RAPID: A Controlled Response to COVID-19

RAPID：对 COVID-19 的可控反应

• 批准号: 2028036
• 负责人: Massimo Franceschetti
• 金额: $ 20万
• 依托单位: University of California-San Diego
• 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: 2020-05-01 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028036&HistoricalAwards=false
• 关键词: RAPID; Controlled; Response; COVID; 19

The forecast and containment of epidemics are central themes in public health and are well-studied in the literature through modeling and simulations. In the case of COVID-19, the lack of a vaccine, and the limited response to treatment, can lead to possible acute respiratory complications that may nevertheless be non-lethal, provided that patients have access to respiratory support in intensive-care units equipped with ventilation machines. Proactive planning and optimal allocation of resources are key challenges to face in the current emergency. In this scenario, the project studies the response to the pandemic from a network service point of view. Namely, it aims at determining actionable strategies under which the hospitals' response system can reach an equilibrium state where the spreading of the infection occurs, but the spreading rate does not lead to service overflow. There has been widespread recognition on the need to “flatten the curve,” representative of the infection spreading, and different strategies to achieve this objective have been enforced by governments across the world. The project’s research can be viewed in this context as a principled approach to predict how “the curve” will respond to different strategies, whether it can be kept below the hospitals' saturation threshold for a given amount of time, and what is the societal cost required to achieve this objective. Given a service-rate constraint dictated by hospital capacity and dynamic equations describing the spread of the infection, the project aims at deriving a control policy expressed in terms of public-health policies that meets the-service rate constraint of the local health-care system while minimizing their economic impact. Since societal constraints can make such an optimal strategy cost-prohibitive, or infeasible, the project will also study strategies that, given a certain cost budget, lead to the most desirable (time-varying) arrival rate that minimizes the global mortality rate of the population. While derived solutions are expected to be optimal in the context of the proposed dynamical-system model, results will also be validated using extensive simulations driven by real population data, in order to demonstrate their wider applicability in a practical setting.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.

流行病的预测和遏制是公共卫生的中心主题，在文献中通过建模和模拟进行了充分研究。 在COVID-19的情况下，缺乏疫苗和对治疗的有限反应可能导致可能的急性呼吸系统并发症，但只要患者能够在配备呼吸机的重症监护室获得呼吸支持，这些并发症可能是非致命的。积极规划和优化资源分配是当前紧急情况下面临的主要挑战。 在这种情况下，该项目从网络服务的角度研究应对大流行病的措施。也就是说，它的目的是确定可操作的策略下，医院的反应系统可以达到一个平衡状态，在感染的传播发生，但传播速度不会导致服务溢出。人们已经广泛认识到需要“使曲线变平”，这代表了感染传播，世界各国政府已经实施了实现这一目标的不同策略。在这种情况下，该项目的研究可以被视为一种原则性的方法，以预测“曲线”将如何对不同的策略作出反应，它是否可以在给定的时间内保持在医院的饱和阈值以下，以及实现这一目标所需的社会成本是多少。鉴于医院能力和描述感染传播的动态方程所决定的服务率限制，该项目旨在得出一个控制政策，表示在公共卫生政策，满足当地卫生保健系统的服务率限制，同时尽量减少其经济影响。由于社会制约因素可能使这种最佳战略成本过高或不可行，该项目还将研究在一定的成本预算下，导致最理想的（随时间变化的）到达率，从而最大限度地减少人口的全球死亡率的战略。 虽然推导出的解决方案预计将是最佳的背景下提出的动态系统模型，结果也将使用广泛的模拟驱动的真实的人口数据进行验证，以证明其在实际setting.This奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。