摘要
目的研究基于传染病动力学易感-暴露-感染-恢复(SEIR)模型对新型冠状病毒肺炎(COVID-19)疫情发展情况的预测效果,为有效应对疫情提供指导。方法利用Python爬虫自动更新功能获取中华人民共和国国家卫生健康委员会公布的疫情数据,通过改进传染病动力学SEIR模型,自动修正COVID-19基本再生数(R0),对中国湖北省和韩国的COVID-19疫情发展趋势进行预测。结果模型预测的湖北省COVID-19疫情顶点在2020年2月21日,现有确诊病例数约为50000例(2月19日),预计疫情将于3月4日回落至30000例以下,并在5月10日左右结束。中华人民共和国国家卫生健康委员会公布的实际数据显示,确诊人数顶点为53371例。模型预测的韩国疫情峰值在3月7日,将于4月底结束。结论改进的传染病动力学SEIR模型在COVID-19疫情早期实现了较准确的数据预测,政府相关部门在疫情中及时、有效的强力干预明显影响了疫情的发展进程,东亚其他国家如韩国的疫情在3月仍处于上升期,提示中国需要提防输入性疫情风险。
Objective To predict the coronavirus disease 2019(COVID-19)epidemic situation based on the infectious disease dynamics susceptible-exposed-infected-recovered(SEIR)model,so as to provide guidance for effective control of the epidemic.Methods Python crawler automatic update function was used to collect the epidemic data released by the National Health Commission of China.An improved infectious disease dynamics SEIR model,which can automatically correct the COVID-19 basic reproductive number(R0),was constructed to predict the development trend of COVID-19 epidemic in Hubei Province of China and South Korea.Results The peak of the COVID-19 epidemic in Hubei Province of China predicted by the model would appear on Feb.21,2020.The number of confirmed COVID-19 cases would be about 50000 on Feb.19 and would fall to below 30000 on Mar.4,and the epidemic would end on May 10.According to the actual data released by the National Health Commission of China,the peak number of confirmed COVID-19 patients was 53371.The model predicted that an epidemic peak in South Korea would be on Mar.7,and would end at the end of April.Conclusion This improved infectious disease dynamics SEIR model established in the early stage of COVID-19 epidemic has achieved relatively accurate prediction.The timely and effective intervention by relevant government departments has significantly affected the development of the epidemic.The epidemic situation in other countries in East Asia,such as South Korea,is still on the rise in March,suggesting that China needs to be on guard against the risk of imported epidemic.
作者
王思远
谭瀚霖
李东杰
WANG Si-yuan;TAN Han-lin;LI Dong-jie(Xiangya International Medical Center,National Clinical Research Center for Geriatric Disorders,Xiangya Hospital,Central South University,Changsha 410008,Hunan,China;Changsha Research Institute of Mining and Metallurgy Co.,Ltd,Changsha 410000,Hunan,China;School of System Engineer,National University of Defense Technology,Changsha 410012,Hunan,China)
出处
《第二军医大学学报》
CAS
CSCD
北大核心
2020年第6期637-641,共5页
Academic Journal of Second Military Medical University
基金
国家自然科学基金(21806186)
国家科技部科研院所技术开发研究专项(2012EG113170).
