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COPEWELL: A Conceptual Framework and System Dynamics Model for Predicting Community Functioning and Resilience After

Jonathan M Links¹, Brian S Schwartz¹, Sen Lin²

¹1Department of Environmental Health and Engineering,Johns Hopkins Bloomberg School of Public Health,Baltimore,Maryland.

Disaster Medicine and Public Health Preparedness

|June 22, 2017

Summary

This summary is machine-generated.

This study developed a dynamic model to assess community resilience after disasters, separating it from community functioning. The model can guide investments to enhance disaster preparedness and recovery.

Keywords:

community functioning resilience system dynamics

Related Experiment Videos

Area of Science:

Disaster science
Public health preparedness
Community resilience

Background:

Assessing community resilience to disasters is crucial for policymakers and practitioners.
Previous resilience models conflated resilience with community functioning and used static approaches for a dynamic process.
There is a need for a dynamic model that accurately separates and measures community resilience components.

Purpose of the Study:

To develop linked conceptual and computational models of community functioning and resilience after a disaster.
To create a system dynamics model that predicts community functioning over time.
To calculate resistance, recovery, and resilience for all US counties.

Main Methods:

Developed a system dynamics computational model to predict community functioning post-disaster.
Separated conceptual components of resilience and community functioning.
Utilized publicly available county-level measures for model components.

Main Results:

The conceptual model distinguished resilience from community functioning, identifying key components and their interconnections.
The system dynamics model generated time-course data for community functioning, resistance, recovery, and resilience.
Geographic clustering and varied values for resilience metrics were observed across US counties.

Conclusions:

The developed model offers a transparent and dynamic approach to understanding community resilience.
The model can be refined with improved measurements and validated for practical application.
This tool can inform targeted investments to enhance community resilience and disaster preparedness.