Dose-response relationships for environmentally mediated infectious disease transmission models.
Dose-response relationships for environmentally mediated infectious disease transmission models.
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DOI：
10.1371/journal.pcbi.1005481
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发表时间：
2017-04
影响因子：
4.3
通讯作者：
Eisenberg JNS
中科院分区：
文献类型：
--
作者：
Brouwer AF;Weir MH;Eisenberg MC;Meza R;Eisenberg JNS
环境介导的传染病传播模型提供了一种机制的方法来检查环境干预爆发，如水处理或表面去污。从传统的SIR框架向纳入环境的框架的转变需要编纂暴露于环境病原体与感染之间的关系，即剂量-反应关系。大部分表征剂量-反应关系的函数形式的工作都使用了对实验数据的统计拟合。然而，很少有研究探讨的后果的功能形式的选择的背景下，传输动态。为此，我们确定了在选择函数形式时应考虑的剂量响应函数的四个属性：低剂量线性，可扩展性，可扩展性，以及它是否是一个单一的命中模型。我们发现，i）中剂量和高剂量数据不限制低剂量反应，并且在给定数据的情况下同样合理的不同剂量反应形式可能导致模拟爆发动态的显著差异; ii）如何将连续暴露聚合为离散剂量的选择可能影响建模的感染力; iii）低剂量线性凹函数允许基本繁殖数控制全局动态; iv）可识别性分析提供了一种管理多个不确定性来源的方法，并利用环境监测对传染性进行推断。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爆发，我们证明了环境监测允许推断病原体的感染性，从而提高了我们识别爆发特征，如病原体菌株的能力。许多传染病干预措施，包括水处理、手部卫生和表面去污，都以环境中的病原体为目标。在传播模型中对环境中病原体的浓度进行明确建模，不仅可以考虑这种缓解努力的影响，而且可以考虑病原体的空间传播和环境监测的采样策略，这是一种有用的方法。然而，我们需要了解剂量-反应关系，即接触病原体如何转化为感染概率。定量微生物风险评估领域已经根据实验数据开发了剂量-反应模型，但很少有工作评估剂量-反应模型的选择对传播模型动力学的影响。我们表明，动态的模拟传输模型，结合剂量-反应模型，已适合实验数据可以有很大的不同，尽管在拟合的实验剂量-反应数据的统计差异不大。这一结果和其他结果使我们能够在传输建模环境中使用剂量响应函数给出具体的指导。我们还强调环境介导的传播模型的有用性，通过演示环境监测数据如何可以用来提供有关病原体菌株的新信息。
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. Many infectious disease interventions, including water treatment, hand hygiene, and surface decontamination, target pathogens in the environment. Explicitly modeling the concentration of pathogens in the environment within transmission models can be a useful way to consider not only the impact of such mitigation efforts but also the spatial spread of pathogens and sampling strategies for environmental monitoring. However, we need to understand the dose–response relationship, that is, how exposure to pathogens translates into a probability of infection. The field of quantitative microbial risk assessment has developed dose–response models from experimental data, but little work has been done to assess the impact the choice of dose–response model has on transmission model dynamics. We show that dynamics of simulated transmission models incorporating a dose–response model that has been fit to experimental data can vary widely despite little statistical difference in the fit to the experimental dose–response data. This and other results allow us to give specific guidance for the use of dose–response functions in a transmission modeling context. We also underscore the usefulness of environmentally mediated transmission models by demonstrating how environmental monitoring data can be used to provide new information about pathogen strain.