Secure Food Systems Team, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, 55108, USA.
Department of Mathematics, Faculty of Science, Gulu University, Gulu, Uganda.
Open Vet J. 2022 Nov-Dec;12(6):787-796. doi: 10.5455/OVJ.2022.v12.i6.2. Epub 2022 Nov 5.
African swine fever (ASF) is one of the most important foreign animal diseases to the U.S. swine industry. Stakeholders in the swine production sector are on high alert as they witness the devastation of ongoing outbreaks in some of its most important trade partner countries. Efforts to improve preparedness for ASF outbreak management are proceeding in earnest and mathematical modeling is an integral part of these efforts.
This study aimed to assess the impact on within-herd transmission dynamics of ASF when the models used to simulate transmission assume there is homogeneous mixing of animals within a barn.
Barn-level heterogeneity was explicitly captured using a stochastic, individual pig-based, heterogeneous transmission model that considers three types of infection transmission, (1) within-pen via nose-to-nose contact; (2) between-pen via nose-to-nose contact with pigs in adjacent pens; and (3) both between- within-pen via distance-independent mechanisms (e.g., via fomites). Predictions were compared between the heterogeneous and the homogeneous Gillespie models.
Results showed that the predicted mean number of infectious pigs at specific time points differed greatly between the homogeneous and heterogeneous models for scenarios with low levels of between-pen contacts via distance-independent pathways and the differences between the two model predictions were more pronounced for the slow contact rate scenario. The heterogeneous transmission model results also showed that it may take significantly longer to detect ASF, particularly in large barns when transmission predominantly occurs via nose-to-nose contact between pigs in adjacent pens.
The findings emphasize the need for completing preliminary explorations when working with homogeneous mixing models to ascertain their suitability to predict disease outcomes.
非洲猪瘟（ASF）是对美国养猪业最重要的外来动物疾病之一。由于目睹其一些最重要的贸易伙伴国家正在发生的疫情的破坏，养猪业的利益相关者处于高度戒备状态。为提高对 ASF 暴发管理的准备工作正在认真进行，数学模型是这些努力的重要组成部分。
本研究旨在评估当用于模拟传播的模型假设畜舍内动物均匀混合时，ASF 对畜群内传播动态的影响。
使用基于个体猪的随机异质传播模型，明确捕获畜舍级别的异质性，该模型考虑了三种感染传播类型：（1）通过猪鼻对鼻接触在猪圈内传播；（2）通过猪鼻对鼻接触在相邻猪圈内传播；（3）通过距离无关机制（例如通过媒介物）在猪圈内和猪圈之间传播。比较了异质和同质 Gillespie 模型的预测结果。
结果表明，在低水平的通过距离无关途径的猪圈间接触情况下，同质和异质模型在特定时间点预测的感染猪数量差异很大，并且两种模型预测之间的差异对于慢接触率情况更为明显。异质传播模型的结果还表明，当传播主要通过相邻猪圈内的猪鼻对鼻接触发生时，特别是在大型畜舍中，ASF 可能需要更长的时间才能被发现。
这些发现强调了在使用同质混合模型进行工作时需要进行初步探索，以确定其预测疾病结果的适用性。
