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

冠状病毒病2019年(新冠肺炎)是一场全球性的大流行，已经影响到人类生活的方方面面。虽然我们对新冠肺炎传播的理解仍在发展，但政策制定者需要在这种不确定性中做出战略决策，以缓解这一流行病。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。这项研究将评估县级新冠肺炎的潜在流行率，并预测新冠肺炎在各种非药物和药物干预序列下的未来发展轨迹。此外，这些新的估计和预测将被纳入我们现有的在线新冠肺炎模拟器(www.covid19sim.org)，为县级决策提供参考。此外，新冠肺炎模拟器将检测社区层面新冠肺炎爆发的早期迹象，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新冠肺炎传播的理解，并为缓解病毒传播的政策提供信息，最终导致新冠肺炎疾病负担的减轻。本研究将使用流行病学和运筹学建模方法，模拟新冠肺炎在县级的传播以及不同干预措施对缓解新冠肺炎的影响。此外，还将使用因果决策树和基于混合整数规划的机器学习算法来识别县级传播热点。这项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，还将有助于知识优势，包括1)近乎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)估计新冠肺炎的潜在流行率并预测各种干预措施下的未来轨迹，3)开发实时参数估计、模型校准和预测的创新方法，以及(4)使用流行病学、统计学和基于机器学习的建模技术检测社区层面新冠肺炎爆发的早期迹象。这一快速奖项由环境生物学司传染病生态学和进化计划颁发，使用冠状病毒援助、救济和经济安全(CARE)法案的资金该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.