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

Published online by Cambridge University Press:  21 June 2017

Jonathan M. Links*
Affiliation:
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland Center for Public Health Preparedness, Johns Hopkins University, Baltimore, Maryland
Brian S. Schwartz
Affiliation:
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
Sen Lin
Affiliation:
Department of Civil Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland
Norma Kanarek
Affiliation:
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
Judith Mitrani-Reiser
Affiliation:
Department of Civil Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland
Tara Kirk Sell
Affiliation:
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland Center for Health Security, Johns Hopkins University, Baltimore, Maryland
Crystal R. Watson
Affiliation:
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland Center for Health Security, Johns Hopkins University, Baltimore, Maryland
Doug Ward
Affiliation:
Division of Public Safety Leadership, Johns Hopkins School of Education, Baltimore, Maryland
Cathy Slemp
Affiliation:
Independent Consultants
Robert Burhans
Affiliation:
Independent Consultants
Kimberly Gill
Affiliation:
Disaster Research Center, University of Delaware, Newark, Delaware
Tak Igusa
Affiliation:
Department of Civil Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland
Xilei Zhao
Affiliation:
Department of Civil Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland
Benigno Aguirre
Affiliation:
Disaster Research Center, University of Delaware, Newark, Delaware
Joseph Trainor
Affiliation:
Disaster Research Center, University of Delaware, Newark, Delaware
Joanne Nigg
Affiliation:
Disaster Research Center, University of Delaware, Newark, Delaware
Thomas Inglesby
Affiliation:
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland Center for Health Security, Johns Hopkins University, Baltimore, Maryland
Eric Carbone
Affiliation:
Office of Public Health Preparedness and Response, Centers for Disease Control and Prevention, Atlanta, Georgia
James M. Kendra
Affiliation:
Disaster Research Center, University of Delaware, Newark, Delaware
*
Correspondence and reprint requests to Jonathan M. Links, Johns Hopkins University, 258 Garland Hall, 3400 N Charles St, Baltimore, MD 21218 (e-mail: jlinks1@jhu.edu).

Abstract

Objective

Policy-makers and practitioners have a need to assess community resilience in disasters. Prior efforts conflated resilience with community functioning, combined resistance and recovery (the components of resilience), and relied on a static model for what is inherently a dynamic process. We sought to develop linked conceptual and computational models of community functioning and resilience after a disaster.

Methods

We developed a system dynamics computational model that predicts community functioning after a disaster. The computational model outputted the time course of community functioning before, during, and after a disaster, which was used to calculate resistance, recovery, and resilience for all US counties.

Results

The conceptual model explicitly separated resilience from community functioning and identified all key components for each, which were translated into a system dynamics computational model with connections and feedbacks. The components were represented by publicly available measures at the county level. Baseline community functioning, resistance, recovery, and resilience evidenced a range of values and geographic clustering, consistent with hypotheses based on the disaster literature.

Conclusions

The work is transparent, motivates ongoing refinements, and identifies areas for improved measurements. After validation, such a model can be used to identify effective investments to enhance community resilience. (Disaster Med Public Health Preparedness. 2018;12:127–137)

Type
Concepts in Disaster Medicine
Copyright
Copyright © Society for Disaster Medicine and Public Health, Inc. 2017 

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