US-UK Collab: Mycobacterial Transmission Dynamics in Agricultural Systems: Integrating Phylogenetics, Epidemiology, Ecology, and Economics

疾病传播的数学模型是研究传染病控制的重要工具；然而，从现场数据估计参数往往很难量化，特别是对于复杂的疾病。近年来，由于下一代测序技术，全基因组测序变得更快、更负担得起。随着已测序病原体分离株的大型数据库的出现，传染病流行病学家将能够使用这些数据库，这些数据库比以前的血清分型方法更细粒度，以提高我们对传播动力学的理解。然而，对于传播时间较慢的细菌中如此详细的数据，还没有一种通用的方法。该项目建议开发一种量化方法，将全基因组序列(WGS)数据纳入细菌传播模型。为此，美国和英国的农业系统将使用分枝杆菌感染的分离物。分枝杆菌感染，包括鸟分枝杆菌亚种。副结核(MAP)和牛分枝杆菌(BTB)通常是在简单的单宿主系统中研究的。由于分枝杆菌病潜伏期长、诊断敏感性差、野生动物和环境的感染库以及异质性菌株的感染性，因此分枝杆菌病极难控制。控制这些疾病的关键被认为是一种综合的方法，以了解病原体在生态系统中各级发生传播的途径：动物内部、动物个体之间、牲畜与野生动物之间以及牲畜与环境之间。提出了一种利用WGS数据和系统动力学来确定传播动力学的多尺度通用方法。这些动态将被纳入经济框架内的疾病传播和控制的预测模型，以帮助决策者做出知情的控制选择。

Mathematical modeling of disease transmission is an important tool in studying infectious disease control; however, parameter estimation from field data is often difficult to quantify, especially with complex diseases. Whole-genome sequencing has become faster and more affordable in recent years, thanks to Next-Generation Sequencing techniques. As large databases of sequenced pathogen isolates become available, infectious disease epidemiologists will be able to use these databases, which are more fine-grained than previous serotyping methods, to improve our understanding of transmission dynamics. However, a general methodology is not yet available for such detailed data in bacteria with slow transmission timescales. This project proposes to develop a quantitative methodology for incorporating whole genome sequence (WGS) data into bacterial transmission models. For this, isolates will be used from mycobacterial infections in agricultural systems in the US and UK. Mycobacterial infections, including Mycobacterium avium subsp. paratuberculosis (MAP) and M. bovis (bovine tuberculosis or bTB), are usually studied in simple one-host systems. Mycobacterial disease is extremely difficult to control due to long latent periods, poor diagnostic sensitivity, wildlife and environmental reservoirs of infection, and heterogeneous strain infectiousness. The key to controlling these diseases is believed to be an integrated approach to understand the pathways through which pathogen transmission occurs at all levels in an ecosystem: within animals, between individual animals, between livestock and wildlife, and between livestock and the environment. A generalized, multi-scale methodology is proposed for determining transmission dynamics using WGS data and phylodynamics. These dynamics will be incorporated into predictive models of disease transmission and control within an economic framework, to help decision-makers make informed control choices.