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COVID-19 疫情中抑制和缓解干预措施的建模。

Modeling of suppression and mitigation interventions in the COVID-19 epidemics.

机构信息

School of Computer Engineering and Science, Shanghai University, Shanghai, People's Republic of China.

Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, People's Republic of China.

出版信息

BMC Public Health. 2021 Apr 14;21(1):723. doi: 10.1186/s12889-021-10663-6.

Abstract

BACKGROUND

The global spread of the COVID-19 pandemic has become the most fundamental threat to human health. In the absence of vaccines and effective therapeutical solutions, non-pharmaceutic intervention has become a major way for controlling the epidemic. Gentle mitigation interventions are able to slow down the epidemic but not to halt it well. While strict suppression interventions are efficient for controlling the epidemic, long-term measures are likely to have negative impacts on economics and people's daily live. Hence, dynamically balancing suppression and mitigation interventions plays a fundamental role in manipulating the epidemic curve.

METHODS

We collected data of the number of infections for several countries during the COVID-19 pandemics and found a clear phenomenon of periodic waves of infection. Based on the observation, by connecting the infection level with the medical resources and a tolerance parameter, we propose a mathematical model to understand impacts of combining intervention measures on the epidemic dynamics.

RESULTS

Depending on the parameters of the medical resources, tolerance level, and the starting time of interventions, the combined intervention measure dynamically changes with the infection level, resulting in a periodic wave of infections controlled below an accepted level. The study reveals that, (a) with an immediate, strict suppression, the numbers of infections and deaths are well controlled with a significant reduction in a very short time period; (b) an appropriate, dynamical combination of suppression and mitigation may find a feasible way in reducing the impacts of epidemic on people's live and economics.

CONCLUSIONS

While the assumption of interventions deployed with a cycle of period in the model is limited and unrealistic, the phenomenon of periodic waves of infections in reality is captured by our model. These results provide helpful insights for policy-makers to dynamically deploy an appropriate intervention strategy to effectively battle against the COVID-19.

摘要

背景

COVID-19 大流行的全球传播已成为对人类健康最根本的威胁。在缺乏疫苗和有效治疗方法的情况下,非药物干预已成为控制疫情的主要手段。温和的缓解干预措施能够减缓疫情,但不能很好地阻止疫情。虽然严格的抑制干预措施对控制疫情非常有效,但长期措施可能会对经济和人们的日常生活产生负面影响。因此,动态平衡抑制和缓解干预措施在控制疫情曲线方面起着至关重要的作用。

方法

我们收集了 COVID-19 大流行期间几个国家的感染人数数据,发现了一个明显的周期性感染波现象。基于这一观察,我们通过将感染水平与医疗资源和容忍参数联系起来,提出了一个数学模型来理解联合干预措施对疫情动态的影响。

结果

根据医疗资源、容忍水平和干预开始时间的参数,联合干预措施会随着感染水平的变化而动态变化,从而将感染控制在可接受的水平以下,形成周期性的感染波。研究表明,(a)立即采取严格的抑制措施,可在很短的时间内显著减少感染人数和死亡人数;(b)适当的、动态的抑制和缓解措施的结合可能为减轻疫情对人们生活和经济的影响找到可行的方法。

结论

虽然模型中干预措施以周期方式部署的假设是有限的和不现实的,但我们的模型确实捕捉到了现实中感染周期性波动的现象。这些结果为决策者提供了有益的见解,以便他们能够动态部署适当的干预策略,有效应对 COVID-19。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c4f0/8048268/0b6423584581/12889_2021_10663_Fig1_HTML.jpg

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