Health System Resilience Modeling to Support Post-Disaster Recovery and Future Crisis Preparedness Planning

Health systems are frequently challenged by natural disasters, pandemics, and humanitarian crises, which disrupt service delivery, compromise infrastructure, and exacerbate population health vulnerabilities. Ensuring continuity of care and rapid recovery in such contexts requires a systematic understanding of health system resilience—the capacity to absorb shocks, adapt to changing conditions, and transform in response to crises. This presents a conceptual framework for modeling health system resilience to support post-disaster recovery and future preparedness planning. The framework integrates operational, demographic, epidemiological, and environmental data to map critical health system components, including facilities, supply chains, workforce, and utilities, and to identify potential points of vulnerability. By simulating stress scenarios and projecting service disruptions, the model provides actionable insights for optimizing resource allocation, maintaining essential services, and reducing morbidity and mortality during and after crises. Key metrics for resilience assessment include service continuity, recovery time, and health outcomes under varying disaster intensities. The framework emphasizes the use of real-time surveillance data and Internet of Things (IoT)-enabled monitoring to enable dynamic adjustments and evidence-informed decision-making. Additionally, it incorporates scenario planning to anticipate multi-hazard events and to evaluate the impact of interventions on system robustness and responsiveness. Ethical considerations, including equitable prioritization of resources and protection of vulnerable populations, are embedded in the modeling process to ensure that recommendations are socially responsible and contextually appropriate. By providing a structured approach to resilience assessment, the framework aims to support policymakers, health planners, and emergency response teams in strengthening health system preparedness, enhancing adaptive capacity, and facilitating rapid recovery post-disaster. Ultimately, the application of data-driven resilience modeling can inform strategic investments, guide multi-sectoral coordination, and foster sustainable improvements in health system performance, ensuring that populations are better protected against the increasing frequency and intensity of public health emergencies.