关于春节返程人口流动对新型冠状病毒肺炎(COVID-19)疫情影响的讨论  

Impact of returning population migration after the Chinese Spring Festival on the COVID-19 epidemic

作　　者：石耀霖[1,2] 程惠红 任天翔[3] 黄禄渊 Yaolin Shi;Huihong Cheng;Tianxiang Ren;Luyuan Huang(Key Laboratory of Computational Geodynamics,Chinese Academy of Sciences,Beijing 100049,China;University of Chinese Academy of Sciences,Beijing 100049,China;Chinese Academy of Geological Sciences,Beijing 100037,China;Institute of Crustal Dynamics,Chinese Earthquake Administration,Beijing 100085,China)

机构地区：[1]中国科学院计算地球动力学重点实验室,北京100049 [2]中国科学院大学,北京100049 [3]中国地质科学院,100037 [4]中国地震局地壳应力研究所,北京100085

出　　处：《科学通报》2020年第22期2314-2320,共7页Chinese Science Bulletin

基　　金：国家自然科学基金(40344007)资助。

摘　　要：当2020年春运拉开帷幕时,预期春运从1月10日~2月18日,在40 d时间里,中国有30亿人次出行(http://www.gov.cn/xinwen/2020-01/10/content_5468027.htm).虽然新型冠状病毒肺炎(以下简称新冠肺炎,COVID-19)的暴发导致今年春运40 d客流量同比下降45%,但武汉采取封城措施前,已经有500万人离开武汉,造成了新冠肺炎全国性的扩散。The outbreak of the novel coronavirus disease 2019(COVID-19) and its spread throughout the China have caused a huge impact on China and the international community. And now it becomes a worldwide infectious disease which poses a major threat to the lives of people around the world. What is worth noting about China is five million people left Wuhan before the Spring Festival, which caused the nationwide spreading of COVID-19 epidemic. Then, it raises a question of concern, should the return of migrant workers and students after the Spring Festival cause an increase in the epidemic? In this study, we use the discrete stochastic model(DSM) to study the transmission dynamics of COVID-19. The DSM is different from the classical continuous variable ordinary differential equations, and has two characteristics. First, on account of few patients at the beginning of the COVID-19 and random fluctuations during the transmission process are prominent;the DSM can better reflect the initial transmission characteristics than the continuous variable deterministic ordinary differential equation model. Second, the DSM can easily track the changes in the epidemic situation, and well reflect the infectious rate varies with time due to different prevention and control measures, and then gradually estimate the development of the epidemic situation. Meanwhile, based on the facts that there are successive time lags among epidemic infection, symptom onset, and diagnosis confirmation, the Erlang probability density distribution, which is frequent used in the queuing theory, has been applied to the calculation of numbers of epidemic daily outbreaks and daily infections respectively from the corrected numbers of patients diagnosed and confirmed reported every day since the outbreak of the epidemic in Hubei Province. The number of symptom onset patients we calculated agrees well with recent statistics made by the Chinese Center for Disease Control and Prevention(CDC), showing the feasibility of our method. The calculation results indicate tha

关 键 词：春运 肺炎 客流量 ID 疫情 

分 类 号：R563.1[医药卫生—呼吸系统] R181.8[医药卫生—内科学]