Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, United States of America.
Department of Animal Sciences, Wageningen University, Wageningen, The Netherlands.
PLoS One. 2020 Nov 20;15(11):e0242683. doi: 10.1371/journal.pone.0242683. eCollection 2020.
Infectious disease management relies on accurate characterization of disease progression so that transmission can be prevented. Slowly progressing infectious diseases can be difficult to characterize because of a latency period between the time an individual is infected and when they show clinical signs of disease. The introduction of Mycobacterium avium ssp. paratuberculosis (MAP), the cause of Johne's disease, onto a dairy farm could be undetected by farmers for years before any animal shows clinical signs of disease. In this time period infected animals may shed thousands of colony forming units. Parameterizing trajectories through disease states from infection to clinical disease can help farmers to develop control programs based on targeting individual disease state, potentially reducing both transmission and production losses due to disease. We suspect that there are two distinct progression pathways; one where animals progress to a high-shedding disease state, and another where animals maintain a low-level of shedding without clinical disease. We fit continuous-time hidden Markov models to multi-year longitudinal fecal sampling data from three US dairy farms, and estimated model parameters using a modified Baum-Welch expectation maximization algorithm. Using posterior decoding, we observed two distinct shedding patterns: cows that had observations associated with a high-shedding disease state, and cows that did not. This model framework can be employed prospectively to determine which cows are likely to progress to clinical disease and may be applied to characterize disease progression of other slowly progressing infectious diseases.
传染病的管理依赖于对疾病进展的准确描述，以便预防传播。由于个体感染和出现临床疾病迹象之间存在潜伏期，因此缓慢进展的传染病难以描述。分枝杆菌 avium ssp. paratuberculosis（MAP）的引入，即约翰氏病的病因，可能会在任何动物出现临床疾病迹象之前，被农民多年未察觉。在此期间，受感染的动物可能会排出数千个菌落形成单位。通过从感染到临床疾病的疾病状态来参数化轨迹，可以帮助农民制定基于针对个体疾病状态的控制计划，从而有可能减少因疾病导致的传播和生产损失。我们怀疑存在两种不同的进展途径；一种是动物进展为高排放疾病状态，另一种是动物在没有临床疾病的情况下保持低水平排放。我们使用连续时间隐马尔可夫模型拟合了来自美国三个奶牛场的多年纵向粪便采样数据，并使用改进的 Baum-Welch 期望最大化算法估计了模型参数。通过后验解码，我们观察到两种不同的排放模式：一种是与高排放疾病状态相关的观测值的奶牛，另一种是没有的奶牛。该模型框架可以前瞻性地用于确定哪些奶牛可能进展为临床疾病，并可用于描述其他缓慢进展的传染病的疾病进展。
