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的迅速传播导致全球各国实施强大的社会疏远和 锁定措施减少传输。新报告的Covid-19案件和死亡的第一个高峰 美国发生在2020年4月，但积极测试的数量在6月再次增加，因为 尽管持续传播，许多州仍开始放松他们最初的庇护所。在没有 广泛部署有效的疫苗或其他药物干预措施，州和地方 政府将不得不依靠一系列非药品干预措施（NPI）来限制进一步的爆发 在接下来的12-24个月中。为政策制定者提供有关功效的可行信息 在一系列现实的流行病学环境下，不同的NPI，我们将研究不同NPI的影响 使用地理上现实的，基于代理的模型在移动性模式和疾病传播上。第一的， 我们将组装与Covid-19的本地，县和州政策的全面数据库 公共网站和社交媒体，并根据干预类型对这些政策进行分类。我们还将获得 来自几个不同公共数据库的流行病学数据，并使用县级案例，测试， 住院以及死亡率数据，以评估不同县和州政策以及NPI对现实的影响 时间。 我们将使用手机衍生的移动性数据来评估NPI和SARS-COV-2动力学之间的链接 与几个数据提供商的公开来源和数据共享协议的结合。首先，我们 将使用统计模型来评估县和州共同199政策的不同类别的影响 和NPI关于流行病学与人类流动性和活动模式，包括不同的活动数据 利益的场所受特定的共同限制受到特定的限制。然后将使用这些移动性指标 在我们的基于代理的传输模型中告知本地接触模式的变化。此传输模型 还将纳入有关人口统计，社会经济因素，合并症和详细信息 已证明对SARS-COV-2流行病学很重要的职业。当地人口将是 使用从手机数据得出的区域连接指标链接。这些细节的合并将允许 我们在会计期间估计不同政策对一系列设置中传输动态的影响 对于当地条件以及区域动力学。模型估算将每周迭代更新 在整个项目的过程中，提供有关感染，住院和死亡的短期预测 全国NPI当前的组合。这些预测将用于验证我们的npi Impact 通过将预测与未来观察结果进行比较来估计。

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