蜱传病原体的比较生态分析和预测模型。

William Manley, Tam Tran, Melissa Prusinski, Dustin Brisson
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引用次数: 0

摘要

蜱媒疾病是北美洲最主要的病媒传播健康威胁。最近的观察发现,黑腿蜱(Ixodes scapularis Say,Acari: Ixodidae)的分布范围明显扩大,同时由其传播的病原体引起的疾病发病率也在上升:Borrelia burgdorferi Johnson(螺旋体科:Spirochaetales)、Babesia microti Starcovici(螺旋体科:Babesiidae)和 Anaplasma phagocytophilium Zhu(立克次体科:Anaplasmataceae)分别是莱姆病、巴贝丝菌病和无形体病的病原体。先前的研究发现了影响恙虫病和巴贝虫生态动态的环境特征,这些特征可用于预测这些生物的分布和丰度,从而预测莱姆病的风险。与此相反,有关微小芽孢杆菌和噬菌体的环境决定因素的研究却很少。在这里,我们利用十多年的监测数据来模拟环境特征对纽约州(NYS)各地蜱虫中这些日益常见的人类病原体感染率的影响。我们的研究结果表明,从 2009 年到 2019 年,B. microti 在纽约州一直向北和向西扩展,而 A. phagocytophilum 的分布范围在精细空间尺度上有所不同。我们利用超过 650 个地点年的访问数据构建了生物地理模型,其中包含 250 多个环境变量,以准确预测每种病原体在模型训练中未包含的未来年份的感染率。研究发现,几种环境特征对病原体的影响各不相同,揭示了制约病原体分布和数量的潜在生态差异。这些经过验证的生物地理模型适用于疾病预防工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative ecological analysis and predictive modeling of tick-borne pathogens.

Tick-borne diseases constitute the predominant vector-borne health threat in North America. Recent observations have noted a significant expansion in the range of the black-legged tick (Ixodes scapularis Say, Acari: Ixodidae), alongside a rise in the incidence of diseases caused by its transmitted pathogens: Borrelia burgdorferi Johnson (Spirochaetales: Spirochaetaceae), Babesia microti Starcovici (Piroplasmida: Babesiidae), and Anaplasma phagocytophilium Zhu (Rickettsiales: Anaplasmataceae), the causative agents of Lyme disease, babesiosis, and anaplasmosis, respectively. Prior research identified environmental features that influence the ecological dynamics of I. scapularis and B. burgdorferi that can be used to predict the distribution and abundance of these organisms, and thus Lyme disease risk. In contrast, there is a paucity of research into the environmental determinants of B. microti and A. phagocytophilium. Here, we use over a decade of surveillance data to model the impact of environmental features on the infection prevalence of these increasingly common human pathogens in ticks across New York State (NYS). Our findings reveal a consistent northward and westward expansion of B. microti in NYS from 2009 to 2019, while the range of A. phagocytophilum varied at fine spatial scales. We constructed biogeographic models using data from over 650 site-year visits and encompassing more than 250 environmental variables to accurately forecast infection prevalence for each pathogen to a future year that was not included in model training. Several environmental features were identified to have divergent effects on the pathogens, revealing potential ecological differences governing their distribution and abundance. These validated biogeographic models have applicability for disease prevention efforts.

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