COVID-19 Scratch Models to Support Local Decisions
COVID-19 Scratch Models to Support Local Decisions
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DOI：
10.1287/msom.2020.0891
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发表时间：
2020-07-01
影响因子：
6.3
通讯作者：
Kaplan, Edward H.
中科院分区：
文献类型：
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作者：
Kaplan, Edward H.
本文基于应耶鲁大学、耶鲁纽黑文医院和康涅狄格州在 SARS-CoV-2 爆发初期几周的要求而进行的模型研究。这项工作大部分依赖于临时建模，即实时创建的模型。应用包括建议活动人群规模限制、医院激增计划、时间决策（何时停止并可能重新启动大学活动）以及评估替代干预措施影响的情景分析等问题。本文记录了地方层面实时应对 COVID-19 危机期间面临的问题、开发的模型以及提供的建议。结果包括一个计算事件最大规模的简单公式，可确保 99% 的概率不存在感染者；确定现有重症监护病房 (ICU) 容量不足以容纳新冠肺炎 (COVID-19) 患者，因此建立了一个大型专用新冠肺炎 (COVID-19) 负压 ICU；一种新的流行病模型表明，大学举办正常的春季和夏季活动是不可行的，如果不采取额外的公共卫生行动，类似封锁的居家和社交距离限制只会延缓传播并在限制解除后实现反弹，而积极的社区筛查以快速发现和隔离感染者可能会结束疫情。
This article is based on modeling studies conducted in response to requests from Yale University, the Yale New Haven Hospital, and the State of Connecticut during the early weeks of the SARS-CoV-2 outbreak. Much of this work relied on scratch modeling, that is, models created hunt scratch in real time. Applications included recommending event crowd-size restrictions, hospital surge planning, timing decisions (when to stop and possibly restart university activities), and scenario analyses to assess the impacts of alternative interventions, among other problems. This paper documents the problems faced, models developed, and advice offered during real-time response to the COVID-19 crisis at the local level. Results include a simple formula for the maximum size of an event that ensures no infected persons are present with 99% probability; the determination that existing intensive care unit (ICU) capacity was insufficient for COVID-19 arrivals, which led to creating a large dedicated COVID-19-negative pressure ICU; and a new epidemic model that showed the infeasibility of the university hosting normal spring and summer events, that lockdown-like stay-at-home and social distancing restrictions without additional public health action would only delay transmission and enable a rebound after restrictions are lifted, and that aggressive community screening to rapidly detect and isolate infected persons could end the outbreak.