voices and desires of Haitians. 

In 2021, both Haitian and non-Haitian church leaders, women’s rights groups, lawyers, humanitarian workers, the Voodoo Sector and more created the Commission to Search for a Haitian Solution to the Crisis . The commission has proposed the “ Montana Accord ,” outlining a two-year interim government with oversight committees tasked with restoring order, eradicating corruption and establishing fair elections. 

For more from NBC BLK, sign up for our weekly newsletter .

CORRECTION (March 15, 2024, 9:58 a.m. ET): An earlier version of this article misstated which university Jemima Pierre is affiliated with. She is a professor at the University of British Columbia, Vancouver, not the University of California, Los Angeles, (or Columbia University, as an earlier correction misstated).

Patrick Smith is a London-based editor and reporter for NBC News Digital.

Char Adams is a reporter for NBC BLK who writes about race.

Buckle up for likely earthquake aftershocks, geologists say. Here's how the East Coast should prepare.

• Geologists say the East Coast could be in for more earthquakes in the weeks ahead.
• There's also a slim chance of aftershocks with a similar or larger magnitude, the USGS said.
• To prepare, you should create an emergency plan, secure household items, and assemble a bugout bag.

Aftershocks could hit the East Coast following Friday's 4.8 magnitude earthquake, and millions of people in the region should prepare in the unlikely event the earthquake is bigger next time, the US Geological Survey said.

"There is a likely chance that there will be more felt earthquakes in the two or three range, and then a small chance that there could be another earthquake of similar or larger magnitude," Paul Earle, a seismologist at the USGS National Earthquake Information Center, told reporters on Friday.

"You just need to be prepared," Earle added.

Already, a magnitude 4.0 aftershock hit just before 6 p.m. ET, USGS confirmed Friday.

Don't stand inside a doorway when an earthquake hits. It turns out that's just a survival myth .

Instead, you should drop where you are, cover your head and neck, ideally crawl under a table, and hold on until the shaking stops.

Possible aftershocks could be damaging.

Related stories

"As a reminder, damaging earthquakes can occur in the future," Jessica Jobe, a research geologist with the USGS Earthquake Hazards Program, said on Friday. "And no one can predict the exact time or place of any earthquake, including the aftershocks."

One of the most important things you can do to prepare, Earle said, is to create an emergency plan.

He said to be sure to move objects on shelves or walls that can fall on you, especially while you're sleeping. And, he added, have a plan for contacting your relatives in case of an emergency.

When making your emergency plan with members of your household, you should map out your evacuation route, coordinate who will retrieve emergency supplies and where they will be located, and devise a reconnection plan if you are separated, Business Insider previously reported .

Another great step to prepare for an earthquake — or any emergency — is to assemble a "bugout bag" that should include things like a first aid kit, an emergency radio, flashlights, medicine, and a gas and water shutoff tool.

In the event of a major quake, you should also keep at least one gallon of water per person on hand in case you become trapped in your home and lose access to your home's water supply.

Having a fire extinguisher in your home and enough non-perishable food to last a few days is also valuable for emergency situations.

The 4.8 earthquake that hit Friday was centered in New Jersey, about 30 miles west of Newark, but its effects were felt as far away as Boston, Philadelphia, and Maine.

The USGS said it's uncommon to have earthquakes in the region, but not unexpected.

It was the third-biggest quake ever recorded in New Jersey and the largest in the state in nearly 250 years, according to a 2019 earthquake mitigation report.

As of Friday afternoon, authorities hadn't reported any injuries or major structural damage caused by the quake.

Watch: New York City resident reacts to 4.8 magnitude earthquake

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