| 李卫霞,曹莉,张德昊,蔡畅,黄丽菊,赵建农,宁毅.新型冠状病毒Omicron变异株BA.5.1.3亚型潜伏期研究[J].Chinese journal of Epidemiology,2023,44(3):367-372 |
| 新型冠状病毒Omicron变异株BA.5.1.3亚型潜伏期研究 |
| Study of incubation period of infection with 2019-nCoV Omicron variant BA.5.1.3 |
| Received:December 12, 2022 |
| DOI：10.3760/cma.j.cn112338-20221212-01060 |
| KeyWord: 新型冠状病毒 Omicron变异株 潜伏期 贝叶斯估计 |
| English Key Word: 2019-nCoV Omicron variant Incubation period Bayes estimation |
| FundProject:海南省高层次人才项目（820RC649）；海南省重点研发项目（ZDYF2021GXJS018） |
| Author Name | Affiliation | E-mail | | Li Weixia | Department of Mathematical Statistics, International School of Public Health and One Health, Hainan Medical University, Haikou 571199, China | | | Cao Li | Department of Mathematical Statistics, International School of Public Health and One Health, Hainan Medical University, Haikou 571199, China | | | Zhang Dehao | Department of Mathematical Statistics, International School of Public Health and One Health, Hainan Medical University, Haikou 571199, China | | | Cai Chang | Sanya Center for Disease Control and Prevention, Sanya 572000, China | | | Huang Liju | Sanya Center for Disease Control and Prevention, Sanya 572000, China | | | Zhao Jiannong | Hainan Medical University, Haikou 571199, China | | | Ning Yi | Department of Mathematical Statistics, International School of Public Health and One Health, Hainan Medical University, Haikou 571199, China
The First Affiliated Hospital of Hainan Medical University, Haikou 570102, China | ningyi@vip.163.com |
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| Abstract: |
| 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。 |
| English Abstract: |
| 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. |
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