Human mobility models to forecast disease dynamics and the effectiveness of public health interventions

项目摘要/摘要 人类的流动性是传染病传播的基础，并决定了 暴发和地方病动态。然而，我们不知道如何最好地将个人或 将人口流动模式转化为传染病模型。人类旅行已经成功地被纳入 转变为用于规划、监测和应对流感大流行的模型，新冠肺炎 大流行、疟疾和其他疾病。然而，几乎没有对这些模型中使用的方法进行验证或比较 已经完成了。此外，还没有系统地调查许多不同的 现有的人类旅行数据来源量化了旅行模式，或者说对人类流动性的描述最多 与疾病过程相关的。全球可用的少量人员流动性数据需要 将一个数据集的特征概括或外推到另一个环境、时间或环境。这 对于病原体的子集或传播途径，泛化可能适用于病原体的某些特征，但是 可能在其他人身上失败得很惨。不太可能所有的旅行模式都与所有类型的疾病相关。《生活》 每种病原体的病史、传播途径、发病年龄结构和疫情背景都将决定 特定运动类型的重要性。使移动性数据在规划疫情和监测时有用 干预措施、利用流动数据的传播模型和模型必须面对流行病学 来自各种来源的数据(包括接触者追踪、传统监测和遗传数据)。在这里，我们 建议对现有的移动性数据和模型进行首次系统分析，以确定哪些模型 使用一系列模拟和来自历史疫情的数据，在多种假设下表现最好。我们会 还要确定广义模型或非本地数据具有误导性的情况。要做到这一点，我们将整理 并对通过各种方法收集的大量移动数据集进行标准化。我们将在统计上 确定这些数据集的特征，以确定个人、家庭 社区，以及更大的规模。我们将开发多个描述移动性的候选模型，并将 这些候选模型被转化为一系列常用的传染病传播模型。继续进行 其原理是，人类的流动性只有在提高我们的能力的情况下才对传染病模型有用 概括一下观察到的疫情动态，我们将测试这些候选疫情的每一个的流动性 解释观察到的接触模式和登革热暴发中观察到的病原体序列的模型， 寨卡病毒、埃博拉病毒和新冠肺炎。在这样做的过程中，我们将确定可以改善人类流动性的条件。 我们对传播和病原体的了解，告知应对策略，并创建一种资源， 可以为应对当前和未来的多起疫情提供信息。

PROJECT SUMMARY/ABSTRACT Human mobility underlies infectious disease transmission and determines the spatial-temporal dynamics of outbreaks and endemic disease dynamics. Yet, we do not understand how best to incorporate individual or population mobility patterns into models of infectious diseases. Human travel has been successfully incorporated into models used for planning, surveillance, and reactive responses to influenza pandemics, the COVID-19 pandemic, malaria, and others. However, little validation or comparison of approaches used in these models has been performed. Further, there has been no systematic investigation of the extent to which the many different existing sources of human travel data quantify travel patterns, or which descriptions of human mobility are most relevant to disease processes. The small amount of human mobility data available globally requires generalization or extrapolation of features of one dataset to another setting, time or circumstance. This generalization may work for some features of pathogens for a subset of pathogens or transmission routes but may fail miserably in others. It is unlikely that all travel patterns are relevant for all types of diseases. The life history of each pathogen, transmission routes, age structure of incidence and outbreak context will all dictate the importance of specific types of movement. For mobility data to be useful in planning for outbreaks and monitoring interventions, transmission models utilizing mobility data and models must be confronted with epidemiological data (including contact tracing, traditional surveillance, and genetic data) from a variety of sources. Here, we propose to perform the first systematic analysis of existing mobility data and models to identify which models perform best under multiple assumptions using a range of simulations and data from historic outbreaks. We will also identify circumstances when generalized models or non-local data are misleading. To do this, we will collate and standardize a large number of mobility datasets collected by various methods. We will statistically characterize these datasets to identify sources of variation in human mobility at individual, household, community, and larger scales. We will develop multiple candidate models describing mobility and incorporate these candidate models into a range of commonly used models of infectious disease transmission. Proceeding with the principle that human mobility is only useful to models of infectious diseases if it improves our ability to recapitulate the dynamics of observed outbreaks, we will test the ability of each of these candidate mobility models to explain observed patterns of contacts and sequenced pathogens observed in outbreaks of dengue, Zika, Ebola, and COVID-19. In doing this, we will identify conditions under which human mobility can improve our understanding of the transmission and pathogens, inform response strategies and create a resource that can inform responses to multiple current and future outbreaks.