Courant Institute of Mathematical Sciences, New York University, New York, NY, United States of America.
Institute for Advanced Study, Princeton, NJ, United States of America.
PLoS One. 2021 May 13;16(5):e0251349. doi: 10.1371/journal.pone.0251349. eCollection 2021.
This paper contains a theoretical study of epidemic control. It is inspired by current events but not intended to be an accurate depiction of the SARS-CoV-2 pandemic. We consider the emergence of a highly transmissible pathogen, focusing on metropolitan areas. To ensure some degree of realism, we present a conceptual model of the outbreak and early attempts to stave off the onslaught, including the use of lockdowns. Model outputs show strong qualitative-in some respects even quantitative-resemblance to the events of Spring 2020 in many cities worldwide. We then use this model to project forward in time to examine different paths in epidemic control after the initial surge is tamed and before the arrival of vaccines. Three very different control strategies are analyzed, leading to vastly different outcomes in terms of economic recovery and total infected population (or progress toward herd immunity). Our model, which is a version of the SEIQR model, is a time-dependent dynamical system with feedback-control. One of the main conclusions of this analysis is that the course of the epidemic is not entirely dictated by the virus: how the population responds to it can play an equally important role in determining the eventual outcome.
这是一篇关于传染病控制的理论研究。它的灵感来源于当前的事件,但并不旨在准确描述 SARS-CoV-2 大流行。我们考虑了一种高度传染性病原体的出现,重点关注大都市地区。为了确保一定程度的现实性,我们提出了一种疫情爆发和早期遏制措施的概念模型,包括封锁措施。模型输出显示,在某些方面,甚至在某些方面与 2020 年春季全球许多城市的事件具有强烈的定性相似性。然后,我们使用这个模型进行时间预测,以研究初始疫情得到控制但疫苗尚未到来后的不同传染病控制策略。分析了三种非常不同的控制策略,导致在经济复苏和总感染人数(或群体免疫进展)方面出现了截然不同的结果。我们的模型是 SEIQR 模型的一个版本,是一个具有反馈控制的时变动力系统。该分析的一个主要结论是,疫情的进程并不完全由病毒决定:人口对病毒的反应同样可以在确定最终结果方面发挥重要作用。