Assessing the impact of COVID-19 interventions on human mobility and SARS-CoV-2 transmission dynamics in the United States

评估 COVID-19 干预措施对美国人口流动和 SARS-CoV-2 传播动态的影响

• 批准号: 10288079
• 负责人: Sean Michael Moore
• 金额: $ 23.48万
• 依托单位: UNIVERSITY OF NOTRE DAME
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-18 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10288079
• 关键词: 2019-nCoV; Accounting; Address; Affect; Agreement; Behavior; Behavioral; Businesses; COVID-19; COVID-19 intervention; COVID-19 pandemic; Categories; Cellular Phone; Cessation of life; Characteristics; China; Cities; Complex; Country; County; Data; Data Analyses; Databases; Demographic Factors; Demography; Diagnostic tests; Disease; Disease Outbreaks; Economics; Education; Effectiveness; Employment; Epidemic; Epidemiology; Equilibrium; Future; Geographic Locations; Geography; Government; Hospitalization; Human; Incidence; Income; Individual; Infection; Influenza; Intervention; Lifting; Link; Local Government; Location; Measures; Modeling; Movement; Occupations; Pattern; Performance; Pharmacologic Substance; Policies; Policy Maker; Population; Population Density; Provider; Recovery; Reporting; Resolution; Restaurants; SARS-CoV-2 infection; SARS-CoV-2 transmission; Schools; Series; Serodiagnoses; Shelter facility; Social Distance; Social Impacts; Socioeconomic Factors; Source; State Government; Statistical Methods; Statistical Models; Testing; Time; United States; Update; Vaccines; base; comorbidity; data sharing; demographics; disease transmission; economic impact; epidemiologic data; interest; mortality; pandemic disease; response; simulation; social media; spatiotemporal; transmission process; web site

Project Summary The rapid spread of SARS-CoV-2 led countries across the globe to implement strong social distancing and lockdown measures to reduce transmission. The first peak of newly reported COVID-19 cases and deaths in the United States occurred in April 2020, but the number of positive tests began increasing again in June as many states started easing their initial shelter-in-place orders despite ongoing transmission. In the absence of widespread deployment of an effective vaccine or another pharmaceutical intervention, state and local governments will have to rely on a range of non-pharmaceutical interventions (NPIs) to limit further outbreaks over the next 12-24 months. To provide policy makers with actionable information regarding the efficacy of different NPIs under a range of realistic epidemiological contexts, we will examine the impact of different NPIs on both mobility patterns and disease transmission using a geographically realistic, agent-based model. First, we will assemble a comprehensive database of local, county, and state policies related to COVID-19 from public websites and social media and categorize these policies by intervention type. We will also obtain epidemiological data from several different publicly available databases and use county-level case, testing, hospitalization, and mortality data to assess the impact of different county and state policies and NPIs in real- time. We will assess the link between NPIs and SARS-CoV-2 dynamics using cell phone-derived mobility data from a combination of publicly available sources and data sharing agreements with several data providers. First, we will use statistical models to assess the impact of different categories of county and state COVID-19 policies and NPIs on epidemiologically-relevant human mobility and activity patterns, including activity data at different places-of-interest subject to particular COVID-19 related restrictions. These mobility metrics will then be used to inform changes in local contact patterns in our agent-based transmission model. This transmission model will also incorporate detailed information on the demographics, socioeconomic factors, co-morbidities, and occupations that have been shown to be important for SARS-CoV-2 epidemiology. Local populations will be linked using regional connectivity metrics derived from cell phone data. Incorporation of these details will allow us to estimate the impact of different policies on transmission dynamics in a range of settings while accounting for local conditions as well as regional dynamics. Model estimates will be iteratively updated on a weekly basis over the course of the project to provide short-term forecasts of infections, hospitalizations, and deaths based on the current mix of NPIs across the country. These forecasts will be used to validate our NPI-impact estimates by comparing forecasts to future observations.

项目概要 SARS-CoV-2 的迅速传播导致全球各国实施了强有力的社会隔离和 采取封锁措施减少传播。新报告的 COVID-19 病例和死亡人数首次出现高峰 美国发生在2020年4月，但阳性检测数量在6月份再次开始增加 尽管病毒仍在传播，许多州仍开始放松最初的就地避难令。在没有 州和地方广泛部署有效的疫苗或其他药物干预措施 各国政府将不得不依靠一系列非药物干预措施（NPI）来限制疫情进一步爆发 在接下来的 12-24 个月内。为政策制定者提供有关有效性的可操作信息 在一系列现实流行病学背景下的不同 NPI，我们将研究不同 NPI 的影响 使用地理上真实的、基于代理的模型来研究流动模式和疾病传播。第一的， 我们将收集与 COVID-19 相关的地方、县和州政策的综合数据库 公共网站和社交媒体，并按干预类型对这些政策进行分类。我们还将获得 流行病学数据来自几个不同的公开数据库，并使用县级病例、测试、 住院率和死亡率数据，以评估不同县和州政策以及 NPI 的实际影响 时间。 我们将使用来自手机的移动数据来评估 NPI 和 SARS-CoV-2 动态之间的联系 公开来源以及与多个数据提供商的数据共享协议的组合。首先，我们 将使用统计模型来评估不同类别的县和州 COVID-19 政策的影响 和流行病学相关的人员流动和活动模式的 NPI，包括不同地区的活动数据 受特定新冠肺炎 (COVID-19) 相关限制的名胜古迹。然后将使用这些移动性指标 通知我们基于代理的传播模型中本地联系模式的变化。此传动模型 还将纳入有关人口统计、社会经济因素、合并症和 已被证明对 SARS-CoV-2 流行病学很重要的职业。当地居民将 使用源自手机数据的区域连接指标进行链接。纳入这些细节将允许 我们在核算时估计不同政策对一系列环境下传播动态的影响 因地制宜以及区域动态。模型估计将每周迭代更新 在项目过程中提供感染、住院和死亡的短期预测 关于目前全国非营利机构的结构。这些预测将用于验证我们的 NPI 影响 通过将预测与未来观察进行比较来进行估计。