Advances and Applications in Statistics
Volume 90, Issue 1, Pages 35 - 58
(November 2023) http://dx.doi.org/10.17654/0972361723062 |
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ON THE BAYESIAN ZERO-INFLATED SPATIO-TEMPORAL MODELLING OF DENGUE HEMORRHAGIC FEVER
Daniel R. Sanson and Daisy Lou Lim-Polestico
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Abstract: This study focuses on improving the convergence rates of parameter estimates for two Bayesian spatio-temporal models, namely the Bayesian zero-inflated Poisson spatio-temporal (BZIP S-T) distribution and the Bayesian zero-inflated negative binomial spatio-temporal (BZINB S-T) distribution, employed for modeling dengue hemorrhagic fever (DHF) data in the Caraga region, Philippines. The predictive performance of these models, incorporating meteorological factors such as rainfall and population density, is enhanced through the implementation of an overrelaxation algorithm designed to expedite convergence. Markov chain Monte Carlo (MCMC) techniques, specifically utilizing the full conditional distribution, are utilized for parameter estimation.
Our findings reveal that the application of the overrelaxation algorithm yields significant improvements in the convergence rates of parameter estimates, with acceleration percentages of up to 67% and 40% observed for the BZIP S-T and BZINB S-T models, respectively. Notably, both the models identify rainfall and population density as statistically significant predictors for DHF case predictions in the Caraga region, Philippines. While the BZINB S-T model exhibits the smallest deviance, both the models prove to be valuable tools for predicting DHF cases in the region, contributing to the advancement of epidemiological research and public health planning.
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Keywords and phrases: Bayesian zero-inflated Poisson distribution, zero-inflated negative binomial distribution, spatio-temporal model.
Received: April 4, 2023; Revised: September 13, 2023; Accepted: September 20, 2023
How to cite this article: Daniel R. Sanson and Daisy Lou Lim-Polestico, On the Bayesian zero-inflated spatio-temporal modelling of dengue hemorrhagic fever, Advances and Applications in Statistics 90(1) (2023), 35-58. http://dx.doi.org/10.17654/0972361723062
This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License
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