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

Shi Y.,
Cheng H.,
Ren T.,
Huang L.
Document Type
Source Title
Kexue Tongbao/Chinese Science Bulletin
Chinese Academy of Sciences


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 that in the rising stage of the epidemic, although the number of newly diagnostic confirmed patients reported before the day of lock down of Wuhan city was only 180 daily, the number of newly infected people may have reached about 2500, and the cumulative number of infected may reach 33000. More than 5500 people may have rushed out of Hubei Province uncontrollably, thus causing a national epidemic of COVID-19. However, it is in the epidemic decline stage mow. Even if Hubei's daily confirmed diagnosis remains at a high level of more than 1700 people a day, the number of newly infected people may be less than 800 nowadays, and most of whom may already be concerned about quarantine. The number of infections in other provinces and cities is much lower than that in Hubei. Therefore, as long as the epidemic situation in different regions is distinguished, return trips could be arranged at reduced people density, attentive epidemic prevention of transportation, and well preparation at the receiving cities. Rebound of the epidemic is quite unlikely. Although, the possibility of an epidemic rebound is small, only in conditions of slack thinking and strict measures are carried out. In accordance with the transmission of the world epidemic, more attention must be paid to the inspection of the influx of foreign infected people. © 2020, Science Press. All right reserved.

Migration angle
Region/Country (by coverage)
Index Keywords

Diagnosis; Ordinary differential equations; Patient monitoring; Population dynamics; Probability distributions; Queueing theory; Springs (components); Stochastic control systems; Stochastic models; Stochastic systems; Transmissions; Center for disease control and preventions; Discrete stochastic models; International community; Ordinary differential equation models; Prevention and controls; Probability density distribution; Transmission characteristics; Transmission dynamics; Disease control