Inferring community transmission of SARS-CoV-2 in the United Kingdom using the ONS COVID-19 Infection Survey

IF 8.8 3区 医学 Q1 Medicine
Ruth McCabe , Gabriel Danelian , Jasmina Panovska-Griffiths , Christl A. Donnelly
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引用次数: 0

Abstract

Key epidemiological parameters, including the effective reproduction number, R(t), and the instantaneous growth rate, r(t), generated from an ensemble of models, have been informing public health policy throughout the COVID-19 pandemic in the four nations of the United Kingdom of Great Britain and Northern Ireland (UK). However, estimation of these quantities became challenging with the scaling down of surveillance systems as part of the transition from the “emergency” to “endemic” phase of the pandemic.

The Office for National Statistics (ONS) COVID-19 Infection Survey (CIS) provided an opportunity to continue estimating these parameters in the absence of other data streams. We used a penalised spline model fitted to the publicly-available ONS CIS test positivity estimates to produce a smoothed estimate of the prevalence of SARS-CoV-2 positivity over time. The resulting fitted curve was used to estimate the “ONS-based” R(t) and r(t) across the four nations of the UK. Estimates produced under this model are compared to government-published estimates with particular consideration given to the contribution that this single data stream can offer in the estimation of these parameters.

Depending on the nation and parameter, we found that up to 77% of the variance in the government-published estimates can be explained by the ONS-based estimates, demonstrating the value of this singular data stream to track the epidemic in each of the four nations. We additionally find that the ONS-based estimates uncover epidemic trends earlier than the corresponding government-published estimates.

Our work shows that the ONS CIS can be used to generate key COVID-19 epidemiological parameters across the four UK nations, further underlining the enormous value of such population-level studies of infection. This is not intended as an alternative to ensemble modelling, rather it is intended as a potential solution to the aforementioned challenge faced by public health officials in the UK in early 2022.

利用英国国家统计局 COVID-19 感染调查推断 SARS-CoV-2 在英国的社区传播情况
在 COVID-19 在大不列颠及北爱尔兰联合王国(英国)四国流行的整个过程中,包括有效繁殖数 R(t) 和瞬时增长率 r(t) 在内的关键流行病学参数一直在为公共卫生政策提供信息。然而,随着疫情从 "紧急 "阶段向 "流行 "阶段的过渡,监测系统规模缩小,对这些数量的估算变得具有挑战性。我们使用惩罚性样条曲线模型来拟合公开的国家统计局 CIS 检测阳性率估计值,从而得出 SARS-CoV-2 阳性率随时间变化的平滑估计值。由此得出的拟合曲线用于估算英国四个国家中 "基于 ONS 的 "R(t) 和 r(t)。根据该模型得出的估算值与政府公布的估算值进行了比较,并特别考虑了该单一数据流在估算这些参数时可做出的贡献。我们的工作表明,英国国家统计局的 CIS 可用于生成英国四个国家的 COVID-19 流行病学关键参数,这进一步强调了此类人群感染研究的巨大价值。这并不是为了替代集合建模,而是为了解决英国公共卫生官员在 2022 年初面临的上述挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Infectious Disease Modelling
Infectious Disease Modelling Mathematics-Applied Mathematics
CiteScore
17.00
自引率
3.40%
发文量
73
审稿时长
17 weeks
期刊介绍: Infectious Disease Modelling is an open access journal that undergoes peer-review. Its main objective is to facilitate research that combines mathematical modelling, retrieval and analysis of infection disease data, and public health decision support. The journal actively encourages original research that improves this interface, as well as review articles that highlight innovative methodologies relevant to data collection, informatics, and policy making in the field of public health.
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