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An Integrated and Interdisciplinary Model for Predicting the Risk of Injury and Death in Future Earthquakes

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  • Stav Shapira
  • Lena Novack
  • Yaron Bar-Dayan
  • Limor Aharonson-Daniel

Abstract

Background: A comprehensive technique for earthquake-related casualty estimation remains an unmet challenge. This study aims to integrate risk factors related to characteristics of the exposed population and to the built environment in order to improve communities’ preparedness and response capabilities and to mitigate future consequences. Methods: An innovative model was formulated based on a widely used loss estimation model (HAZUS) by integrating four human-related risk factors (age, gender, physical disability and socioeconomic status) that were identified through a systematic review and meta-analysis of epidemiological data. The common effect measures of these factors were calculated and entered to the existing model’s algorithm using logistic regression equations. Sensitivity analysis was performed by conducting a casualty estimation simulation in a high-vulnerability risk area in Israel. Results: the integrated model outcomes indicated an increase in the total number of casualties compared with the prediction of the traditional model; with regard to specific injury levels an increase was demonstrated in the number of expected fatalities and in the severely and moderately injured, and a decrease was noted in the lightly injured. Urban areas with higher populations at risk rates were found more vulnerable in this regard. Conclusion: The proposed model offers a novel approach that allows quantification of the combined impact of human-related and structural factors on the results of earthquake casualty modelling. Investing efforts in reducing human vulnerability and increasing resilience prior to an occurrence of an earthquake could lead to a possible decrease in the expected number of casualties.

Suggested Citation

  • Stav Shapira & Lena Novack & Yaron Bar-Dayan & Limor Aharonson-Daniel, 2016. "An Integrated and Interdisciplinary Model for Predicting the Risk of Injury and Death in Future Earthquakes," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-11, March.
  • Handle: RePEc:plo:pone00:0151111
    DOI: 10.1371/journal.pone.0151111
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    References listed on IDEAS

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    1. Stav Shapira & Limor Aharonson-Daniel & Igal Shohet & Corinne Peek-Asa & Yaron Bar-Dayan, 2015. "Integrating epidemiological and engineering approaches in the assessment of human casualties in earthquakes," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(2), pages 1447-1462, September.
    2. Ronald T. Eguchi & Hope A. Seligson, 2008. "Loss Estimation Models and Metrics," Risk, Governance and Society, in: Ann Bostrom & Steven French & Sara Gottlieb (ed.), Risk Assessment, Modeling and Decision Support, chapter 6, pages 135-170, Springer.
    3. Masozera, Michel & Bailey, Melissa & Kerchner, Charles, 2007. "Distribution of impacts of natural disasters across income groups: A case study of New Orleans," Ecological Economics, Elsevier, vol. 63(2-3), pages 299-306, August.
    4. T. Levi & D. Bausch & O. Katz & J. Rozelle & A. Salamon, 2015. "Insights from Hazus loss estimations in Israel for Dead Sea Transform earthquakes," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(1), pages 365-388, January.
    5. Gabriela Noriega & Lisa Ludwig, 2012. "Social vulnerability assessment for mitigation of local earthquake risk in Los Angeles County," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 64(2), pages 1341-1355, November.
    6. Jonathan Remo & Nicholas Pinter, 2012. "Hazus-MH earthquake modeling in the central USA," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 1055-1081, September.
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    Cited by:

    1. Chen, Weiyi & Zhang, Limao, 2022. "An automated machine learning approach for earthquake casualty rate and economic loss prediction," Reliability Engineering and System Safety, Elsevier, vol. 225(C).

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