Key epidemiological parameters,including the effective reproduction number,,and the instantaneous growth rate,,generated from an ensemble of models,have been informing public health policy throughout the COVID-19 pand...Key epidemiological parameters,including the effective reproduction number,,and the instantaneous growth rate,,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”and 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.展开更多
基金This work was supported by the NIHR HPRU in Emerging and Zoonotic Infections,a partnership between the United Kingdom Health Security Agency(UKHSA),University of Oxford,University of Liverpool and Liverpool School of Tropical Medicine[grant number NIHR200907 supporting RM and CAD]the MRC Centre for Global Infectious Disease Analysis[grant number MR/X020258/1],funded by the UK Medical Research Council(MRC)This UK funded award is carried out in the frame of the Global Health EDCTP3 Joint Undertaking.RM was also supported by the UKHSA and the Isaac Newton Institute(INI)Knowledge Transfer Network(KTN)in funding and coordinating a 3-month placement at the UK Health Security Agency,respectively.
文摘Key epidemiological parameters,including the effective reproduction number,,and the instantaneous growth rate,,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”and 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.
