Beck-Johnson Lindsay M, Gorsich Erin E, Hallman Clayton, Tildesley Michael J, Miller Ryan S, Webb Colleen T
Department of Biology, Colorado State University, United States of America.
Department of Biology, Colorado State University, United States of America.
Epidemics. 2023 Mar;42:100668. doi: 10.1016/j.epidem.2023.100668. Epub 2023 Jan 18.
Transboundary livestock diseases are a high priority for policy makers because of the serious economic burdens associated with infection. In order to make well informed preparedness and response plans, policy makers often utilize mathematical models to understand possible outcomes of different control strategies and outbreak scenarios. Many of these models focus on the transmission between herds and the overall trajectory of the outbreak. While the course of infection within herds has not been the focus of the majority of models, a thorough understanding of within-herd dynamics can provide valuable insight into a disease system by providing information on herd-level biological properties of the infection, which can be used to inform decision making in both endemic and outbreak settings and to inform larger between-herd models. In this study, we develop three stochastic simulation models to study within-herd foot and mouth disease dynamics and the implications of different empirical data-based assumptions about the timing of the onset of infectiousness and clinical signs. We also study the influence of herd size and the proportion of the herd that is initially infected on the outcome of the infection. We find that increasing herd size increases the duration of infectiousness and that the size of the herd plays a more significant role in determining this duration than the number of initially infected cattle in that herd. We also find that the assumptions made regarding the onset of infectiousness and clinical signs, which are based on contradictory empirical findings, can result in the predictions about when infection would be detectable differing by several days. Therefore, the disease progression used to characterize the course of infection in a single bovine host could have significant implications for determining when herds can be detected and subsequently controlled; the timing of which could influence the overall predicted trajectory of outbreaks.
跨界牲畜疾病是政策制定者的高度优先事项，因为感染会带来严重的经济负担。为了制定充分知情的防范和应对计划，政策制定者经常利用数学模型来了解不同控制策略和疫情情景的可能结果。其中许多模型侧重于畜群间的传播以及疫情的总体发展轨迹。虽然畜群内的感染过程并非大多数模型的重点，但深入了解畜群内动态可以通过提供有关感染的畜群水平生物学特性的信息，为疾病系统提供有价值的见解，这些信息可用于为地方病和疫情环境下的决策提供参考，并为更大的畜群间模型提供参考。在本研究中，我们开发了三个随机模拟模型，以研究畜群内口蹄疫动态以及关于传染性和临床症状发作时间的不同基于经验数据的假设的影响。我们还研究了畜群规模以及最初感染的畜群比例对感染结果的影响。我们发现，畜群规模的增加会增加传染性持续时间，并且畜群规模在确定这一持续时间方面比该畜群中最初感染的牛的数量发挥更重要的作用。我们还发现，基于相互矛盾的经验发现所做的关于传染性和临床症状发作的假设，可能导致关于何时可检测到感染的预测相差数天。因此，用于描述单个牛宿主感染过程的疾病进展可能对确定何时可以检测到畜群并随后进行控制具有重大影响；这一时间安排可能会影响疫情的总体预测轨迹。
