Exploring early prevention and control of COVID-19 outbreak based on system dynamics model analysis / 浙江大学学报·医学版

To explore early prevention and control of coronavirus disease 2019 （COVID-19） outbreak based on system dynamics model analysis. The data of early outbreak of COVID-19 were collected from the World Health Organization，covering countries of the China，United States，United Kingdom，Australia，Serbia and Italy. The susceptible-exposed-infected-recovered （SEIR） model was generalized and then its parameters were optimized. According to the parameters in the basic infection number expression，the sensitivity in the system dynamics model was used to quantitatively analyze the influence of the protection rate，infection rate and average quarantine time on the early spread of the outbreak. Based on the analysis results，targeted prevention and control measures for the early outbreak of COVID-19 were proposed. The generalized SEIR model had a good fit for the early prediction and evaluation of COVID-19 outbreaks in six countries. The spread of COVID-19 was mainly affected by the protection rate，infection rate and average quarantine time. The improvement of the protection rate in the first ays was the most important:the greater the protection rate，the fewer the number of confirmed cases. The infection rate in the first 5 days was the most critical:the smaller the infection rate，the fewer the number of confirmed cases. The average quarantine time in the first 5 days was very important:the shorter the average quarantine time，the fewer the number of confirmed cases. Through the comparison of key parameters of six countries，Australia and China had implemented strict epidemic prevention policies，which had resulted in good epidemic prevention effects. In the early stage of the outbreak，it is necessary to improve the protection rate，shorten the average quarantine time，and implement strict isolation policies to curb the spread of COVID-19.

Predicting COVID-19 epidemiological trend by applying population mobility data in two-stage modeling / 浙江大学学报·医学版

:To predict the epidemiological trend of coronavirus disease 2019 (COVID-19) by mathematical modeling based on the population mobility and the epidemic prevention and control measures. : As of February 8，2020，the information of 151 confirmed cases in Yueqing，Zhejiang province were obtained，including patients' infection process，population mobility between Yueqing and Wuhan，etc. To simulate and predict the development trend of COVID-19 in Yueqing, the study established two-stage mathematical models，integrating the population mobility data with the date of symptom appearance of confirmed cases and the transmission dynamics of imported and local cases. : It was found that in the early stage of the pandemic，the number of daily imported cases from Wuhan (using the date of symptom appearance) was positively associated with the number of population travelling from Wuhan to Yueqing on the same day and 6 and 9 days before that. The study predicted that the final outbreak size in Yueqing would be 170 according to the number of imported cases estimated by consulting the population number travelling from Wuhan to Yueqing and the susceptible-exposed-infectious-recovered (SEIR) model; while the number would be 165 if using the reported daily number of imported cases. These estimates were close to the 170，the actual monitoring number of cases in Yueqing as of April 27，2020. : The two-stage modeling approach used in this study can accurately predict COVID-19 epidemiological trend.

Impact of Wuhan lockdown on the spread of COVID-19 in China: a study based on the data of population mobility / 浙江大学学报·医学版

This study aimed to quantitatively assess the effectiveness of the Wuhan lockdown measure on controlling the spread of coronavirus diesase 2019 (COVID-19). : Firstly，estimate the daily new infection rate in Wuhan before January 23，2020 when the city went into lockdown by consulting the data of Wuhan population mobility and the number of cases imported from Wuhan in 217 cities of Mainland China. Then estimate what the daily new infection rate would have been in Wuhan from January 24 to January 30th if the lockdown measure had been delayed for 7 days，assuming that the daily new infection in Wuhan after January 23 increased in a high，moderate and low trend respectively (using exponential, linear and logarithm growth models). Based on that，calculate the number of infection cases imported from Wuhan during this period. Finally，predict the possible impact of 7-day delayed lockdown in Wuhan on the epidemic situation in China using the susceptible-exposed-infectious-removed (SEIR) model. : The daily new infection rate in Wuhan was estimated to be 0.021%，0.026%，0.029%，0.033% and 0.070% respectively from January 19 to January 23. And there were at least 20 066 infection cases in Wuhan by January 23，2020. If Wuhan lockdown measure had been delayed for 7 days，the daily new infection rate on January 30 would have been 0.335% in the exponential growth model，0.129% in the linear growth model，and 0.070% in the logarithm growth model. Correspondingly，there would have been 32 075，24 819 and 20 334 infection cases travelling from Wuhan to other areas of Mainland China，and the number of cumulative confirmed cases as of March 19 in Mainland China would have been 3.3-3.9 times of the officially reported number. Conclusions: Timely taking city-level lockdown measure in Wuhan in the early stage of COVID-19 outbreak is essential in containing the spread of the disease in China.

Retrospective analysis of Chinese epidemic situation model based on elbow cluster analysis / 上海交通大学学报（医学版）

Objective • To explore the correlation patterns of the new coronavirus disease 2019 (COVID-19) epidemic in various provincial administrative regions in China at the early stage of the epidemic, and forecast the following development of epidemic situation. Methods • The data on the COVID-19 epidemic situation in various provincial administrative regions in China published by National Health Commission of People's Republic of China from Jan. 13 to Feb. 13, 2020, were retrospectively analyzed. The elbow cluster analysis method was used to cluster the provincial administrative regions. The SEIR (susceptible-exposed-infectious-recovered) model was used to calculate the basic infection number (R0) of different clusters, whose changing trends were also predicted. Results • According to the prevalence rates, the 34 provincial administrative regions were divided into four types of clusters: Cluster (22 provincial administrative regions), Cluster Ⅱ (9 provincial administrative regions), Cluster III (2 provincial administrative regions) and Cluster (Hubei). The prevalence rate of Hubei was higher than those of other clusters (P=0.000), but the differences in the cure rate and the case-fatality rate among the four clusters were not statistically significant; the R0 values based on the SEIR model of them were 2.764, 3.056, 3.899 and 3.984, respectively. By Feb. 13, 2020, except for Hubei, the cumulative prevalence curves of the other clusters tended to be stable and the cure rates increased. The prevalence rate and case-fatality rate of Hubei were still higher, and the cure rate was lower. Conclusion • From Jan. 13 to Feb. 13, 2020, 34 provincial administrative regions in China can be divided into four clusters according to the severity of the COVID-19 epidemic, and the prevalence rate of Cluster was significantly higher than those of other three clusters; by Feb. 13, 2020, the epidemic situations in the Cluster , Ⅱ and III has been alleviated, and the epidemic situation in Cluster areas were still severe.