Bayesian Analysis of the Disaster Damage in Brazil

Camila Bertini Martins (), Viviana Aguilar Muñoz, André Yoshizumi Gomes, Ricardo Manhães Savii and Carolina Locatelli Colla
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Camila Bertini Martins: Federal University of São Paulo, Paulista School of Medicine, Department of Preventive Medicine
Viviana Aguilar Muñoz: National Centre for Monitoring and Early Warnings of Natural Disasters (CEMADEN)
André Yoshizumi Gomes: Serasa Experian, Decision Analytics
Ricardo Manhães Savii: Federal University of São Paulo, Institute of Science and Technology
Carolina Locatelli Colla: Federal University of São Paulo, Institute of Science and Technology

A chapter in Towards Mathematics, Computers and Environment: A Disasters Perspective, 2019, pp 163-183 from Springer

Abstract: 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.

Date: 2019
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DOI: 10.1007/978-3-030-21205-6_9

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