Comparing different spatio-temporal modeling methods in dengue fever data analysis in Colombia during 2012–2015

Research output: Contribution to journalArticle

Abstract

In this paper, we compare a variety of spatio-temporal conditional autoregressive models to a dengue fever dataset in Colombia, and incorporate an innovative data transformation method in the data analysis. In order to gain a better understanding on the effects of different niche variables in the epidemiological process, we explore Poisson-lognormal and binomial models with different Bayesian spatio-temporal modeling methods in this paper. Our results show that the selected model can well capture the variations of the data. The population density, elevation, daytime and night land surface temperatures are among the contributory variables to identify potential dengue outbreak regions; precipitation and vegetation variables are not significant in the selected spatio-temporal mixed effects model. The generated dengue fever probability maps from the model show a geographic distribution of risk that apparently coincides with the elevation gradient. The results in the paper provide the most benefits for future work in dengue studies.

Original languageEnglish (US)
Article number100360
JournalSpatial and Spatio-temporal Epidemiology
Volume34
DOIs
StatePublished - Aug 2020
Externally publishedYes

Keywords

  • Bayesian statistics
  • Conditional autoregressive model
  • Ill-balanced data
  • Moran's I test
  • Random effect model

ASJC Scopus subject areas

  • Epidemiology
  • Geography, Planning and Development
  • Infectious Diseases
  • Health, Toxicology and Mutagenesis

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