Herraiz Cesar, Triguero-Ocaña Roxana, Laguna Eduardo, Jiménez-Ruiz Saúl, Peralbo-Moreno Alfonso, Martínez-López Beatriz, García-Bocanegra Ignacio, Risalde María Ángeles, Vicente Joaquín, Acevedo Pelayo
Health and Biotechnology Research Group (SaBio), Institute for Game and Wildlife Research (IREC), CSIC-JCCM-UCLM, Ciudad Real, Spain.
Fundación Artemisan, Ciudad Real, Spain.
J Anim Ecol. 2024 Sep;93(9):1275-1287. doi: 10.1111/1365-2656.14142. Epub 2024 Jul 14.
Interspecific interactions are highly relevant in the potential transmission of shared pathogens in multi-host systems. In recent decades, several technologies have been developed to study pathogen transmission, such as proximity loggers, GPS tracking devices and/or camera traps. Despite the diversity of methods aimed at detecting contacts, the analysis of transmission risk is often reduced to contact rates and the probability of transmission given the contact. However, the latter process is continuous over time and unique for each contact, and is influenced by the characteristics of the contact and the pathogen's relationship with both the host and the environment. Our objective was to assess whether a more comprehensive approach, using a movement-based model which assigns a unique transmission risk to each contact by decomposing transmission into contact formation, contact duration and host characteristics, could reveal disease transmission dynamics that are not detected with more traditional approaches. The model was built from GPS-collar data from two management systems in Spain where animal tuberculosis (TB) circulates: a national park with extensively reared endemic cattle, and an area with extensive free-range pigs and cattle farms. In addition, we evaluated the effect of the GPS device fix rate on the performance of the model. Different transmission dynamics were identified between both management systems. Considering the specific conditions under which each contact occurs (i.e. whether the contact is direct or indirect, its duration, the hosts characteristics, the environmental conditions, etc.) resulted in the identification of different transmission dynamics compared to using only contact rates. We found that fix intervals greater than 30 min in the GPS tracking data resulted in missed interactions, and intervals greater than 2 h may be insufficient for epidemiological purposes. Our study shows that neglecting the conditions under which each contact occurs may result in a misidentification of the real role of each species in disease transmission. This study describes a clear and repeatable framework to study pathogen transmission from GPS data and provides further insights to understand how TB is maintained in multi-host systems in Mediterranean environments.
种间相互作用在多宿主系统中共享病原体的潜在传播方面具有高度相关性。近几十年来，已经开发了多种技术来研究病原体传播，例如近距离记录器、GPS跟踪设备和/或相机陷阱。尽管旨在检测接触的方法多种多样，但对传播风险的分析通常简化为接触率以及给定接触情况下的传播概率。然而，后一过程随时间是连续的，并且每次接触都是独特的，并且受到接触特征以及病原体与宿主和环境关系的影响。我们的目标是评估一种更全面的方法，即使用基于移动的模型，通过将传播分解为接触形成、接触持续时间和宿主特征，为每次接触分配独特的传播风险，是否能够揭示传统方法未检测到的疾病传播动态。该模型是根据西班牙两个动物结核病（TB）流行的管理系统的GPS项圈数据构建的：一个有大量饲养本地牛的国家公园，以及一个有大量自由放养的猪和养牛场的地区。此外，我们评估了GPS设备定位率对模型性能的影响。在两个管理系统之间识别出了不同的传播动态。考虑到每次接触发生的具体条件（即接触是直接还是间接、其持续时间、宿主特征、环境条件等），与仅使用接触率相比，导致识别出不同的传播动态。我们发现GPS跟踪数据中大于30分钟的定位间隔会导致错过相互作用，而大于2小时的间隔可能对于流行病学目的来说是不够的。我们的研究表明，忽略每次接触发生的条件可能会导致错误识别每个物种在疾病传播中的实际作用。这项研究描述了一个清晰且可重复的框架，用于从GPS数据研究病原体传播，并提供了进一步的见解，以了解结核病在地中海环境的多宿主系统中是如何维持的。
