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疫苗接种、盾牌免疫和隔离控制 COVID-19 疫情的策略:一种度量时态逻辑方法。

Control strategies for COVID-19 epidemic with vaccination, shield immunity and quarantine: A metric temporal logic approach.

机构信息

School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, United States of America.

Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, United States of America.

出版信息

PLoS One. 2021 Mar 5;16(3):e0247660. doi: 10.1371/journal.pone.0247660. eCollection 2021.

Abstract

Ever since the outbreak of the COVID-19 epidemic, various public health control strategies have been proposed and tested against the coronavirus SARS-CoV-2. We study three specific COVID-19 epidemic control models: the susceptible, exposed, infectious, recovered (SEIR) model with vaccination control; the SEIR model with shield immunity control; and the susceptible, un-quarantined infected, quarantined infected, confirmed infected (SUQC) model with quarantine control. We express the control requirement in metric temporal logic (MTL) formulas (a type of formal specification languages) which can specify the expected control outcomes such as "the deaths from the infection should never exceed one thousand per day within the next three months" or "the population immune from the disease should eventually exceed 200 thousand within the next 100 to 120 days". We then develop methods for synthesizing control strategies with MTL specifications. To the best of our knowledge, this is the first paper to systematically synthesize control strategies based on the COVID-19 epidemic models with formal specifications. We provide simulation results in three different case studies: vaccination control for the COVID-19 epidemic with model parameters estimated from data in Lombardy, Italy; shield immunity control for the COVID-19 epidemic with model parameters estimated from data in Lombardy, Italy; and quarantine control for the COVID-19 epidemic with model parameters estimated from data in Wuhan, China. The results show that the proposed synthesis approach can generate control inputs such that the time-varying numbers of individuals in each category (e.g., infectious, immune) satisfy the MTL specifications. The results also show that early intervention is essential in mitigating the spread of COVID-19, and more control effort is needed for more stringent MTL specifications. For example, based on the model in Lombardy, Italy, achieving less than 100 deaths per day and 10000 total deaths within 100 days requires 441.7% more vaccination control effort than achieving less than 1000 deaths per day and 50000 total deaths within 100 days.

摘要

自 COVID-19 疫情爆发以来,已经提出了各种公共卫生控制策略来对抗冠状病毒 SARS-CoV-2。我们研究了三种特定的 COVID-19 疫情控制模型:带有疫苗接种控制的易感者、暴露者、感染性、恢复者(SEIR)模型;带有盾牌免疫控制的 SEIR 模型;以及带有隔离控制的易感者、未隔离感染者、隔离感染者、确诊感染者(SUQC)模型。我们使用度量时间逻辑(MTL)公式(一种形式规范语言)来表达控制要求,这些公式可以指定预期的控制结果,例如“在未来三个月内,每天因感染而死亡的人数不应超过一千人”或“在未来 100 到 120 天内,人群对疾病的免疫力应超过 20 万人”。然后,我们开发了使用 MTL 规范合成控制策略的方法。据我们所知,这是第一篇系统地基于 COVID-19 流行模型和形式规范来综合控制策略的论文。我们在三个不同的案例研究中提供了仿真结果:使用从意大利伦巴第的数据估计的模型参数对 COVID-19 疫情进行疫苗接种控制;使用从意大利伦巴第的数据估计的模型参数对 COVID-19 疫情进行盾牌免疫控制;以及使用从中国武汉的数据估计的模型参数对 COVID-19 疫情进行隔离控制。结果表明,所提出的综合方法可以生成控制输入,使得每个类别(如感染性、免疫性)的个体数量随时间变化满足 MTL 规范。结果还表明,早期干预对于减轻 COVID-19 的传播至关重要,并且对于更严格的 MTL 规范需要更多的控制努力。例如,基于意大利伦巴第的数据模型,要实现每天死亡人数少于 100 人,100 天内总死亡人数少于 10000 人,需要比每天死亡人数少于 1000 人,100 天内总死亡人数少于 50000 人多 441.7%的疫苗接种控制力度。

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