A time-space Bayesian regression model of rabies cases in the animal population of Kazakhstan (2013-2023).

ABSTRACT:

Introduction

Despite its endemic status and socioeconomic impacts, the spatial-temporal variation in rabies risk and its underlying determinants in Kazakhstan animal populations remain poorly understood. This study aimed to characterize the time-space dynamics of rabies in animal populations across Kazakhstan regions from 2013 to 2023 and identify the key drivers of transmission.

Methods

Using a Bayesian hierarchical regression model with spatial and temporal random effects, we analyzed national surveillance data on rabies cases in livestock, companion animals, and wildlife, alongside sociodemographic and animal population variables.

Results

The model revealed that higher median income (odds ratio [OR]: 1.18, 95% posterior predictive interval [PPI]: 1.06-1.31), the pr

SUBMITTER: Gomez-Buendia A 

PROVIDER: S-EPMC12643857 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature