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Eales O, Riley S. Differences between the true reproduction number and the apparent reproduction number of an epidemic time series. Epidemics 2024; 46:100742. [PMID: 38227994 DOI: 10.1016/j.epidem.2024.100742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 12/21/2023] [Accepted: 01/11/2024] [Indexed: 01/18/2024] Open
Abstract
The time-varying reproduction number R(t) measures the number of new infections per infectious individual and is closely correlated with the time series of infection incidence by definition. The timings of actual infections are rarely known, and analysis of epidemics usually relies on time series data for other outcomes such as symptom onset. A common implicit assumption, when estimating R(t) from an epidemic time series, is that R(t) has the same relationship with these downstream outcomes as it does with the time series of incidence. However, this assumption is unlikely to be valid given that most epidemic time series are not perfect proxies of incidence. Rather they represent convolutions of incidence with uncertain delay distributions. Here we define the apparent time-varying reproduction number, RA(t), the reproduction number calculated from a downstream epidemic time series and demonstrate how differences between RA(t) and R(t) depend on the convolution function. The mean of the convolution function sets a time offset between the two signals, whilst the variance of the convolution function introduces a relative distortion between them. We present the convolution functions of epidemic time series that were available during the SARS-CoV-2 pandemic. Infection prevalence, measured by random sampling studies, presents fewer biases than other epidemic time series. Here we show that additionally the mean and variance of its convolution function were similar to that obtained from traditional surveillance based on mass-testing and could be reduced using more frequent testing, or by using stricter thresholds for positivity. Infection prevalence studies continue to be a versatile tool for tracking the temporal trends of R(t), and with additional refinements to their study protocol, will be of even greater utility during any future epidemics or pandemics.
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Affiliation(s)
- Oliver Eales
- Infectious Disease Dynamics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia; School of Public Health, Imperial College London, London, United Kingdom; MRC Centre for Global infectious Disease Analysis, Imperial College London, London, United Kingdom; Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom.
| | - Steven Riley
- School of Public Health, Imperial College London, London, United Kingdom; MRC Centre for Global infectious Disease Analysis, Imperial College London, London, United Kingdom; Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom.
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Eales O, Plank MJ, Cowling BJ, Howden BP, Kucharski AJ, Sullivan SG, Vandemaele K, Viboud C, Riley S, McCaw JM, Shearer FM. Key Challenges for Respiratory Virus Surveillance while Transitioning out of Acute Phase of COVID-19 Pandemic. Emerg Infect Dis 2024; 30:e230768. [PMID: 38190760 PMCID: PMC10826770 DOI: 10.3201/eid3002.230768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024] Open
Abstract
To support the ongoing management of viral respiratory diseases while transitioning out of the acute phase of the COVID-19 pandemic, many countries are moving toward an integrated model of surveillance for SARS-CoV-2, influenza virus, and other respiratory pathogens. Although many surveillance approaches catalyzed by the COVID-19 pandemic provide novel epidemiologic insight, continuing them as implemented during the pandemic is unlikely to be feasible for nonemergency surveillance, and many have already been scaled back. Furthermore, given anticipated cocirculation of SARS-CoV-2 and influenza virus, surveillance activities in place before the pandemic require review and adjustment to ensure their ongoing value for public health. In this report, we highlight key challenges for the development of integrated models of surveillance. We discuss the relative strengths and limitations of different surveillance practices and studies as well as their contribution to epidemiologic assessment, forecasting, and public health decision-making.
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Eales O, Haw D, Wang H, Atchison C, Ashby D, Cooke GS, Barclay W, Ward H, Darzi A, Donnelly CA, Chadeau-Hyam M, Elliott P, Riley S. Dynamics of SARS-CoV-2 infection hospitalisation and infection fatality ratios over 23 months in England. PLoS Biol 2023; 21:e3002118. [PMID: 37228015 DOI: 10.1371/journal.pbio.3002118] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 04/11/2023] [Indexed: 05/27/2023] Open
Abstract
The relationship between prevalence of infection and severe outcomes such as hospitalisation and death changed over the course of the COVID-19 pandemic. Reliable estimates of the infection fatality ratio (IFR) and infection hospitalisation ratio (IHR) along with the time-delay between infection and hospitalisation/death can inform forecasts of the numbers/timing of severe outcomes and allow healthcare services to better prepare for periods of increased demand. The REal-time Assessment of Community Transmission-1 (REACT-1) study estimated swab positivity for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in England approximately monthly from May 2020 to March 2022. Here, we analyse the changing relationship between prevalence of swab positivity and the IFR and IHR over this period in England, using publicly available data for the daily number of deaths and hospitalisations, REACT-1 swab positivity data, time-delay models, and Bayesian P-spline models. We analyse data for all age groups together, as well as in 2 subgroups: those aged 65 and over and those aged 64 and under. Additionally, we analysed the relationship between swab positivity and daily case numbers to estimate the case ascertainment rate of England's mass testing programme. During 2020, we estimated the IFR to be 0.67% and the IHR to be 2.6%. By late 2021/early 2022, the IFR and IHR had both decreased to 0.097% and 0.76%, respectively. The average case ascertainment rate over the entire duration of the study was estimated to be 36.1%, but there was some significant variation in continuous estimates of the case ascertainment rate. Continuous estimates of the IFR and IHR of the virus were observed to increase during the periods of Alpha and Delta's emergence. During periods of vaccination rollout, and the emergence of the Omicron variant, the IFR and IHR decreased. During 2020, we estimated a time-lag of 19 days between hospitalisation and swab positivity, and 26 days between deaths and swab positivity. By late 2021/early 2022, these time-lags had decreased to 7 days for hospitalisations and 18 days for deaths. Even though many populations have high levels of immunity to SARS-CoV-2 from vaccination and natural infection, waning of immunity and variant emergence will continue to be an upwards pressure on the IHR and IFR. As investments in community surveillance of SARS-CoV-2 infection are scaled back, alternative methods are required to accurately track the ever-changing relationship between infection, hospitalisation, and death and hence provide vital information for healthcare provision and utilisation.
