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: 10599117
• 负责人: Derek A Cummings
• 金额: $ 64.54万
• 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/10599117
• 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; 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; data modeling; disease transmission; epidemiologic data; flexibility; future outbreak; 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.

项目摘要/摘要 人类流动性是传染病传播的基础，并确定 爆发和特有疾病动态。但是，我们不明白如何最好地合并个人或 人口流动模式进入传染病模型。人类旅行已成功合并 进入用于计划，监视和对流感大流感的反应响应的模型，Covid-19 大流行，疟疾等。但是，几乎没有验证或比较这些模型中使用的方法 已执行。此外，还没有系统地调查许多不同的程度 人类旅行数据的现有来源量化旅行模式，或者对人类流动性的描述最多 与疾病过程有关。全球可用的少量人类流动数据需要 将一个数据集的特征概括或推断到另一个设置，时间或情况。这 对于病原体或传播路线的子集，概括可能适用于某些病原体的特征，但是 可能会在其他人中惨败。所有旅行模式都不太可能与所有类型的疾病有关。生活 每种病原体的历史，传播路线，入射率和爆发环境的年龄结构都将决定 特定类型运动的重要性。使移动性数据可用于计划爆发和监视 干预措施，利用移动性数据和模型的传输模型必须面对流行病学 来自各种来源的数据（包括接触跟踪，传统监视和遗传数据）。在这里，我们 建议对现有移动性数据和模型进行首次系统分析，以确定哪些模型 使用一系列模拟和历史暴发的数据，在多个假设下执行最佳。我们将 还要确定广义模型或非本地数据误导的情况。为此，我们将整理 并标准化大量通过各种方法收集的移动性数据集。我们将从统计上 特征这些数据集以确定个人，家庭中人类流动性变异的来源， 社区和更大的规模。我们将开发多个描述移动性并合并的候选模型 这些候选模型进入了一系列常用的传染病传播模型。程序 以这样的原则，即人类流动性仅对传染病模型有用，如果它提高了我们的能力 概述观察到的爆发的动态，我们将测试这些候选迁移率的每种能力 解释在登革热暴发中观察到的观察到的接触模式和测序病原体的模型， Zika，Ebola和Covid-19。为此，我们将确定人类流动性可以改善的条件 我们对传播和病原体的理解，为响应策略提供信息，并创建一个资源 可以告知对多次当前和未来爆发的回应。