%0 Journal Article
%T COPEWELL: a conceptual framework and system dynamics model for predicting community functioning and resilience after disasters
%J Disaster medicine and public health preparedness
%D 2018
%A Links, Jonathan M.
%A Schwartz, Brian S.
%A Lin, Sen
%A Kanarek, Norma
%A Mitrani-Reiser, Judith
%A Sell, Tara Kirk
%A Watson, Crystal R.
%A Ward, Doug
%A Slemp, Cathy
%A Burhans, Robert
%A Gill, Kimberly
%A Igusa, Tak
%A Zhao, Xilei
%A Aguirre, Benigno
%A Trainor, Joseph
%A Nigg, Joanne
%A Inglesby, Thomas
%A Carbone, Eric
%A Kendra, James M.
%V 12
%N 1
%P 127-137
%X 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. <br><br>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. <br><br>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. <br><br>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. 2017;page 1 of 11).<p /> <p>Language: en</p>
%G en
%I Cambridge University Press
%@ 1935-7893
%U http://dx.doi.org/10.1017/dmp.2017.39