COPEWELL: A Conceptual Framework and System Dynamics Model for Predicting Community Functioning and Resilience After Disasters. (21st June 2017)

• Record Type: Journal Article
• Authors: Links, Jonathan M.; Schwartz, Brian S.; Lin, Sen; Kanarek, Norma; Mitrani-Reiser, Judith; Sell, Tara Kirk; Watson, Crystal R.; Ward, Doug; Slemp, Cathy; Burhans, Robert; Gill, Kimberly; Igusa, Tak; Zhao, Xilei; Aguirre, Benigno; Trainor, Joseph; Nigg, Joanne; Inglesby, Thomas; Carbone, Eric; Kendra, James M.
• Is Part Of: Disaster medicine and public health preparedness. Volume 12:Number 1(2018)
• Page Start: 127
• Page End: 137
• Publication Date: 2017-06-21
• Subjects: resilience, -- system dynamics, -- community functioning; Disaster medicine -- Periodicals; Emergency management -- Planning -- Periodicals; Public health -- Periodicals; 363.34
• DOI: 10.1017/dmp.2017.39
• Languages: English
• ISSNs: 1935-7893

Abstract

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)