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Affiliation(s)
- Oliver Eales
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - David Haw
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - Haowei Wang
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - Christina Atchison
- School of Public Health, Imperial College London, London, United Kingdom
| | - Deborah Ashby
- School of Public Health, Imperial College London, London, United Kingdom
| | - Graham S Cooke
- Department of Infectious Disease, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London, United Kingdom
| | - Wendy Barclay
- Department of Infectious Disease, Imperial College London, London, United Kingdom
| | - Helen Ward
- School of Public Health, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London, United Kingdom
| | - Ara Darzi
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London, United Kingdom
- Institute of Global Health Innovation, Imperial College London, London, United Kingdom
| | - Christl A Donnelly
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Marc Chadeau-Hyam
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Paul Elliott
- School of Public Health, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Health Data Research (HDR) UK London at Imperial College, London, United Kingdom
- UK Dementia Research Institute at Imperial College, London, United Kingdom
| | - Steven Riley
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
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Elliott P, Whitaker M, Tang D, Eales O, Steyn N, Bodinier B, Wang H, Elliott J, Atchison C, Ashby D, Barclay W, Taylor G, Darzi A, Cooke GS, Ward H, Donnelly CA, Riley S, Chadeau-Hyam M. Design and Implementation of a National SARS-CoV-2 Monitoring Program in England: REACT-1 Study. Am J Public Health 2023; 113:545-554. [PMID: 36893367 PMCID: PMC10088956 DOI: 10.2105/ajph.2023.307230] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/10/2023] [Indexed: 03/11/2023]
Abstract
Data System. The REal-time Assessment of Community Transmission-1 (REACT-1) Study was funded by the Department of Health and Social Care in England to provide reliable and timely estimates of prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection over time, by person and place. Data Collection/Processing. The study team (researchers from Imperial College London and its logistics partner Ipsos) wrote to named individuals aged 5 years and older in random cross-sections of the population of England, using the National Health Service list of patients registered with a general practitioner (near-universal coverage) as a sampling frame. We collected data over 2 to 3 weeks approximately every month across 19 rounds of data collection from May 1, 2020, to March 31, 2022. Data Analysis/Dissemination. We have disseminated the data and study materials widely via the study Web site, preprints, publications in peer-reviewed journals, and the media. We make available data tabulations, suitably anonymized to protect participant confidentiality, on request to the study's data access committee. Public Health Implications. The study provided inter alia real-time data on SARS-CoV-2 prevalence over time, by area, and by sociodemographic variables; estimates of vaccine effectiveness; and symptom profiles, and detected emergence of new variants based on viral genome sequencing. (Am J Public Health. 2023;113(5):545-554. https://doi.org/10.2105/AJPH.2023.307230).
