Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-24T06:12:04.292Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  21 June 2017

Jonathan M. Links ,
Brian S. Schwartz ,
Sen Lin ,
Norma Kanarek ,
Judith Mitrani-Reiser ,
Tara Kirk Sell ,
Crystal R. Watson ,
Doug Ward ,
Cathy Slemp  and
Robert Burhans
...Show all authors
Jonathan M. Links*
Affiliation:
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland Center for Public Health Preparedness, Johns Hopkins University, Baltimore, Maryland
Brian S. Schwartz
Affiliation:
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
Sen Lin
Affiliation:
Department of Civil Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland
Norma Kanarek
Affiliation:
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
Judith Mitrani-Reiser
Affiliation:
Department of Civil Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland
Tara Kirk Sell
Affiliation:
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland Center for Health Security, Johns Hopkins University, Baltimore, Maryland
Crystal R. Watson
Affiliation:
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland Center for Health Security, Johns Hopkins University, Baltimore, Maryland
Doug Ward
Affiliation:
Division of Public Safety Leadership, Johns Hopkins School of Education, Baltimore, Maryland
Cathy Slemp
Affiliation:
Independent Consultants
Robert Burhans
Affiliation:
Independent Consultants
Kimberly Gill
Affiliation:
Disaster Research Center, University of Delaware, Newark, Delaware
Tak Igusa
Affiliation:
Department of Civil Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland
Xilei Zhao
Affiliation:
Department of Civil Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland
Benigno Aguirre
Affiliation:
Disaster Research Center, University of Delaware, Newark, Delaware
Joseph Trainor
Affiliation:
Disaster Research Center, University of Delaware, Newark, Delaware
Joanne Nigg
Affiliation:
Disaster Research Center, University of Delaware, Newark, Delaware
Thomas Inglesby
Affiliation:
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland Center for Health Security, Johns Hopkins University, Baltimore, Maryland
Eric Carbone
Affiliation:
Office of Public Health Preparedness and Response, Centers for Disease Control and Prevention, Atlanta, Georgia
James M. Kendra
Affiliation:
Disaster Research Center, University of Delaware, Newark, Delaware
*
Correspondence and reprint requests to Jonathan M. Links, Johns Hopkins University, 258 Garland Hall, 3400 N Charles St, Baltimore, MD 21218 (e-mail: [email protected]).
Get access Rights & Permissions [Opens in a new window]

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)

