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

哈萨克斯坦动物群体狂犬病病例的时空贝叶斯回归模型(2013-2023 年)

阅读:2

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 presence of rabies in wildlife (OR: 1.55, 95% PPI: 1.27-1.89), and companion animal rabies incidence (low: 1-5 cases/year, OR: 1.39, 95% PPI: 1.06-1.85; high: ≥6 cases/year, OR: 2.07, 95% PPI: 1.46-2.96) were associated with increased livestock rabies risk, while higher human population density correlated with reduced risk (OR: 0.68, 95% PPI: 0.5-0.9). Spatial analysis identified persistent high-risk zones in western Kazakhstan and lower risk in southern regions, driven by ecological and socioeconomic heterogeneity. DISCUSSION: These findings highlight the relationship between wildlife reservoirs, domestic animal management, and socioeconomic factors in rabies transmission in Kazakhstan. By integrating these insights into national policy, Kazakhstan can advance toward the global target of eliminating dog-mediated human rabies deaths by 2030, serving as a model for Central Asia.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。