Public Health Guidance and Expertise Joint Modelling Cell, Public Health England, Salisbury, UK.
Epidemiol Infect. 2022 May 12;150:e100. doi: 10.1017/S0950268821002776.
This paper presents a method used to rapidly assess the incursion and the establishment of community transmission of suspected SARS-CoV-2 variant of concern Delta (lineage B.1.617.2) into the UK in April and May 2021. The method described is independent of any genetically sequenced data, and so avoids the inherent lag times involved in sequencing of cases. We show that, between 1 April and 12 May 2021, there was a strong correlation between local authorities with high numbers of imported positive cases from India and high COVID-19 case rates, and that this relationship holds as we look at finer geographic detail. Further, we also show that Bolton was an outlier in the relationship, having the highest COVID-19 case rates despite relatively few importations. We use an artificial neural network trained on demographic data, to show that observed importations in Bolton were consistent with similar areas. Finally, using an SEIR transmission model, we show that imported positive cases were a contributing factor to persistent transmission in a number of local authorities, however they could not account for increased case rates observed in Bolton. As such, the outbreak of Delta variant in Bolton was likely not a result of direct importation from overseas, but rather secondary transmission from other regions within the UK.
本文提出了一种用于快速评估疑似 SARS-CoV-2 变体 Delta(谱系 B.1.617.2)在 2021 年 4 月和 5 月入侵和在英国建立社区传播的方法。所描述的方法独立于任何基因测序数据,因此避免了与病例测序相关的固有滞后时间。我们表明,在 2021 年 4 月 1 日至 5 月 12 日期间,当地政府从印度输入的阳性病例数量多与 COVID-19 病例率高之间存在很强的相关性,并且当我们观察更精细的地理细节时,这种关系仍然存在。此外,我们还表明,博尔顿是这种关系中的一个异常值,尽管进口量相对较少,但 COVID-19 病例率却最高。我们使用基于人口统计数据的人工神经网络,表明博尔顿观察到的进口病例与类似地区一致。最后,使用 SEIR 传播模型,我们表明输入的阳性病例是导致一些地方当局持续传播的一个因素,但它们无法解释博尔顿观察到的病例率增加。因此,博尔顿的 Delta 变体爆发不太可能是直接从海外输入的结果,而是英国其他地区的二次传播。