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: 10434915
• 负责人: Sean Michael Moore
• 金额: $ 19.56万
• 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 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10434915
• 关键词: 2019-nCoV; Accounting; Address; Affect; Agreement; Behavior; Behavioral; Businesses; COVID-19; COVID-19 impact; 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; public database; 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.

项目摘要 SARS-CoV-2的快速传播导致地球仪各国实施了强有力的社交距离， 采取封锁措施减少传播。2019年新报告的COVID-19病例和死亡人数的第一个高峰 美国发生在2020年4月，但阳性检测数量在6月再次开始增加， 尽管疫情仍在传播，但许多州已开始放松最初的就地安置命令。在没有 广泛部署有效的疫苗或其他药物干预，州和地方 各国政府将不得不依靠一系列非药物干预措施（NPI）来限制进一步的疫情爆发 在接下来的12-24个月里。为决策者提供关于以下方面的有效性的可操作信息： 在一系列现实的流行病学背景下，我们将研究不同NPI的影响 使用地理上现实的、基于代理人的模型对流动模式和疾病传播进行研究。第一、 我们将收集一个全面的数据库，其中包括与COVID-19相关的地方、县和州政策， 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，检测， 住院和死亡率数据，以评估不同的县和州的政策和NPI在真实的- 时间 我们将使用手机获得的移动数据评估NPI和SARS-CoV-2动态之间的联系， 公开来源和与几个数据提供者的数据共享协议的组合。一是 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 和关于流行病学相关的人类流动和活动模式的国家绩效指标，包括不同国家和地区的活动数据。 受COVID-19相关特定限制的景点。然后，将使用这些移动性指标 以告知我们基于代理的传播模型中当地接触模式的变化。这种传输模式 还将纳入有关人口统计学、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 使用从手机数据导出的区域连通性度量进行关联。这些细节的结合将允许 我们估计的影响，不同的政策对传输动态在一系列设置，而会计 以适应当地条件和区域动态。模型估计值将每周迭代更新 在该项目的过程中，提供基于感染、住院和死亡的短期预测， 目前全国各地的非营利组织的组合。这些预测将用于验证我们的NPI影响 通过将预测与未来观测进行比较来估计。

项目成果

SPRC19: A Database of State Policy Responses to COVID-19 in the United States.

• DOI: 10.1038/s41597-023-02343-4
• 发表时间: 2023-08-07
• 期刊: SCIENTIFIC DATA
• 影响因子: 9.8
• 作者: Boehmke, Frederick J.;Desmarais, Bruce A.;Eastman, Abbie;Grassel, Isabelle;Harden, Jeffrey J.;Harper, Samuel;Kaboli, Liam;Ko, Hyein;Oster, Elisabeth;Saunders, Tracee M.
• 通讯作者: Saunders, Tracee M.

Nursing home quality, COVID-19 deaths, and excess mortality.

• DOI: 10.1016/j.jhealeco.2022.102592
• 发表时间: 2022-03
• 期刊: Journal of health economics
• 影响因子: 3.5
• 作者: Cronin CJ;Evans WN
• 通讯作者: Evans WN