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Abstract

The coronavirus disease 2019 (COVID-19) has become a major public health concern internationally. To capture the epidemic growing patterns and quantify the transmissibility, some key epidemiological parameters and dynamic models are of significance for helping us to understand the features of COVID-19 and thus informing the strategic decision-making in combating the outbreak. In this study, we review and summarize the recently released research results about the reproduction numbers, incubation period and serial interval of COVID-19. We summarize the estimates as well as estimation approaches adopted to calculate these epidemiological parameters in the existing literature. These studies found that the basic reproduction number is estimated at 2.6, the mean incubation period at about 5.0 days, and the mean serial interval at about 5.5 days. The COVID-19 infections can increase rapidly if it is not controlled. The control measures including the isolation, quarantine, contact tracing, improvement of public awareness, and adoption of self-protection measures can effectively mitigate the COVID-19 outbreak.

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Authors and contacts

Corresponding author:Li Ying-Ke,xjaulyk@163.com; He Dai-Hai,daihai.he@polyu.edu.hk

Funds:Project supported by the General Research Fund of Research Grants Council of Hong Kong, China (Grant No. 15205119), the Alibaba (China)-Hong Kong Polytechnic University Collaborative Research Project, China, and the College Scientific Research Project of Xinjiang Uygur Autonomous Region, China (Grant No. XJEDU2018Y021)

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模型或分布	公式	参数介绍
$\rm {SIR}$模型	$ {\cal{R} }_{0}=1+ {r}/{b} $	r为增长率,b为移出率
${\rm {SEIR}}$模型	${\cal{R} }_{0}=\left(1+ {r}/{b_1}\right)\left(1+ {r}/{b_2}\right)$	$b_1, $ $ b_2$分别为从E,I的移出率
多阶段暴露与感染模型	${\cal{R} }_{0}=\dfrac{\left(1+\dfrac{r}{b_1}\right)^x}{\displaystyle\sum\limits_{i=1}^{y}\left(1+\dfrac{r}{b_2}\right)^{-i} }$	$b_1, $ $b_2$为暴露x, 感染y阶段移出率
正态分布	${\cal{R} }_{0}=\exp\left(rT_{\rm c}-\dfrac{1}{2}r^2\sigma^2\right)$	$T_{\rm c}$为代间隔均值, $\sigma$为方差
德尔塔分布	${\cal{R} }_{0}={\rm e}^{rT_{\rm c}}$	$T_{\rm c}$为代间隔均值
经验分布	${\cal{R} }_{0}=\dfrac{r}{\displaystyle\sum\limits_{i=1}^{n}y_i\dfrac{ {\rm e }^{-ra_{i-1} }-{\rm e}^{-ra_i} }{a_i-a_{i-1} } }$	a_i与$y_i$分别表示年龄与相应频率

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地点	${\cal{R}}_0$	置信区间	截止时间	计算方法	参考文献
武汉市	2.2	[1.40, 2.20]	2020/01	临床诊断推断	[2]
武汉市	2.68	[2.47, 2.86]	2020/01/28	MCMC	[3]
武汉市	4.08	—	2020/01/26	有效再生数${\cal{R}}_{\rm{e}}$	[5]
武汉市	3.0	[0.75, 7.80]	2020/01/24	统计推断	[16]
武汉市	6.47	[5.71, 7.23]	2020/01/15	统计推断	[17]
武汉市	2.56	[2.49, 2.63]	2020/01/24	极大似然	[18]
武汉市	2.3	—	2020/01/24	有效再生数${\cal{R}}_{\rm{e}}$	[19]
武汉市	2.23	[1.77, 3.00]	2020/01/24	有效再生数${\cal{R}}_{\rm{e}}$	[20]
武汉市	7.05	[6.11, 8.18]	2020/02/08	最大似然	[21]
湖北省	2.56	[4.70, 6.60]	2020/01/25	SEIR仓室模型	[22]
武汉市	2.24	[1.96, 2.25]	2020/01/24	极大似然	[23]
注:表2—表4中参考文献数据均来自国家卫健委、湖北卫健委、中国CDC等网站及已发表文献.

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时间/d	置信区间	截止时间	估计方法	参考文献
M: 7.5	[5.3, 19.0]	2020/01	最优化	[1]
M: 4.41	—	2020/02/02	最优化	[24]
M: 4.7	[3.7, 6.0]	2020/02	最优化	[25]

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点估计	置信区间	分布形式	截止时间	估计方法	参考文献
M: 5.2	[4.1, 7.0]	指数增长	2020/01/22	—	[1]
M: 6.4	[5.6, 7.7]	威布尔	2020/01/29	贝叶斯	[6]
M: 5.0	[4.2, 6.0]	对数正态	2020/01/23	最优化	[7]
Me: 5.2	[4.4, 6.0]	对数正态	2020/01/30	最优化	[7]
Me: 4.0	—	经验分布	2020/01/29	非参数估计	[11]
注:M:n表示均值是n天,Me:n表示中位数是n天.表4含义表示相同.

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Vol. 69, Issue 9 - 2020-05-05

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