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Chinese Science Bulletin, Volume 65 , Issue 22 : 2314-2320(2020) https://doi.org/10.1360/TB-2020-0151

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

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Abstract

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.


Acknowledgment

致谢 感谢国家自然科学基金(40344007)资助; 感谢三位匿名审稿人对文章提出的意见.


Author information

石耀霖 中国科学院大学地球与行星科学学院教授, 中国科学院院士. 长期从事地球动力学基础研究工作. 2003年, 提出“SARS流行病传播的系统动力学概率模型及应用” 课题, 把地球动力学研究中的一种思想方法应用于SARS的传染病学研究, 成功建立了SARS传播概率模型.


程惠红 中国科学院大学地球与行星科学学院副教授. 从事计算地球动力学研究工作, 致力于地震孕育及同震形变等高性能并行计算方面的研究工作.


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  • Figure 1

    Distribution of delay days interval between patient onset and diagnosis in the early days of COVID-19 in Beijing. The blue column is the actual data and the red column is the theoretical value of the second order Erlang distribution

  • Figure 2

    Comparison of the daily confirmed number of patients before and after Erlang distribution correction in Hubei Province, and daily number of patients onset and infected from daily confirmed number of patient using Erlang distribution

  • Figure 3

    The patient statistics provided by the researchers of China Center for Disease Control and Prevention (CDC)[8] as of February 11, 2020. The blue column shows the daily number of new cases; the red column is the daily reported confirmed number of patient. Note that since many patients who had been onset 12 d go have not yet seen a doctor or been diagnosed, the number of onset patients has been missing since February 2. Moreover, the missing is more serious around February 12. Therefore, we blur the right of this figure. Modified according to CDC[7]

  • Figure 4

    Sensitivity test of the impact of the confirmed number of patients in the next 15 d on the daily number of patients onset

  • Figure 5

    Daily cumulative numbers vary with time. Blue presents the daily cumulative number of infected patients; red means the daily cumulative number of patients onset; black shows the daily cumulative confirmed number of patients after correction and yellow is the original daily cumulative confirmed number

  • Figure 6

    The difference (blue) between the daily cumulative number of patients infected and onset over time, which reflecting the number of patients who have been infected but have not yet been diagnosed. And the difference between the daily cumulative number of patients onset and confirmed (green is the difference between the daily cumulative number of corrected confirmed patients, red is the difference between the daily cumulative number of confirmed people who have been diagnosed with the original report), which reflecting the number of patients who have been onset but have not yet been diagnosed

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