Brouwer Andrew F, Weir Mark H, Eisenberg Marisa C, Meza Rafael, Eisenberg Joseph N S
Department of Epidemiology, University of Michigan, Ann Arbor, MI, United States of America.
Division of Environmental Health Sciences, The Ohio State University, Columbus, OH, United States of America.
PLoS Comput Biol. 2017 Apr 7;13(4):e1005481. doi: 10.1371/journal.pcbi.1005481. eCollection 2017 Apr.
Environmentally mediated infectious disease transmission models provide a mechanistic approach to examining environmental interventions for outbreaks, such as water treatment or surface decontamination. The shift from the classical SIR framework to one incorporating the environment requires codifying the relationship between exposure to environmental pathogens and infection, i.e. the dose-response relationship. Much of the work characterizing the functional forms of dose-response relationships has used statistical fit to experimental data. However, there has been little research examining the consequences of the choice of functional form in the context of transmission dynamics. To this end, we identify four properties of dose-response functions that should be considered when selecting a functional form: low-dose linearity, scalability, concavity, and whether it is a single-hit model. We find that i) middle- and high-dose data do not constrain the low-dose response, and different dose-response forms that are equally plausible given the data can lead to significant differences in simulated outbreak dynamics; ii) the choice of how to aggregate continuous exposure into discrete doses can impact the modeled force of infection; iii) low-dose linear, concave functions allow the basic reproduction number to control global dynamics; and iv) identifiability analysis offers a way to manage multiple sources of uncertainty and leverage environmental monitoring to make inference about infectivity. By applying an environmentally mediated infectious disease model to the 1993 Milwaukee Cryptosporidium outbreak, we demonstrate that environmental monitoring allows for inference regarding the infectivity of the pathogen and thus improves our ability to identify outbreak characteristics such as pathogen strain.
环境介导的传染病传播模型提供了一种机械方法，用于研究针对疫情的环境干预措施，如水处理或表面去污。从经典的SIR框架向纳入环境因素的框架转变，需要将暴露于环境病原体与感染之间的关系进行编码，即剂量反应关系。许多表征剂量反应关系功能形式的工作都采用了对实验数据的统计拟合。然而，在传播动力学背景下，很少有研究探讨功能形式选择的后果。为此，我们确定了在选择功能形式时应考虑的剂量反应函数的四个属性：低剂量线性、可扩展性、凹性以及它是否为单击中模型。我们发现：i）中高剂量数据并不限制低剂量反应，并且给定数据下同样合理的不同剂量反应形式可能导致模拟疫情动态的显著差异；ii）将连续暴露汇总为离散剂量的方式选择会影响建模的感染力；iii）低剂量线性凹函数允许基本再生数控制全局动态；iv）可识别性分析提供了一种管理多种不确定性来源并利用环境监测推断传染性的方法。通过将环境介导的传染病模型应用于1993年密尔沃基隐孢子虫疫情，我们证明环境监测有助于推断病原体的传染性，从而提高我们识别疫情特征（如病原体菌株）的能力。
