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

Author:

Han Yuexing,Xie Zeyang,Guo Yike,Wang BingORCID

Abstract

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.

Publisher

Springer Science and Business Media LLC

Subject

Public Health, Environmental and Occupational Health

Reference39 articles.

1. He D, Zhao S, Xu X, Lin Q, Zhuang Z, Cao P, Wang M, Lou Y, Xiao L, Wu Y. Low dispersion in the infectiousness of covid-19 cases implies difficulty in control. BMC Public Health. 2020; 20(1):1558.

2. Imai N, Cori A, Dorigatti I, Baguelin M, Donnelly CA, Riley S, Ferguson NM. Report 3: Transmissibility of 2019-nCoV. Imp Coll Lond. 2020. https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-3-transmissibility-of-covid-19/. Accessed 25 Jan 2020.

3. Lin Q, Zhao S, Gao D, Lou Y, Yang S, S.Musa S, H.Wang M. A conceptual model for the coronavirus disease 2019(covid-19) outbreak in wuhan, china with individual reaction and governmental action. Int J Infect Dis. 2020; 93:211–6.

4. Read JM, Bridgen JRE, Cummings DAT, Ho A, Jewell CP. Novel coronavirus 2019-ncov: early estimation of epidemiological parameters and epidemic predictions. MedRxiv 2020:2020.01.23.20018549. 2020. https://doi.org/10.1101/2020.01.23.20018549.

5. Eikenberry SE, Mancuso M, Iboi E. To mask or not to mask: modeling the potential for face mask use by the general public to curtail the covid-19 pandemic. Infect Dis Model. 2020; 5:293–308.

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3