RAPID: Planning for the present and future management of COVID-19

COVID-19的出现和迅速传播在短短几个月内改变了我们的世界。在一年的时间里，与covid相关的疾病夺去了全球200多万人的生命，美国有40多万人死亡。此次疫情暴露了我们医疗体系的严重脆弱性，并在设备、疫苗、检测和其他资源的分配方面带来了前所未有的后勤挑战。此外，决策者发现自己在非治疗性干预措施的时机和严重程度方面面临困难的选择，这些干预措施可以减缓疾病的传播，但也会对经济和下游健康产生负面影响。最优的自适应决策策略使用累积数据来告知应该如何在空间和时间上分配资源，从而在控制成本的同时最大化累积健康状况。在这种情况下，最优决策策略有可能挽救数以万计的生命，更有效地利用资源，并减少负面的经济影响。这一研究项目开发了新的方法，利用包括人员流动数据、空中交通以及疾病传播和疫苗传播信息在内的异构数据流来估计最佳决策战略。该项目还为学生和一名博士后提供培训机会。该研究项目通过将行为改变纳入随机区隔模型，推进了统计和数学疾病模型。多种数据流的整合，包括人员流动、人口普查、发病率和检测报告、疫苗接种传播和航空旅行，使模型比使用单一数据模式的模型更丰富、更现实。此外，与人口普查数据相关联的流动性数据将产生关于异质性的新知识，以应对诸如跨人口统计学的就地安置等干预措施。这些模型被整合到新的时空强化学习方法中，用于估计最佳决策策略，该策略建议如何在多个决议（例如，地方和国家）中分配资源和非治疗性干预措施，以最大限度地减少受经济和其他下游影响限制的疾病传播。该奖项反映了美国国家科学基金会的法定使命，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。

The emergence and rapid spread of COVID-19 has transformed our world in a matter of months. In the course of a year, COVID-related illness has claimed the lives of more than 2 million people across the globe and more than 400,000 people in the United States. The outbreak has exposed critical vulnerabilities in our healthcare system and created unprecedented logistical challenges in the distribution of equipment, vaccines, tests, and other resources. Furthermore, policymakers have found themselves facing difficult choices regarding the timing and severity of non-therapeutic interventions which slow the spread of disease but also incur negative economic and downstream health impacts. An optimal adaptive decision strategy uses accumulating data to inform how resources should be allocated over space and time to maximize cumulative health while controlling cost. In this context, an optimal decision strategy has the potential to save tens of thousands of lives, use resources more efficiently, and reduce negative economic impacts. This research project develops new methodologies for estimation of optimal decision strategies using heterogeneous data streams including human mobility data, air traffic, as well as information on disease spread and vaccine dissemination. This project also provides training opportunities for students and a postdoctoral scholar. This research project advances statistical and mathematical disease models by including behavior-change into stochastic compartmental models. The integration of multiple data streams including human-mobility, the census, incidence and testing reports, vaccination dissemination, and air-travel allows for richer and more realistic models than those fit using a single data modality. Furthermore, mobility data linked with census data will generate new knowledge about heterogeneity in response to interventions such as shelter-in-place across demographics. These models are integrated into novel spatio-temporal reinforcement learning methods for estimation of an optimal decision strategy that recommends how resources and non-therapeutic interventions should be allocated across multiple resolutions (e.g., locally and nationally) to minimize disease spread subject to constraints on economic and other downstream impacts.This 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.