Bayesian spatio-temporal disease mapping of COVID-19 cases in Bangladesh.

ABSTRACT:

Background

COVID-19 is a highly transmittable respiratory illness induced by SARS-CoV-2, a novel coronavirus. The spatio-temporal analysis considers interactions between space and time is essential for understanding the virus's transmission pattern and developing efficient mitigation strategies.

Objective

This study explicitly examines how meteorological, demographic, and vaccination with all doses of risk factors are interrelated with COVID-19's complex evolution and dynamics in 64 Bangladeshi districts over space and time.

Methods

The study employed Bayesian spatio-temporal Poisson modeling to determine the most suitable model, including linear trend, analysis of variance (ANOVA), separable models, and Poisson temporal model for spatiotemporal effects. The study emp

SUBMITTER: Barket SE 

PROVIDER: S-EPMC11828356 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature