RAPID: Optimizing the Life-Cycle Impacts of COVID-19 Policy Interventions

RAPID：优化 COVID-19 政策干预的生命周期影响

• 批准号: 2032465
• 负责人: Philip Brown
• 金额: $ 12.32万
• 依托单位: University of Colorado at Colorado Springs
• 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: 2020-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2032465&HistoricalAwards=false
• 关键词: RAPID; Optimizing; Life; Cycle; Impacts

As COVID-19 spreads throughout communities, government officials face a series of challenging decisions, each fraught with an array of difficult tradeoffs. Current efforts to stop the spread of the novel coronavirus by closing non-essential businesses have resulted in the loss of millions of jobs. In addition to the economic costs, lockdown measures have had other unintended consequences that are difficult to measure. Unquestionably, the suffering caused by COVID-19 will go far beyond clinical effects alone. However, if no measures are taken, the virus may spread rapidly, overwhelming local healthcare resources and causing substantial human loss. This raises the urgent question: How can leaders make public policy decisions regarding the COVID-19 pandemic in a scientific way that is locally appropriate and properly accounts for both near-term and longer-term costs of policy interventions? This project combines rigorous mathematical modeling, innovative approaches to data collection, and input from policymakers, to develop a decision aid framework that weighs the costs and benefits of various policy interventions at a local level and tailors interventions to the locale considering the effects of specific indicators such as urbanization, economic distress, and availability of regional healthcare. Additionally, graduate students will be trained in the context of this research. To develop a rigorous understanding of the tradeoffs involved in policy interventions geared at mitigating COVID-19, two complementary approaches will be applied. First, the benefits of policy interventions will be estimated using a new dynamical game-theoretic mathematical COVID-19 epidemic model which accounts for the interactions between social behavior, policy interventions, and disease contagion. Second, the costs of policy interventions will be estimated via geospatial data aggregation and analysis which identifies local vulnerability to unintended consequences of policy interventions and assesses the disparities of these impacts across racial and socioeconomic divides. This project will advance knowledge in two complementary directions. First, the project’s data collection and aggregation activities will create datasets which would be otherwise impossible to recreate if this historic opportunity were missed; these datasets will facilitate our understanding of the connection between the spread of COVID-19 and the emergence of social behavior, identify the long-term costs of infection, and are anticipated to be applicable to future pandemics as well. Second, the project’s game-theoretic epidemiological model captures the feedback interconnection between two dynamical systems (human behavior and disease spread, respectively). These systems and their abstractions have been well-studied in isolation, but their interconnection is not well understood. Mathematical models will be reviewed and revised based on feedback from policymakers in order to best tailor interventions to the locale considering the effects of specific indicators such as urbanization, economic distress, and availability of regional healthcare. This interdisciplinary project will significantly advance understanding of these inherent societal feedback effects in a data driven, real world context. This RAPID award is made by the Ecology and Evolution of Infectious Disease Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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.

随着新冠肺炎在各个社区的传播，政府官员面临着一系列具有挑战性的决定，每个决定都充满了艰难的取舍。目前通过关闭非必要企业来阻止新型冠状病毒传播的努力已导致数百万人失业。除了经济成本，封锁措施还产生了其他难以衡量的意想不到的后果。毫无疑问，新冠肺炎造成的痛苦将远远超出临床效果。然而，如果不采取措施，病毒可能会迅速传播，使当地医疗资源不堪重负，并造成大量人员损失。这提出了一个紧迫的问题：领导人如何以科学的方式做出有关新冠肺炎疫情的公共政策决策，既适合当地，又能正确考虑政策干预的短期和长期成本？该项目结合了严格的数学建模、数据收集的创新方法和政策制定者的意见，以开发一个决策援助框架，该框架权衡地方层面各种政策干预的成本和收益，并考虑城市化、经济困境和地区医疗保健可获得性等特定指标的影响，针对当地定制干预措施。此外，研究生将在这项研究的背景下接受培训。为了严格理解旨在缓解新冠肺炎的政策干预所涉及的权衡，将采用两种互补的方法。首先，将使用一个新的动态博弈论数学新冠肺炎流行病模型来估计政策干预的好处，该模型考虑了社会行为、政策干预和疾病传播之间的相互作用。第二，将通过地理空间数据汇总和分析来估计政策干预的成本，以确定当地易受政策干预意外后果的影响，并评估这些影响在种族和社会经济差异方面的差异。该项目将在两个相辅相成的方向上推进知识。首先，项目的数据收集和汇总活动将创建如果错过这一历史性机遇就不可能重新创建的数据集；这些数据集将有助于我们了解新冠肺炎的传播和社会行为出现之间的联系，确定感染的长期成本，并预计也将适用于未来的流行病。其次，该项目的博弈论流行病学模型捕捉到了两个动力系统(分别是人类行为和疾病传播)之间的反馈相互联系。这些系统及其抽象已经孤立地得到了很好的研究，但它们的相互联系并没有被很好地理解。将根据政策制定者的反馈对数学模型进行审查和修订，以便考虑城市化、经济困境和地区医疗保健可获得性等特定指标的影响，最好地为当地量身定做干预措施。这一跨学科项目将在数据驱动的真实世界背景下显著促进对这些固有的社会反馈效应的理解。这一快速奖项是由环境生物学部门传染病生态学和进化计划利用冠状病毒援助、救济和经济安全(CARE)法案的资金颁发的。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。

项目成果

Individual Altruism Cannot Overcome Congestion Effects in a Global Pandemic Game

个人利他主义无法克服全球流行病博弈中的拥堵效应

• DOI: 10.1109/allerton49937.2022.9929384
• 发表时间: 2022
• 期刊: and Computing
• 作者: Brown, Philip N.;Collins, Brandon;Hill, Colton;Barboza, Gia;Hines, Lisa
• 通讯作者: Hines, Lisa

A spatiotemporal analysis of the impact of COVID-19 on child abuse and neglect in the city of Los Angeles, California

• DOI: 10.1016/j.chiabu.2020.104740
• 发表时间: 2021-05-24
• 期刊: CHILD ABUSE & NEGLECT
• 影响因子: 4.8
• 作者: Barboza, Gia E.;Schiamberg, Lawrence B.;Pachl, Layne
• 通讯作者: Pachl, Layne

Robust Stochastic Stability in Dynamic and Reactive Environments

动态和反应环境中的鲁棒随机稳定性

• DOI: 10.1109/cdc45484.2021.9683342
• 发表时间: 2021
• 期刊: 60th IEEE Conference on Decision and Control (CDC
• 作者: Collins, Brandon C.;Hines, Lisa;Barboza, Gia;Brown, Philip N.
• 通讯作者: Brown, Philip N.