Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

Revista da Sociedade Brasileira de Medicina Tropical

Universidade de São Paulo, Ribeirão Preto¹  Souza, Aparecida Doniseti Pires de¹  Universidade Estadual Paulista, Presidente Prudente¹  Tachibana, Vilma Mayumi¹  Achcar, Jorge Alberto¹  Martinez, Edson Zangiacomi¹  Flores, Edilson Ferreira¹ 

关键词: Malaria;    Statistics;    Deforestation;    Environment;    Amazon;    Bayesian methods;   

DOI  :  10.1590/S0037-86822011000600019

学科分类：农业科学（综合）

来源: Sociedade Brasileira de Medicina Tropical

【 摘 要 】

INTRODUCTION: Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities. The objective of this article was to investigate the association between environmental variables and the yearly registers of malaria in the Amazon region using Bayesian spatiotemporal methods.METHODS: We used Poisson spatiotemporal regression models to analyze the Brazilian Amazon forest malaria count for the period from 1999 to 2008. In this study, we included some covariates that could be important in the yearly prediction of malaria, such as deforestation rate. We obtained the inferences using a Bayesian approach and Markov Chain Monte Carlo (MCMC) methods to simulate samples for the joint posterior distribution of interest. The discrimination of different models was also discussed.RESULTS: The model proposed here suggests that deforestation rate, the number of inhabitants per km2, and the human development index (HDI) are important in the prediction of malaria cases.CONCLUSIONS: It is possible to conclude that human development, population growth, deforestation, and their associated ecological alterations are conducive to increasing malaria risk. We conclude that the use of Poisson regression models that capture the spatial and temporal effects under the Bayesian paradigm is a good strategy for modeling malaria counts.

【 授权许可】

Unknown