刘天, 徐琴雯, 何诗琪, 阮德欣, 黄继贵, 毛安禄. 基于真实世界数据奥密克戎BA.5.2变异株传播动力学参数估算[J]. 疾病监测, 2023, 38(4): 457-461. DOI: 10.3784/jbjc.202211090483
引用本文: 刘天, 徐琴雯, 何诗琪, 阮德欣, 黄继贵, 毛安禄. 基于真实世界数据奥密克戎BA.5.2变异株传播动力学参数估算[J]. 疾病监测, 2023, 38(4): 457-461. DOI: 10.3784/jbjc.202211090483
Liu Tian, Xu Qinwen, He Shiqi, Ruan Dexin, Huang Jigui, Mao Anlu. Estimation of transmission dynamics parameters for Omicron BA.5.2 variant—based on real-world data[J]. Disease Surveillance, 2023, 38(4): 457-461. DOI: 10.3784/jbjc.202211090483
Citation: Liu Tian, Xu Qinwen, He Shiqi, Ruan Dexin, Huang Jigui, Mao Anlu. Estimation of transmission dynamics parameters for Omicron BA.5.2 variant—based on real-world data[J]. Disease Surveillance, 2023, 38(4): 457-461. DOI: 10.3784/jbjc.202211090483

基于真实世界数据奥密克戎BA.5.2变异株传播动力学参数估算

Estimation of transmission dynamics parameters for Omicron BA.5.2 variant—based on real-world data

  • 摘要:
      目的  利用湖北省荆州市2起由奥密克戎BA.5.2变异株引起的局部疫情数据,估算奥密克戎BA.5.2变异株的传播动力学参数,为认识奥密克戎BA.5.2变异株传播能力提供科学依据。
      方法  采用自行设计表格收集2起疫情中感染关系明确的感染者与被感染者的感染时间、发病时间及首次核酸阳性采样时间等数据。 对获得数据分别拟合对数正态分布、伽马分布和伽马分布计算潜伏期、世代间隔(GT)、代际间隔(SI)。 分别采用指数增长模型(EG)和极大似然估计法(ML)计算基本再生数(R0),取R2较大者作为R0估算值。
      结果  荆州市2起疫情中,疫情A为外市来荆人员在荆州市进行社交活动引起,主要通过同住、同餐、同娱乐和同工作传播,为常见社交传播情形;疫情B涉及人群聚集的批发市场,首发病例进入后停留3 h后随即离开,在农贸市场快速传播并外溢至同住人员,为人群密集场所传播情形。 2起疫情共计纳入感染者与被感染者39对52例的数据用于潜伏期、GT和SI估算。 潜伏期中位数为2.52 d(44例,1.32~4.84 d),SI中位数为2.13 d(37例,1.63~2.64 d),GT中位数为1.91 d(21例,1.05~3.15 d)。 疫情A、疫情B最优拟合模型均为EG模型,对应R2分别为0.62、0.88。疫情A、疫情B拟合的R0分别为5.33(95%CI:2.08~13.44)、22.58(95%CI:7.23~91.36)。 单次暴露感染者观察到暴露至检出最短时间为20~21 h。
      结论  奥密克戎BA.5.2变异株具有潜伏期短、传播速度快、传染性强的特点。 在人群密集场所,奥密克戎BA.5.2变异株传播能力极强。

     

    Abstract:
      Objective  To estimate the transmission dynamics parameters of Omicron BA.5.2 variant and provide a scientific evidence for understanding the infectivity of Omicron BA.5.2.
      Methods  The data of infection time, onset time and first positive sampling time of the infection cases with clear infection relationship in two outbreaks in Jingzhou were collected by using self-designed forms. The obtained data were fitted with log-normal distribution, gamma distribution and gamma distribution to calculate incubation period, generation time (GT), and serial interval (SI). The exponential growth model (EG) and the maximum likelihood estimation method (ML) were used to calculate R0, respectively. The fitted value with the larger R2 was used as the R0 estimate.
      Results  In the 2 outbreaks in Jingzhou, outbreak A was caused by the imported virus spread in local population mainly through people's living, dining, amusing and working together, showing a common social transmission pattern. Outbreak B occurred in a wholesale market, which was caused by an infection case who had stay in the market for 3 hours and subsequent rapid transmission in market workers and their close contacts, showing a clustered transmission pattern. A total of 52 cases in 39 pairs of primary and secondary cases in the 2 outbreaks were included for the estimations of incubation period, GT and SI. The median of incubation period was 2.52 days (44 cases, 1.32−4.84 days), the median of SI was 2.13 days (37 cases, 1.63−2.64 days), and the median of GT was 1.91 days (21 cases, 1.05–3.15 days). The optimal fitting models of outbreak A and outbreak B were all EG models, and the corresponding R2 were 0.62 and 0.88, respectively. The fitted R0s of outbreak A and outbreak B were 5.33 (95%CI: 2.08–13.44) and 22.58 (95%CI: 7.23–91.36), respectively. In some cases, the shortest interval (20−21 hours) between exposure and detection was observed.
      Conclusion  Omicron BA.5.2 variant has the characteristics of short incubation period, rapid transmission speed and strong infectivity. In crowded places, Omicron BA.5.2 variant has very strong transmission ability.

     

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