Earthquakes have long been a specter of destruction, often arriving without warning and leaving chaos in their wake. The traditional approach to earthquake engineering has focused heavily on building resilient infrastructures, a method essential yet insufficient when countless lives are at stake. This realization has inspired a ground-breaking concept known as the 'Earthquake Survival Chain.' By shifting the focus from mere infrastructure to human life and health, researchers aim to transform disaster response.
The core idea of the Earthquake Survival Chain is to treat earthquake response as a sequence of interconnected lifelines that can improve the chances of survival and recovery. This perspective emerged from field investigations and interdisciplinary efforts, blending engineering and medical insights to reevaluate how we prepare for and react to seismic events.
Historically, earthquake engineering has significantly improved building standards. After the 1971 San Fernando Earthquake in California, extensive damage to concrete structures prompted new code provisions in the following decades. During the 1994 Northridge Earthquake, brittle damage in steel frame connections led to more robust standards in the 1997 Uniform Building Code. Despite these advancements, the 21st century has witnessed catastrophic earthquakes in India, Turkey, and Haiti, injuring hundreds of thousands and emphasizing the need for a broader approach beyond structural integrity.
This traditional approach has predominantly benefited wealthier regions like North America, which can afford to renew their infrastructure over time. However, retrofit programs are rare and insufficient in many parts of Asia and South America, where rapid urbanization has led to many seismically vulnerable homes. The focus must extend beyond making buildings resilient to encompass comprehensive emergency responses that prioritize human life.
The Earthquake Survival Chain framework comprises various stages of emergency response, similar to the American Heart Association's Chain of Survival for cardiac arrest victims. The initial stage involves search and rescue operations, a race against time to locate and extricate victims from collapsed structures. Victims often sustain orthopedic injuries or crush syndrome from being trapped under rubble, and their survival largely depends on swift action.
Following search and rescue, patients must be mobilized to hospitals, a phase fraught with logistical challenges. Earthquakes frequently disrupt transportation infrastructure, creating bottlenecks and delays in patient transport. For instance, after the 2023 Türkiye earthquake, debris from collapsed buildings in Hatay blocked roads, complicating the movement of emergency vehicles. The rapid influx of injured people to the few functioning hospitals exacerbated traffic congestion, further delaying critical medical care.
Once patients reach medical facilities, providing timely and appropriate medical treatment is crucial. Earthquake patients require diverse medical resources depending on their conditions—crush syndrome victims may need hemodialysis, while those with fractures require surgical intervention. However, post-earthquake observations have shown that the functionality of hospitals relies heavily not just on their structural integrity but also on human factors such as risk perception and adaptability among medical staff.
Doctors and nurses must make quick decisions about whether to evacuate or continue operations amid uncertain conditions. IVR (Immersive Virtual Reality) environments could significantly enhance training for these scenarios by simulating different types of structural damage and helping medical personnel practice their response strategies. For instance, after the 2023 Türkiye earthquake, Mustafa Kemal University Hospital evacuated its main building despite its structural integrity being reasonably intact, highlighting the vital role of risk perception in decision-making during crises.
Addressing the complex dynamics of earthquake damage, search and rescue efforts, and medical emergencies requires cutting-edge technology and methodologies. Remote sensing, satellite imagery, and AI have revolutionized disaster response capabilities. High-resolution satellite and UAV (Unmanned Aerial Vehicle) imagery allow researchers to assess the extent of damage remotely and predict building collapses and road disruptions through advanced AI algorithms. However, a wealth of data is needed to train these models accurately, making the establishment of comprehensive datasets paramount for effective disaster management.
The challenges extend to understanding how different types of infrastructure failures impact mobility. Datasets such as Call Detail Records (CDRs) and smartphone location data offer unprecedented insights. These datasets can reveal hyper-local mobility patterns, helping emergency responders to understand how people move and where bottlenecks occur during disaster scenarios. This information can improve the planning and coordination of patient transport and emergency responses.
Importantly, AI can vastly enhance the efficiency of search and rescue operations. AI-powered robots can navigate and search large areas more efficiently for trapped individuals, and close-range sensors can detect structural instabilities that might endanger both victims and rescuers under the rubble. Similarly, AI applications in patient mobilization can manage logistical challenges, helping find optimal routes for emergency vehicles and avoid congested areas.
But technology alone isn’t enough; a nuanced understanding of injuries and medical needs resulting from earthquakes is essential. Traditional casualty models, which use generalized data about building collapses and injuries, lack the resolution to effectively allocate medical resources. Researchers emphasize the importance of developing more granular models that account for different kinds of injuries and their specific medical requirements. This could improve the ability to provide appropriate medical care right after disasters.
One significant deficiency in earthquake casualty assessments is their reliance on uniform risk estimates across entire buildings, ignoring that injuries usually occur where specific structural failures happen. For instance, buildings with higher collapse volume ratios see more severe injuries, but current models do not account for this variability. Enhancing these models requires incorporating new data collection methods, such as drones and high-resolution satellite imagery, which can capture detailed structural failures.
The establishment of non-binary damage indicators—shifting from a simple 'damaged versus undamaged' approach to a graded scale of structural integrity—offers a more precise way to predict casualties. This comprehensive understanding of structural failures could significantly improve search and rescue efforts and casualty estimations.
From gathering real-time mobility data to leveraging AI for damage assessment and casualty predictions, the Earthquake Survival Chain seeks to redefine the paradigm of disaster response. Its interdisciplinary approach calls for collaborations across the fields of engineering, medicine, AI, and emergency management. By focusing on the interconnected lifelines that sustain human life in the aftermath of earthquakes, the framework strives to enhance not just infrastructure resilience but human resilience.
The broader implications of this shift are vast. It involves a rethinking of public policies, urban planning, and emergency protocols to create communities that can quickly recover from disasters. By integrating advanced technologies with human-centered design, the Earthquake Survival Chain aims to provide a holistic, effective response to one of nature's most terrifying phenomena. As Ceferino and colleagues note, this approach “seeks to augment victims' survivability and rapidly stabilize communities’ health, especially after large earthquakes”.
