Mitchell Rebecca M, Whitlock Robert H, Gröhn Yrjö T, Schukken Ynte H
Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14853, USA; Centers for Disease Control and Prevention, Division of Parasitology and Malaria, GA, USA.
New Bolton Center, University of Pennsylvania, Kennett Square, PA, USA.
Prev Vet Med. 2015 Feb 1;118(2-3):215-25. doi: 10.1016/j.prevetmed.2014.12.009. Epub 2014 Dec 22.
Mathematical models for infectious disease are often used to improve our understanding of infection biology or to evaluate the potential efficacy of intervention programs. Here, we develop a mathematical model that aims to describe infection dynamics of Mycobacterium avium subspecies paratuberculosis (MAP). The model was developed using current knowledge of infection biology and also includes some components of MAP infection dynamics that are currently still hypothetical. The objective was to show methods for parameter estimation of state transition models and to connect simulation models with detailed real life data. Thereby making model predictions and results of simulations more reflective and predictive of real world situations. Longitudinal field data from a large observational study are used to estimate parameter values. It is shown that precise data, including molecular diagnostics on the obtained MAP strains, results in more precise and realistic parameter estimates. It is argued that modeling of infection disease dynamics is of great value to understand the patho-biology, epidemiology and control of infectious diseases. The quality of conclusions drawn from model studies depend on two key issues; first, the quality of biology that has gone in the process of developing the model structure; second the quality of the data that go into the estimation of the parameters and the quality and quantity of the data that go into model validation. The more real world data that are used in the model building process, the more likely that modeling studies will provide novel, innovative and valid results.
传染病的数学模型常被用于增进我们对感染生物学的理解，或评估干预项目的潜在效果。在此，我们构建了一个旨在描述副结核分枝杆菌（MAP）感染动态的数学模型。该模型是依据当前的感染生物学知识构建的，其中还纳入了一些目前仍属假设的MAP感染动态组成部分。目的是展示状态转换模型的参数估计方法，并将模拟模型与详细的实际生活数据相联系。从而使模型预测和模拟结果更能反映和预测现实世界的情况。来自一项大型观察性研究的纵向现场数据被用于估计参数值。结果表明，精确的数据，包括对所获MAP菌株的分子诊断，能得出更精确和现实的参数估计。有人认为，对感染病动态进行建模对于理解传染病的病理生物学、流行病学及控制具有重要价值。从模型研究得出的结论质量取决于两个关键问题：第一，构建模型结构过程中所依据的生物学知识的质量；第二，用于参数估计的数据质量以及用于模型验证的数据质量和数量。模型构建过程中使用的现实世界数据越多，建模研究就越有可能提供新颖、创新且有效的结果。
