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

当前的大流行刺激了地方、州和联邦政府的强烈反应，主要通过严格的封锁来实现控制，有效地隔离了该国几乎所有家庭。鉴于这一做法的巨大社会经济影响，必须了解如何尽量减少这一流行病的蔓延，同时尽量减少有害影响并最大限度地提供关键的卫生资源。该项目旨在通过在适当的限制条件下设计更好、可扩展的封锁替代方案来应对这一挑战。该项目侧重于开发COVID-19大流行的模型，特别是在这种情况下研究邻近社区的传播、缓解措施和医疗资源的最佳分配。该项目旨在：(i)设计一种更好、可扩展的替代方案，以取代全面封锁；㈡设计一种认知解决办法，可适用于具有异质连通性和人口分布的各种人口统计数据，而对以前的流行病传播了解最少；(3)最小化流行病模型不确定性对约束和医疗资源配置策略的影响。pi将采用一系列新颖的数学技术来解决问题，这些技术可以处理异构性并具有可扩展性。跨学科团队包括约翰霍普金斯大学，该大学一直是收集COVID-19数据的主要中心。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 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.