TY  - JOUR
PY  - 2018//
TI  - COPEWELL: a conceptual framework and system dynamics model for predicting community functioning and resilience after disasters
JO  - Disaster medicine and public health preparedness
A1  - Links, Jonathan M.
A1  - Schwartz, Brian S.
A1  - Lin, Sen
A1  - Kanarek, Norma
A1  - Mitrani-Reiser, Judith
A1  - Sell, Tara Kirk
A1  - Watson, Crystal R.
A1  - Ward, Doug
A1  - Slemp, Cathy
A1  - Burhans, Robert
A1  - Gill, Kimberly
A1  - Igusa, Tak
A1  - Zhao, Xilei
A1  - Aguirre, Benigno
A1  - Trainor, Joseph
A1  - Nigg, Joanne
A1  - Inglesby, Thomas
A1  - Carbone, Eric
A1  - Kendra, James M.
SP  - 127
EP  - 137
VL  - 12
IS  - 1
N2  - 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>
LA  - en
SN  - 1935-7893
UR  - http://dx.doi.org/10.1017/dmp.2017.39
ID  - ref1
ER  -