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

TitleCOPEWELL: A Conceptual Framework and System Dynamics Model for Predicting Community Functioning and Resilience after Disasters
Publication TypeJournal Article
Year of Publication2018
AuthorsLinks, JM, Schwartz, BS, Lin, S, Kanarek, N, Mitrani-Reiser, J, Sell, TK, Watson, CR, Ward, D, Slemp, C, Burhans, R, Gill, K, Igusa, T, Zhao, X, Aguirre, B, Trainor, J, Nigg, J, Inglesby, T, Carbone, E, Kendra, JM
JournalDisaster Medicine and Public Health Preparedness
Type of ArticleArticle
ISBN Number19357893 (ISSN)
KeywordsAdaptation, Psychological, classification, community functioning, conceptual framework, coping behavior, demography, disaster, disaster planning, disaster victim, Disaster Victims, human, Humans, investment, model, Models, Theoretical, procedures, psychology, Public Health, reproducibility, Reproducibility of Results, Residence Characteristics, Resilience, system analysis, system dynamics, Systems Analysis, theoretical model, trends, validation process

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. © 2017 Society for Disaster Medicine and Public Health, Inc.