Modeling and Analysis of COVID-19 Based on Multi-isolation Strategy

doi:10.12068/j.issn.1005-3026.2020.09.004

Abstract

Abstract: Aiming at the outbreak of COVID-19 (corona virus disease 2019)， considering multi-isolation measures， a non-linear infectious disease model was established to study the trend of COVID-19. Comparing the data in China of the COVID-19 with the results of simulation experiments， the isolation measures are important for the prevention and control of COVID-19. The results showed that if the isolation measures were implemented one week later， the number of infected persons in the entire epidemic would increase almost 7 times. In addition， the measure for newly confirmed clinical cases in Hubei Province is analyzed by simulation experiments. The results showed that the measure effectively solved the problem of previously missed cases and can greatly speed up the extinction of the epidemic. Then， the subsequent official data of COVID-19 also agreed with the simulation results.

Key words: COVID-19, epidemic model, basic reproduction number, multi-isolation strategy, simulation

CLC Number:

FU Qiang， YAO Yu. Modeling and Analysis of COVID-19 Based on Multi-isolation Strategy[J]. Journal of Northeastern University Natural Science, 2020, 41(9): 1239-1243.

References

[1]Hamer W H.Epidemic disease in England—the evidence of variety and of persis-tency of type［J］.Lancet， 1906，1:733-739.
[2]Ross R.The prevention of Malaria［M］.London:Murray，1911.
[3]Kermack W O，McKendrick A G.Contributions to the mathematical theory of epidemics［J］.Proceedings of the Royal Society of London，Series A， 1927，115(772):700-721.
[4]陈兰荪，孟新柱，焦建军.生物动力学［M］.北京:科学出版社，2009.(Chen Lan-sun，Meng Xin-zhu，Jiao Jian-jun.Biodynamics［M］.Beijing:Science Press，2009.)
[5]曹慧，周义仓.具有饱和治疗的离散SEIS结核病模型的动力学性态［J］.数学的实践与认识，2014，44(18):209-216.(Cao Hui，Zhou Yi-cang.Dynamics of a discrete SEIS tuberculosis model with saturation therapy［J］.Journal of Mathematics in Practice and Theory，2014，44(18):209-216.)
[6]张昕.考虑免疫接种的耐药TB模型的渐近性态［D］.北京:华北电力大学，2012.(Zhang Xi.The asymptotical behaviors of models for tuberculosis with vaccination and drug-resistance［D］.Beijing:North China Electric Power University，2012.)
[7]Wu J T，Leung K，Leung G M，et al.Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan，China:a modelling study［J］.Lancet，2020，395(10225):689-697.
[8]周涛，刘权辉，杨紫陌，等.新型冠状病毒感染肺炎基本再生数的初步预测［J］.中国循证医学杂志，2020，20(3):1-6.(Zhou Tao，Liu Quan-hui，Yang Zi-mo，et al.Preliminary prediction of the basic reproduction number of the novel coronavirus 2019-nCoV［J］.Chinese Journal of Evidence-Based Medicine，2020，20(3):1-6.)
[9]Chan J F，Yuan S，Kok K，et al.A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission:a study of a family cluster［J］.Lancet， 2020，395(10223):514-523.
[10]Sun K，Chen J，Viboud C，et al.Early epidemiological analysis of the 2019-nCoV outbreak based on a crowdsourced data［J］.MedRxiv，2020.
[11]Lipsitch M，Cohen T，Cooper B，et al.Transmission dynamics and control of severe acute respiratory syndrome［J］.Science， 2003，300(5627):1966-1970.
[12]Cao Z，Zhang Q，Lu X，et al.Incorporating human movement data to improve epidemiological estimates for 2019-nCoV［J］.MedRxiv，2020.
[13]Adnerson R M，May R M.Infectious diseases of humans:dynamics and control［M］.Oxford:Oxford University Press，1991.
[14]Riley S，Fraser C，Donnelly C A，et al.Transmission dynamics of the etiological agent of SARS in Hong Kong:impact of public health interventions［J］.Science，2003，300 (5627):1961-1966.
[15]van Kerkhove M D，Bento A I，Mills H L，et al.A review of epidemiological parameters from Ebola outbreaks to inform early public health decision-making［J］.Scientific Data， 2015，2(1):150019.
[15]关守平，房少纯.一种新型的区间-粒子群优化算法［J］.东北大学学报(自然科学版)，2012，33(10):1381-1384.(Guan Shou-ping，Fang Shao-chun.A new interval particle swarm optimization algorithm［J］.Journal of Northeastern University(Natural Science)，2012，33(10):1381-1384.)