RAPID: Collaborative Research: Mitigation and Suppression of Coronavirus Pandemic with Data-driven RAPID Decisions Using COVID-19 Simulator

RAPID：协作研究：使用 COVID-19 模拟器通过数据驱动的 RAPID 决策缓解和抑制冠状病毒大流行

• 批准号: 2035360
• 负责人: Turgay Ayer
• 金额: $ 9.2万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2035360&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Research; Mitigation; Suppression

Coronavirus disease 2019 (COVID-19), a global pandemic, has affected every sector of human life. While our understanding of the spread of COVID-19 is still evolving, policymakers need to make strategic decisions amidst this uncertainty to mitigate the pandemic. Appropriate policy decisions can reduce morbidity, mortality, and damage to the healthcare system. This study will estimate the underlying prevalence of COVID-19 at the county level and project future trajectories of COVID-19 under various sequences of non-pharmaceutical and pharmaceutical interventions. In addition, these new estimations and projections will be incorporated into our existing online COVID-19 Simulator (www.covid19sim.org) to inform county-level decisions. In addition, the COVID-19 Simulator will detect early signs of community-level COVID-19 outbreaks and predict hotspots in different jurisdictions. From a societal perspective, this research will improve our understanding of COVID-19 transmission and inform policies to mitigate the spread of the virus, ultimately leading to a reduction in COVID-19 disease burden.This research will use epidemiological and operations research modeling approaches to simulate the spread of COVID-19 at the county level and the effects of different interventions on mitigation of COVID-19. In addition, causal decision tree and mixed-integer programing-based machine learning algorithms will be used to identify county level hotspots of transmission. The proposed research will not only inform key decisions with important public health implications, but also contribute to intellectual merits, including 1) linking and using various datasets in near real-time to improve our understanding of disease epidemiology, 2) estimating the underlying prevalence of COVID-19 and projecting future trajectories under various interventions, 3) developing innovative approaches for real time parameter estimations, model calibrations, and projections, and (4) detecting early signs of community-level COVID-19 outbreaks using epidemiological, statistical and machine learning based modeling techniques.This RAPID award is made by the Ecology and Evolution of Infectious Diseases Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) ActThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2019冠状病毒病（COVID-19）是一种全球性大流行病，影响了人类生活的各个方面。尽管我们对COVID-19传播的理解仍在不断发展，但政策制定者需要在这种不确定性中做出战略决策，以缓解疫情。适当的政策决定可以减少发病率、死亡率和对医疗保健系统的损害。该研究将估计COVID-19在县级的潜在患病率，并预测COVID-19在各种非药物和药物干预措施下的未来发展轨迹。此外，这些新的估计和预测将被纳入我们现有的在线COVID-19模拟器（www.covid19sim.org），为县级决策提供信息。此外，COVID-19模拟器将检测社区层面COVID-19爆发的早期迹象，并预测不同司法管辖区的热点。从社会的角度来看，这项研究将提高我们对COVID-19传播的理解，并为减缓病毒传播的政策提供信息，最终导致COVID-19疾病负担的减轻。这项研究将使用流行病学和运筹学建模方法来模拟COVID-19在县级的传播以及不同干预措施对COVID-19缓解的影响。此外，因果决策树和基于混合整数排序的机器学习算法将用于识别县级传播热点。拟议的研究不仅将为具有重要公共卫生影响的关键决策提供信息，还将有助于实现知识价值，包括1）近实时地链接和使用各种数据集，以提高我们对疾病流行病学的理解，2）估计COVID-19的潜在患病率，并预测各种干预措施下的未来轨迹，3）开发用于真实的时间参数估计的创新方法，模型校准和预测，以及（4）使用流行病学，统计和基于机器学习的建模技术检测社区级COVID-19爆发的早期迹象。该RAPID奖由环境生物学部传染病生态学和进化计划颁发，使用冠状病毒援助，救济，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

• DOI: 10.1073/pnas.2113561119
• 发表时间: 2022-04-12
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1

PIN68 COVID-19 Simulator: An Interactive Tool to Inform COVID-19 Intervention Policy Decisions in the United States

• DOI: 10.1016/j.jval.2020.08.909
• 发表时间: 2020-12-11
• 期刊: Value in Health
• 影响因子: 4.5
• 作者: Chhatwal J;Dalgic O;Mueller P;Adee M;Xiao Y;Ladd MA;Linas BP;Ayer T
• 通讯作者: Ayer T