Keywords

resiliencesystem dynamicscommunity functioning
Type
Concepts in Disaster Medicine
Information
Disaster Medicine and Public Health Preparedness , Volume 12 , Issue 1 , February 2018 , pp. 127 - 137
Copyright
Copyright © Society for Disaster Medicine and Public Health, Inc. 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Gordon, JE. Structures: or, Why Things Don’t Fall Down. Harmondsworth, New York: Penguin Books; 1978. https://doi.org/10.1007/978-1-4615-9074-3.Google Scholar
2. Department of Homeland Security. Presidential Policy Directive/PPD-8: National Preparedness. Washington, DC: Department of Homeland Security; 2011. https://www.dhs.gov/presidential-policy-directive-8-national-preparedness. Accessed May 9, 2017.Google Scholar
3. Department of Homeland Security. National Infrastructure Protection Plan: Partnering for Critical Infrastructure and Security. http://www.dhs.gov/sites/default/files/publications/National-Infrastructure-Protection-Plan-2013-508.pdf. Published 2013. Accessed April 29, 2016.Google Scholar
4. Bruneau, M, Chang, SE, Eguchi, RT, et al. A framework to quantitatively assess and enhance the seismic resilience of communities. Earthq Spectra. 2003;19(4):733-752. https://doi.org/10.1193/1.1623497.Google Scholar
5. Pfefferbaum, RL, Pfefferbaum, B, Van Horn, RL, et al. Building community resilience to disasters through a community-based intervention: CART applications. J Emerg Manag. 2013;11(2):151-159. https://doi.org/10.5055/jem.2013.0134.Google Scholar
6. Pfefferbaum, RL, Pfefferbaum, B, Van Horn, RL, et al. The Communities Advancing Resilience Toolkit (CART): an intervention to build community resilience to disasters. J Public Health Manag Pract. 2013;19(3):250-258. https://doi.org/10.1097/PHH.0b013e318268aed8.Google Scholar
7. Cutter, SL, Barnes, L, Berry, M, et al. A place-based model for understanding community resilience to natural disasters. Glob Environ Change. 2008;18(4):598-606. https://doi.org/10.1016/j.gloenvcha.2008.07.013.CrossRefGoogle Scholar
8. Chandra, A, Acosta, J, Meredith, LS, et al. Understanding Community Resilience in the Context of National Health Security. Rand Working Paper. http://www.rand.org/content/dam/rand/pubs/working_papers/2010/RAND_WR737.pdf. Published February 2010. Accessed May 9, 2017.Google Scholar
9. Chandra, A, Acosta, J, Stern, S, et al. Building Community Resilience to Disasters. Rand Technical Report. http://www.rand.org/content/dam/rand/pubs/technical_reports/2011/RAND_TR915.pdf. Published 2011. Accessed May 9, 2017.Google Scholar
10. Plough, A, Fielding, JE, Chandra, A, et al. Building community disaster resilience: perspectives from a large urban county department of public health. Am J Public Health. 2013;103(7):1190-1197. https://doi.org/10.2105/AJPH.2013.301268.Google Scholar
11. Chandra, A, Williams, M, Plough, A, et al. Getting actionable about community resilience: the Los Angeles County Community Disaster Resilience Project. Am J Public Health. 2013;103(7):1181-1189. https://doi.org/10.2105/AJPH.2013.301270.Google Scholar
12. Wells, KB, Tang, J, Lizaola, E, et al. Applying community engagement to disaster planning: developing the vision and design for the Los Angeles County Community Disaster Resilience Initiative. Am J Public Health. 2013;103(7):1172-1180. https://doi.org/10.2105/AJPH.2013.301407.Google Scholar
13. Maglio, PP, Sepulveda, MJ, Mabry, PL. Mainstreaming modeling and simulation to accelerate public health innovation. Am J Public Health. 2014;104(7):1181-1186. https://doi.org/10.2105/AJPH.2014.301873.Google Scholar
14. Checkland, P. Systems Thinking, Systems Practice: Includes a 30-Year Retrospective. Chichester, United Kingdom: John Wiley & Sons; 1999.Google Scholar
15. Meadows, DH, Wright, D. Thinking in Systems: A Primer. White River Junction, VT: Chelsea Green Pub; 2008.Google Scholar
16. Forrester, JW. System dynamics, systems thinking, and soft OR. Syst Dynam Rev. 1994;10(2-3):245-256. doi: 10.1002/sdr.4260100211 Google Scholar
17. Mabry, PL, Olster, DH, Morgan, GD, et al. Interdisciplinarity and systems science to improve population health: a view from the NIH Office of Behavioral and Social Sciences Research. Am J Prev Med. 2008;35(2)(suppl):S211-S224. https://doi.org/10.1016/j.amepre.2008.05.018.Google Scholar
18. Sterman, J. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston: Irwin/McGraw-Hill; 2000.Google Scholar
19. Homer, JB, Hirsch, GB. System dynamics modeling for public health: background and opportunities. Am J Public Health. 2006;96(3):452-458. https://doi.org/10.2105/AJPH.2005.062059.Google Scholar
20. Hovmand, PS. Community Based System Dynamics. Springer; 2014; https://doi.org/10.1007/978-1-4614-8763-0. Accessed May 9, 2017.Google Scholar
21. Leischow, SJ, Milstein, B. Systems thinking and modeling for public health practice. Am J Public Health. 2006;96(3):403-405. https://doi.org/10.2105/AJPH.2005.082842.Google Scholar
22. Lyon, AR, Maras, MA, Pate, CM, et al. Modeling the impact of school-based universal depression screening on additional service capacity needs: a system dynamics approach. Adm Policy Ment Health. 2016;43(2):168-188. https://doi.org/10.1007/s10488-015-0628-y.Google Scholar
23. Sakia, RM. The Box-Cox transformation technique - a review. Statistician. 1992;41(2):169-178. https://doi.org/10.2307/2348250.Google Scholar
24. Box, GEP, Cox, DR. An analysis of transformations. J R Stat Soc B. 1964;26(2):211-252.Google Scholar
25. Norris, FH, Stevens, SP, Pfefferbaum, B, et al. Community resilience as a metaphor, theory, set of capacities, and strategy for disaster readiness. Am J Community Psychol. 2008;41(1-2):127-150. https://doi.org/10.1007/s10464-007-9156-6.CrossRefGoogle ScholarPubMed
26. Miles, SB, Chang, SE. Modeling community recovery from earthquakes. Earthq Spectra. 2006;22(2):439-458. https://doi.org/10.1193/1.2192847.Google Scholar
27. Burby, RJ, Deyle, RE, Godschalk, DR, et al. Creating hazard resilient communities through land-use planning. Nat Hazards Rev. 2000;1(2):99-106. https://doi.org/10.1061/(ASCE)1527-6988(2000)1:2(99).Google Scholar
28. Aguirre, BE, Wenger, D, Vigo, G. Test of the emergent norm theory of collective behavior. Sociol Forum. 1998;13(2):301-320. https://doi.org/10.1023/A:1022145900928.Google Scholar
29. Simpson, B, Willer, R. Beyond altruism: sociological foundations of cooperation and prosocial behavior. Annu Rev Sociol. 2015;41(1):43-63. https://doi.org/10.1146/annurev-soc-073014-112242.Google Scholar
30. Kendra, JM, Wachtendorf, T. Elements of resilience after the World Trade Center disaster: reconstituting New York City’s Emergency Operations Centre. Disasters. 2003;27(1):37-53. https://doi.org/10.1111/1467-7717.00218.Google Scholar
31. Kendra, JM, Wachtendorf, T. Reconsidering convergence and converger legitimacy in response to the World Trade Center disaster. Research in Social Problems in Public Policy. 2003;11:97-122. https://doi.org/10.1016/S0196-1152(03)11007-1.Google Scholar
32. Kendra, JM, Wachtendorf, T. Improvisation, creativity, and the art of emergency management. In: Durmaz H, Sevinc B, Yayla AS, Ekici S, eds. Vol 19: Understanding and Responding to Terrorism. NATO Security Through Science Series E: Human and Societal Dynamics. 2007:324-335.Google Scholar
33. Jacob, B, Mawson, AR, Payton, M, et al. Disaster mythology and fact: hurricane Katrina and social attachment. Public Health Rep. 2008;123(5):555-566.Google Scholar
34. Wisner, B, Blaikie, P, Cannon, T, et al. At Risk: Natural Hazards, People’s Vulnerability, and Disasters. 2nd ed. New York: Psychology Press; 2004.Google Scholar
Supplementary material: File

Links supplementary material

Figure S1

Download Links supplementary material(File)
File 620.1 KB
Supplementary material: File

Links supplementary material

Figure S2

Download Links supplementary material(File)
File 1.1 MB
Supplementary material: File

Links supplementary material

Figure S3

Download Links supplementary material(File)
File 936.9 KB
Supplementary material: File

Links supplementary material

Figure S4

Download Links supplementary material(File)
File 942.9 KB