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Bayesian Analysis of the Disaster Damage in Brazil

  • Camila Bertini MartinsEmail author
  • Viviana Aguilar Muñoz
  • André Yoshizumi Gomes
  • Ricardo Manhães Savii
  • Carolina Locatelli Colla
Chapter

Abstract

Statistical modeling is a useful methodology to support the research on disaster risk reduction (DRR) and the development of strategies for sustainability. It has been established in at least two of the major United Nations Conferences and Summits which have laid the solid foundation for sustainable development: The 2030 Agenda and the Sendai Framework. International standards also indicate various statistical techniques and tools to support risk management, especially at the risk assessment process. The main goal of this chapter is to present the Bayesian inference framework as a promising tool for risk identification process in the context of DRR. For such, it was used the data collected from the S2ID Brazilian database, period 2003–2016. Considering the geographical complexity, this is only a diagnostic research, but it is a first step and a very interesting beginning because Brazilian complexity is a prominent condition to understand risks and disasters deep causes in this country, and an opportunity to strengthen mathematical and statistical solutions to sustainable development challenges.

Notes

Acknowledgements

The authors would like to thank to Federal University of São Paulo—UNIFESP, to CNPq—Brazil, to National Early Warning and Monitoring Center of Natural Disasters (CEMADEN) and to the Thematic Committee Mathematics & Disasters of the Brazilian Society of Applied (SBMAC), by its research support.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Camila Bertini Martins
    • 1
    Email author
  • Viviana Aguilar Muñoz
    • 2
  • André Yoshizumi Gomes
    • 3
  • Ricardo Manhães Savii
    • 4
  • Carolina Locatelli Colla
    • 4
  1. 1.Federal University of São Paulo, Paulista School of Medicine, Department of Preventive MedicineSão PauloBrazil
  2. 2.National Centre for Monitoring and Early Warnings of Natural Disasters (CEMADEN)São José dos CamposBrazil
  3. 3.Serasa Experian, Decision AnalyticsSão PauloBrazil
  4. 4.Federal University of São Paulo, Institute of Science and TechnologySão José dos CamposBrazil

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