RAPID/Collaborative Research: Developing Pandemics and Healing Models for Coronavirus COVID-19 to Assist in Policy Making

目前的大流行刺激了地方、州和联邦政府的强烈反应，遏制在很大程度上是通过严格的封锁实现的，有效地隔离了全国几乎每个家庭。鉴于这种做法的巨大社会经济影响，必须了解如何最大限度地减少流行病的传播，同时最大限度地减少有害影响和最大限度地增加关键卫生资源的可获得性。本项目旨在通过设计一种在适当约束下比封锁更好且可扩展的替代方案来应对这一挑战。本项目专注于开发新冠肺炎大流行的模型，特别是在此背景下研究邻近社区的传播、缓解措施和医疗资源的优化分配。这个项目的目标是(I)设计一个更好和可扩展的替代方案，以取代完全封锁；(Ii)设计一个认知解决方案，可以应用于具有不同连接性和人口分布的各种人口统计数据，并最大限度地减少有关以往疫情传播的信息；以及(Iii)尽量减少流行病模型不确定性对隔离和医疗资源分配策略的影响。PI将使用一组新的数学技术来解决这个问题，这些技术可以处理异构性，并且是可扩展的。该跨学科团队包括约翰霍普金斯大学，该大学一直是新冠肺炎数据收集的主要中心。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。

The current pandemic has stimulated a strong response on the part of local, state, and federal government, with containment largely achieved through stringent lockdowns, effectively quarantining nearly every household in the country. Given the enormous socio-economic impacts of this approach, it is imperative to understand how to minimize the spread of the epidemic while also minimizing deleterious effects and maximizing the availability of critical health resources. This project seeks to address this challenge by devising a better and scalable alternative to lockdown under suitable constraints.This project focuses on developing models for the COVID-19 pandemic, in particular looking at neighboring community spread, mitigation measures, and optimal distribution of healthcare resources in that context. This project aims to (i) devise a better and scalable alternative to full lockdown; (ii) devise a cognitive solution that can be applied to various demographics having heterogeneous connectivity and population distribution with minimal information regarding previous epidemic spread; and (iii) minimize the impact of epidemic model uncertainties on the confinement and medical resource allocation strategies. The PIs will employ a collection of novel mathematical techniques to the problem that can handle heterogeneity and are scalable. The interdisciplinary team includes Johns Hopkins University, which has been a major Center for the collection of COVID-19 data.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.