Modeling COVID-19 epidemic in Heilongjiang province, China

被引:79
|
作者
Sun, Tingzhe [1 ]
Wang, Yan [1 ]
机构
[1] Anqing Normal Univ, Sch Life Sci, 1318 North Jixian Rd, Anqing 246011, Anhui, Peoples R China
基金
中国国家自然科学基金;
关键词
COVID-19; Epidemic; Asymptomatic patient; Imported patient;
D O I
10.1016/j.chaos.2020.109949
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
The Coronavirus Disease 2019 (COVID-19) surges worldwide. However, massive imported patients especially into Heilongjiang Province in China recently have been an alert for local COVID-19 outbreak. We collected data from January 23 to March 25 from Heilongjiang province and trained an ordinary differential equation model to fit the epidemic data. We extended the simulation using this trained model to characterize the effect of an imported 'escaper'. We showed that an imported 'escaper' was responsible for the newly confirmed COVID-19 infections from Apr 9 to Apr 19 in Heilongjiang province. Stochastic simulations further showed that significantly increased local contacts among imported 'escaper', its epidemiologically associated cases and susceptible populations greatly contributed to the local outbreak of COVID-19. Meanwhile, we further found that the reported number of asymptomatic patients was markedly lower than model predictions implying a large asymptomatic pool which was not identified. We further forecasted the effect of implementing strong interventions immediately to impede COVID-19 outbreak for Heilongjiang province. Implementation of stronger interventions to lower mutual contacts could accelerate the complete recovery from coronavirus infections in Heilongjiang province. Collectively, our model has characterized the epidemic of COVID-19 in Heilongjiang province and implied that strongly controlled measured should be taken for infected and asymptomatic patients to minimize total infections. (c) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页数:5
相关论文
共 50 条
  • [31] Lessons drawn from China and South Korea for managing COVID-19 epidemic: Insights from a comparative modeling study
    Tang, Biao
    Xia, Fan
    Bragazzi, Nicola Luigi
    McCarthy, Zachary
    Wang, Xia
    He, Sha
    Sun, Xiaodan
    Tang, Sanyi
    Xiao, Yanni
    Wu, Jianhong
    ISA TRANSACTIONS, 2022, 124 : 164 - 175
  • [32] Modeling the transmission dynamics of COVID-19 epidemic: a systematic review
    Guan, Jinxing
    Wei, Yongyue
    Zhao, Yang
    Chen, Feng
    JOURNAL OF BIOMEDICAL RESEARCH, 2020, 34 (06): : 422 - 430
  • [33] Modeling Growth, Containment and Decay of the COVID-19 Epidemic in Italy
    Capuano, Francesco
    FRONTIERS IN PHYSICS, 2020, 8
  • [34] Response to the COVID-19 epidemic: a report from Shenzhen, China
    Zhao, Xin
    Wu, Youchun
    Li, Zhiwei
    Liu, Lei
    GLOBAL HEALTH & MEDICINE, 2020, 2 (02): : 133 - 134
  • [35] The psychological impact of the COVID-19 epidemic on college students in China
    Cao, Wenjun
    Fang, Ziwei
    Hou, Guoqiang
    Han, Mei
    Xu, Xinrong
    Dong, Jiaxin
    Zheng, Jianzhong
    PSYCHIATRY RESEARCH, 2020, 287
  • [36] Epidemic modeling for the resurgence of COVID-19 in Chinese local communities
    Peng, Min
    Zhang, Jianing
    Gong, Jingrui
    Ran, Xingqi
    Liu, Jvlu
    Zhang, Lin
    JOURNAL OF SAFETY SCIENCE AND RESILIENCE, 2022, 3 (03): : 229 - 234
  • [37] BORN IN WUHAN: LESSONS FROM COVID-19 EPIDEMIC IN CHINA
    Semenov, A., V
    Pshenichnaya, N. Yu
    INFEKTSIYA I IMMUNITET, 2020, 10 (02): : 210 - 220
  • [38] MATHEMATICAL MODELING OF THE VACCINATION INFLUENCE ON THE COVID-19 EPIDEMIC PROPAGATION
    Grinchuk, Pavel S.
    Fisenko, Sergey P.
    Shnip, Alexander L.
    DOKLADY NATSIONALNOI AKADEMII NAUK BELARUSI, 2022, 66 (03): : 274 - 279
  • [39] Modeling the COVID-19 epidemic and awareness diffusion on multiplex networks
    He, Le
    Zhu, Linhe
    COMMUNICATIONS IN THEORETICAL PHYSICS, 2021, 73 (03)
  • [40] Fractional mathematical modeling for epidemic prediction of COVID-19 in Egypt
    Raslan, W. E.
    AIN SHAMS ENGINEERING JOURNAL, 2021, 12 (03) : 3057 - 3062