Georges Lemaître centre for Earth and Climate research, Earth & Life Institute, Université catholique de Louvain, Place Pasteur 3 L4.03.08, 1348, Louvain-la-Neuve, Belgium.
Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Institute of Novel and Emerging Infectious Diseases, Südufer 10, D-17493, Greifswald-Insel Riems, Germany.
Sci Rep. 2019 Feb 20;9(1):2329. doi: 10.1038/s41598-019-38802-5.
Zoonotic diseases are challenging to study from the ecological point of view as, broadly speaking, datasets tend to be either detailed on a small spatial extent, or coarse on a large spatial extent. Also, there are many ways to assess zoonotic disease transmission systems, from pathogens to hosts to humans. We explore the complementarity of datasets considering the pathogen in its host, the host and human cases in the context of Puumala orthohantavirus infection in Germany. We selected relevant environmental predictors using a conceptual framework based on resource-based habitats. This framework assesses the functions, and associated environmental resources of the pathogen and associated host. A resource-based habitat framework supports variable selection and result interpretation. Multiplying 'keyholes' to view a zoonotic disease transmission system is valuable, but requires a strong conceptual framework to select and interpret environmental explanatory variables. This study highlights the usefulness of a structured, ecology-based approach to study drivers of zoonotic diseases at the level of virus, host, and human - not only for PUUV but also for other zoonotic pathogens. Our results show that human disease cases are best explained by a combination of variables related to zoonotic pathogen circulation and human exposure.
从生态学的角度来看，人畜共患病很难研究，因为一般来说，数据集要么在小的空间范围内详细，要么在大的空间范围内粗略。此外，评估人畜共患病传播系统的方法有很多种，从病原体到宿主再到人类。我们探讨了在德国 Puumala 正呼肠孤病毒感染背景下，考虑病原体与其宿主、宿主和人类病例的数据集互补性。我们使用基于资源生境的概念框架选择了相关的环境预测因子。该框架评估了病原体和相关宿主的功能以及相关的环境资源。基于资源的生境框架支持变量选择和结果解释。通过“钥匙孔”相乘来观察人畜共患病传播系统是有价值的，但需要一个强大的概念框架来选择和解释环境解释变量。本研究强调了在病毒、宿主和人类层面上采用基于生态学的结构化方法研究人畜共患病驱动因素的有用性——不仅对于 PUUV，对于其他人畜共患病病原体也是如此。我们的研究结果表明，人类疾病病例最好用与人畜共患病病原体循环和人类暴露相关的变量组合来解释。
