Objective: To study the incubation period of the infection with 2019-nCoV Omicron variant BA.5.1.3. Methods: Based on the epidemiological survey data of 315 COVID-19 cases and the characteristics of interval censored data structure, log-normal distribution and Gamma distribution were used to estimate the incubation. Bayes estimation was performed for the parameters of each distribution function using discrete time Markov chain Monte Carlo algorithm. Results: The mean age of the 315 COVID-19 cases was (42.01±16.54) years, and men accounted for 30.16%. A total of 156 cases with mean age of (41.65±16.32) years reported the times when symptoms occurred. The log-normal distribution and Gamma distribution indicated that the M (Q1, Q3) of the incubation period from exposure to symptom onset was 2.53 (1.86, 3.44) days and 2.64 (1.91, 3.52) days, respectively, and the M (Q1, Q3) of the incubation period from exposure to the first positive nucleic acid detection was 2.45 (1.76, 3.40) days and 2.57 (1.81, 3.52) days, respectively. Conclusions: The incubation period by Bayes estimation based on log-normal distribution and Gamma distribution, respectively, was similar to each other, and the best distribution of incubation period was Gamma distribution, the difference between the incubation period from exposure to the first positive nucleic acid detection and the incubation period from exposure to symptom onset was small. The median of incubation period of infection caused by Omicron variant BA.5.1.3 was shorter than those of previous Omicron variants.
目的： 研究新型冠状病毒（新冠病毒）感染疫情的Omicron变异株BA.5.1.3亚型的潜伏期。 方法： 基于315例新冠病毒感染者流行病学调查数据，根据区间删失数据的特点，采用log-normal和Gamma两种分布估计潜伏期，利用离散时间马尔科夫链蒙特卡罗算法对分布函数的参数进行贝叶斯估计。 结果： 315例感染者年龄（42.01±16.54）岁，男性占30.16%。其中156例报告了症状出现时间，年龄（41.65±16.32）岁，log-normal和Gamma分布估计发病潜伏期M（Q1，Q3）分别为2.53（1.86，3.44）d及2.64（1.91，3.52）d；估计感染潜伏期M（Q1，Q3）分别为2.45（1.76，3.40）d及2.57（1.81，3.52）d。 结论： 基于log-normal和Gamma分布进行贝叶斯估计的潜伏期接近，潜伏期最佳分布均为Gamma分布，感染潜伏期与发病潜伏期M相差较小，Omicron变异株BA.5.1.3亚型比以往的Omicron变异株的潜伏期M更短。.