Song Q Q, Zhao H, Fang L Q, Liu W, Zheng C, Zhang Y
School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China.
Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing 100071, China.
Zhonghua Liu Xing Bing Xue Za Zhi. 2020 Apr 10;41(4):461-465. doi: 10.3760/cma.j.cn112338-20200205-00069.
To study the early dynamics of the epidemic of coronavirus disease (COVID-19) in China from 15 to 31 January, 2020, and estimate the corresponding epidemiological parameters (incubation period, generation interval and basic reproduction number) of the epidemic. By means of Weibull, Gamma and Lognormal distributions methods, we estimated the probability distribution of the incubation period and generation interval data obtained from the reported COVID-19 cases. Moreover, the AIC criterion was used to determine the optimal distribution. Considering the epidemic is ongoing, the exponential growth model was used to fit the incidence data of COVID-19 from 10 to 31 January, 2020, and exponential growth method, maximum likelihood method and SEIR model were used to estimate the basic reproduction number. Early COVID-19 cases kept an increase in exponential growth manner before 26 January, 2020, then the increase trend became slower. The average incubation period was 5.01 (95: 4.31-5.69) days; the average generation interval was 6.03 (95: 5.20-6.91) days. The basic reproduction number was estimated to be 3.74 (95: 3.63-3.87), 3.16 (95: 2.90-3.43), and 3.91 (95: 3.71-4.11) by three methods, respectively. The Gamma distribution fits both the generation interval and incubation period best, and the mean value of generation interval is 1.02 day longer than that of incubation period. The relatively high basic reproduction number indicates that the epidemic is still serious; Based on our analysis, the turning point of the epidemic would be seen on 26 January, the growth rate would be lower afterwards.
为研究2020年1月15日至31日中国新型冠状病毒肺炎（COVID-19）疫情的早期动态，并估计该疫情相应的流行病学参数（潜伏期、代间隔和基本再生数）。通过威布尔分布、伽马分布和对数正态分布方法，我们估计了从报告的COVID-19病例中获得的潜伏期和代间隔数据的概率分布。此外，使用AIC准则确定最优分布。考虑到疫情仍在持续，采用指数增长模型拟合2020年1月10日至31日COVID-19的发病数据，并使用指数增长法、最大似然法和SEIR模型估计基本再生数。2020年1月26日前早期COVID-19病例呈指数增长，之后增长趋势变缓。平均潜伏期为5.01（95%：4.31 - 5.69）天；平均代间隔为6.03（95%：5.20 - 6.91）天。三种方法估计的基本再生数分别为3.74（95%：3.63 - 3.87）、3.16（95%：2.90 - 3.43）和3.91（95%：3.71 - 4.11）。伽马分布对代间隔和潜伏期的拟合效果最佳，代间隔的平均值比潜伏期长1.02天。相对较高的基本再生数表明疫情仍很严重；基于我们的分析，疫情转折点将出现在1月26日，之后增长率会降低。
