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

用于预测疾病动态和公共卫生干预措施有效性的人员流动模型

• 批准号: 10228957
• 负责人: Derek A Cummings
• 金额: $ 70.63万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-09 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10228957
• 关键词: 2019-nCoV; Africa; Age; COVID-19; COVID-19 pandemic; Communicable Diseases; Communities; Contact Tracing; Country; Data; Data Collection; Data Set; Data Sources; Dengue; Dimensions; Disease; Disease Outbreaks; Ebola; Effectiveness; Endemic Diseases; Epidemic; Epidemiology; Foundations; Frequencies; Future; Genetic; Health Policy; Household; Human; Incidence; Income; Individual; Infection; Intervention; Investigation; Malaria; Mathematics; Methodology; Methods; Modeling; Molecular; Monitor; Movement; Pathway interactions; Pattern; Population; Population Study; Process; Property; Public Health; Research; Research Project Grants; Resources; Route; Rural; Source; Standardization; Statistical Methods; Structure; Testing; Thailand; Time; Travel; Validation; Variant; Work; ZIKA; base; data modeling; disease transmission; epidemiologic data; flexibility; human model; improved; individual variation; infectious disease model; life history; low and middle-income countries; multiple datasets; novel; pandemic influenza; pathogen; performance tests; public health intervention; response; simulation; sociodemographics; transmission process; urban area

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.

项目总结/摘要 人的流动性是传染病传播的基础，并决定了传染病传播的时空动态。 暴发和地方病动态。然而，我们不知道如何最好地将个人或 人口流动模式转化为传染病模型。人类旅行已成功纳入 用于规划、监测和应对流感大流行的模型中，COVID-19 流行病、疟疾和其他疾病。然而，很少验证或比较这些模型中使用的方法， 被执行。此外，还没有系统的调查，在多大程度上，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 与疾病进程有关。全球现有的少量人类流动数据需要 将一个数据集的特征概括或外推到另一个设置、时间或环境。这 一般化可能对病原体子集或传播途径的病原体的某些特征起作用， 在其他方面可能会惨败。所有的旅行模式不太可能与所有类型的疾病相关。生命 每种病原体的历史、传播途径、发病年龄结构和暴发背景都将决定 特殊类型的运动。流动性数据可用于规划疫情和监测 干预措施，利用流动性数据和模型的传播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）。这里我们 建议对现有的移动数据和模型进行首次系统分析，以确定哪些模型 在使用一系列模拟和历史疫情数据的多个假设下表现最佳。我们将 还可以识别广义模型或非本地数据具有误导性的情况。为此，我们将整理 并且标准化通过各种方法收集的大量移动性数据集。我们将从统计学上 表征这些数据集，以确定个人，家庭， 社区，更大的规模。我们将开发多个描述移动性的候选模型， 这些候选模型转化为一系列常用的传染病传播模型。程序 原则是，人类的流动性只有在提高我们的能力， 概括观察到的疫情动态，我们将测试这些候选人的能力， 用于解释在登革热暴发中观察到的接触模式和病原体排序的模型， 寨卡、埃博拉和COVID-19。在这样做的过程中，我们将确定在哪些条件下，人类的流动性可以改善 我们对传播和病原体的理解，为应对战略提供信息，并创造一种资源， 可以为应对当前和未来的多重疫情提供信息。