Department of Livestock Development (DLD), Bangkok, Thailand.
Department of Clinical Sciences and Public Health, and the Monitoring and Surveillance Center for Zoonotic Diseases in Wildlife and Exotic Animals, Faculty of Veterinary Science, Mahidol University, Nakhon Pathom, Thailand.
PLoS Negl Trop Dis. 2021 Dec 1;15(12):e0009980. doi: 10.1371/journal.pntd.0009980. eCollection 2021 Dec.
The situation of human rabies in Thailand has gradually declined over the past four decades. However, the number of animal rabies cases has slightly increased in the last ten years. This study thus aimed to describe the characteristics of animal rabies between 2017 and 2018 in Thailand in which the prevalence was fairly high and to quantify the association between monthly rabies occurrences and explainable variables using the generalized additive models (GAMs) to predict the spatial risk areas for rabies spread. Our results indicate that the majority of animals affected by rabies in Thailand are dogs. Most of the affected dogs were owned, free or semi-free roaming, and unvaccinated. Clusters of rabies were highly distributed in the northeast, followed by the central and the south of the country. Temporally, the number of cases gradually increased after June and reached a peak in January. Based on our spatial models, human and cattle population density as well as the spatio-temporal history of rabies occurrences, and the distances from the cases to the secondary roads and country borders are identified as the risk factors. Our predictive maps are applicable for strengthening the surveillance system in high-risk areas. Nevertheless, the identified risk factors should be rigorously considered and integrated into the strategic plans for the prevention and control of animal rabies in Thailand.
在过去的四十年中,泰国的人类狂犬病情况逐渐下降。然而,在过去的十年中,动物狂犬病病例的数量略有增加。因此,本研究旨在描述 2017 年至 2018 年泰国动物狂犬病的特征,该地区的狂犬病流行率相当高,并使用广义加性模型(GAMs)来量化每月狂犬病发生与可解释变量之间的关系,以预测狂犬病传播的空间风险区域。研究结果表明,泰国受狂犬病影响的动物大多数是狗。大多数受感染的狗都是家养的、自由或半自由游荡的,并且未接种疫苗。狂犬病的集群高度分布在东北部,其次是中部和南部。从时间上看,病例数量在 6 月后逐渐增加,并在 1 月达到高峰。根据我们的空间模型,人类和牛的人口密度以及狂犬病发生的时空历史,以及病例与次要道路和国界的距离,被确定为风险因素。我们的预测图适用于加强高风险地区的监测系统。然而,应严格考虑并将确定的风险因素纳入泰国预防和控制动物狂犬病的战略计划中。