Pig herd management and infection transmission dynamics: a challenge for modellers
Pig herd management and infection transmission dynamics: a challenge for modellers
复制标题
猪群管理和感染传播动态：建模者面临的挑战
DOI：
10.1101/2023.05.17.541128
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Mathieu Andraud
中科院分区：
文献类型：
--
作者：
V. Sicard;S. Picault;Mathieu Andraud
流行病的控制需要彻底了解病原体传播、疾病影响以及种群动态和管理之间复杂的相互作用。机制流行病学建模是解决这一问题的有效方法，但处理高度结构化和动态的系统仍然具有挑战性。因此，我们开发了一种新颖的方法，将基于多级代理的系统（MLABS）与空间和时间组织相结合，从而能够对宿主群体之间的传输过程进行调整表示。我们应用这种方法来模拟猪场中 PRRSv 样病毒的传播，并在畜牧业实践方面整合临床后果（受孕和繁殖失败）。结果强调了在流行病学模型中考虑空间和时间结构以及畜群管理政策的重要性。事实上，与疾病相关的流产，导致不同批次母猪的重新分配，被证明可以增强传播过程，有利于病毒在猪群水平上的持续存在。在声明性领域特定语言（DSL）的支持下，我们的方法提供了灵活而强大的解决方案来解决农场流行病和更广泛的公共卫生问题。本申请基于简单的易感-暴露-感染-恢复（SEIR）模型，为表示更复杂的流行病学系统开辟了道路，包括更具体的特征，例如母源抗体、疫苗接种或双重感染，以及它们各自对管理实践的临床后果。
The control of epidemics requires a thorough understanding of the complex interactions between pathogen transmission, disease impact, and population dynamics and management. Mechanistic epidemiological modelling is an effective way to address this issue, but handling highly structured and dynamic systems, remains challenging. We therefore developed a novel approach that combines Multi-Level Agent-Based Systems (MLABS) with spatial and temporal organization, allowing for a tuned representation of the transmission processes amongst the host population. We applied this method to model the spread of a PRRSv-like virus in pig farms, integrating the clinical consequences (conception and reproduction failures), in terms of animal husbandry practices. Results highlighted the importance to account for spatial and temporal structuring and herd management policies in epidemiological models. Indeed, disease-related abortions, inducing reassignments of sows in different batches, was shown to enhance the transmission process, favouring the persistence of the virus at the herd level. Supported by a declarative Domain-Specific Language (DSL), our approach provides flexible and powerful solutions to address the issues of on-farm epidemics and broader public health concerns. The present application, based on a simple Susceptible-Exposed-Infected-Recovered (SEIR) model, opens the way to the representation of more complex epidemiological systems, including more specific features such as maternally derived antibodies, vaccination, or dual infections, along with their respective clinical consequences on the management practices.