RAPID: A Controlled Response to COVID-19

流行病的预测和控制是公共卫生的中心主题，在文献中通过建模和模拟得到了充分的研究。就COVID-19而言，缺乏疫苗和对治疗的有限反应可能导致急性呼吸道并发症，但如果患者能够在配备通气机的重症监护病房获得呼吸支持，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对大流行的反应。即确定可操作的策略，使医院的响应系统达到均衡状态，使感染发生扩散，但扩散速度不会导致服务溢出。人们普遍认识到有必要“拉平”代表感染传播的曲线，世界各国政府已经实施了不同的战略来实现这一目标。在这种背景下，该项目的研究可以被视为一种原则性的方法，以预测“曲线”如何对不同的策略做出反应，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。考虑到由医院容量决定的服务率约束和描述感染传播的动态方程，该项目旨在推导出以公共卫生政策表示的控制政策，该政策既满足当地卫生保健系统的服务率约束，又将其经济影响降至最低。由于社会制约因素可能使这种最优战略成本过高或不可行，因此，本项目还将研究在给定一定成本预算的情况下，实现最理想（时变）到达率，从而使全球人口死亡率降到最低的战略。虽然推导出的解决办法在所提议的动力系统模型中预期是最佳的，但结果也将通过由实际人口数据驱动的广泛模拟加以验证，以便证明它们在实际环境中更广泛的适用性。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 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.