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Title:

COPEWELL: A conceptual framework and system dynamics model for predicting community functioning and resilience after disasters

Authors:

Jonathan M. Links, Brian S. Schwartz, Sen Lin, Norma Kanarek, Judith Mitrani-Reiser, Tara Kirk Sell, Crystal R. Watson, Doug Ward, Cathy Slemp, Robert Burhans, Kimberly Gill, Tak Igusa, Xilei Zhao, Benigno Aguirre, Joseph Trainor, Joanne Nigg, Thomas Inglesby, Eric Carbone and James M. Kendra

Date posted:

June 21, 2017

Publication type:

Article

Publication:

Disaster Medicine and Public Health Preparedness

Publisher:

Society for Disaster Medicine and Public Health, Inc.

DOI:

https://doi.org/10.1017/dmp.2017.39

Availability:

Available on Publisher Website

See also:

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Introduction:

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.

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.

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.

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.