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Affiliation(s)
- Paul Elliott
- Paul Elliott, Matthew Whitaker, David Tang, Oliver Eales, Barbara Bodinier, Haowei Wang, Christina Atchison, Deborah Ashby, Helen Ward, and Marc Chadeu-Hyam are with the School of Public Health, Imperial College London, UK. Nicholas Steyn and Christl A. Donnelly are with the Department of Statistics, University of Oxford, Oxford, UK. Joshua Elliott is with the Imperial College Healthcare NHS Trust, London. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Wendy Barclay, Graham Taylor, and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Matthew Whitaker
- Paul Elliott, Matthew Whitaker, David Tang, Oliver Eales, Barbara Bodinier, Haowei Wang, Christina Atchison, Deborah Ashby, Helen Ward, and Marc Chadeu-Hyam are with the School of Public Health, Imperial College London, UK. Nicholas Steyn and Christl A. Donnelly are with the Department of Statistics, University of Oxford, Oxford, UK. Joshua Elliott is with the Imperial College Healthcare NHS Trust, London. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Wendy Barclay, Graham Taylor, and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - David Tang
- Paul Elliott, Matthew Whitaker, David Tang, Oliver Eales, Barbara Bodinier, Haowei Wang, Christina Atchison, Deborah Ashby, Helen Ward, and Marc Chadeu-Hyam are with the School of Public Health, Imperial College London, UK. Nicholas Steyn and Christl A. Donnelly are with the Department of Statistics, University of Oxford, Oxford, UK. Joshua Elliott is with the Imperial College Healthcare NHS Trust, London. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Wendy Barclay, Graham Taylor, and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Oliver Eales
- Paul Elliott, Matthew Whitaker, David Tang, Oliver Eales, Barbara Bodinier, Haowei Wang, Christina Atchison, Deborah Ashby, Helen Ward, and Marc Chadeu-Hyam are with the School of Public Health, Imperial College London, UK. Nicholas Steyn and Christl A. Donnelly are with the Department of Statistics, University of Oxford, Oxford, UK. Joshua Elliott is with the Imperial College Healthcare NHS Trust, London. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Wendy Barclay, Graham Taylor, and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Nicholas Steyn
- Paul Elliott, Matthew Whitaker, David Tang, Oliver Eales, Barbara Bodinier, Haowei Wang, Christina Atchison, Deborah Ashby, Helen Ward, and Marc Chadeu-Hyam are with the School of Public Health, Imperial College London, UK. Nicholas Steyn and Christl A. Donnelly are with the Department of Statistics, University of Oxford, Oxford, UK. Joshua Elliott is with the Imperial College Healthcare NHS Trust, London. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Wendy Barclay, Graham Taylor, and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Barbara Bodinier
- Paul Elliott, Matthew Whitaker, David Tang, Oliver Eales, Barbara Bodinier, Haowei Wang, Christina Atchison, Deborah Ashby, Helen Ward, and Marc Chadeu-Hyam are with the School of Public Health, Imperial College London, UK. Nicholas Steyn and Christl A. Donnelly are with the Department of Statistics, University of Oxford, Oxford, UK. Joshua Elliott is with the Imperial College Healthcare NHS Trust, London. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Wendy Barclay, Graham Taylor, and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Haowei Wang
- Paul Elliott, Matthew Whitaker, David Tang, Oliver Eales, Barbara Bodinier, Haowei Wang, Christina Atchison, Deborah Ashby, Helen Ward, and Marc Chadeu-Hyam are with the School of Public Health, Imperial College London, UK. Nicholas Steyn and Christl A. Donnelly are with the Department of Statistics, University of Oxford, Oxford, UK. Joshua Elliott is with the Imperial College Healthcare NHS Trust, London. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Wendy Barclay, Graham Taylor, and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Joshua Elliott
- Paul Elliott, Matthew Whitaker, David Tang, Oliver Eales, Barbara Bodinier, Haowei Wang, Christina Atchison, Deborah Ashby, Helen Ward, and Marc Chadeu-Hyam are with the School of Public Health, Imperial College London, UK. Nicholas Steyn and Christl A. Donnelly are with the Department of Statistics, University of Oxford, Oxford, UK. Joshua Elliott is with the Imperial College Healthcare NHS Trust, London. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Wendy Barclay, Graham Taylor, and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Christina Atchison
- Paul Elliott, Matthew Whitaker, David Tang, Oliver Eales, Barbara Bodinier, Haowei Wang, Christina Atchison, Deborah Ashby, Helen Ward, and Marc Chadeu-Hyam are with the School of Public Health, Imperial College London, UK. Nicholas Steyn and Christl A. Donnelly are with the Department of Statistics, University of Oxford, Oxford, UK. Joshua Elliott is with the Imperial College Healthcare NHS Trust, London. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Wendy Barclay, Graham Taylor, and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Deborah Ashby
- Paul Elliott, Matthew Whitaker, David Tang, Oliver Eales, Barbara Bodinier, Haowei Wang, Christina Atchison, Deborah Ashby, Helen Ward, and Marc Chadeu-Hyam are with the School of Public Health, Imperial College London, UK. Nicholas Steyn and Christl A. Donnelly are with the Department of Statistics, University of Oxford, Oxford, UK. Joshua Elliott is with the Imperial College Healthcare NHS Trust, London. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Wendy Barclay, Graham Taylor, and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Wendy Barclay
- Paul Elliott, Matthew Whitaker, David Tang, Oliver Eales, Barbara Bodinier, Haowei Wang, Christina Atchison, Deborah Ashby, Helen Ward, and Marc Chadeu-Hyam are with the School of Public Health, Imperial College London, UK. Nicholas Steyn and Christl A. Donnelly are with the Department of Statistics, University of Oxford, Oxford, UK. Joshua Elliott is with the Imperial College Healthcare NHS Trust, London. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Wendy Barclay, Graham Taylor, and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Graham Taylor
- Paul Elliott, Matthew Whitaker, David Tang, Oliver Eales, Barbara Bodinier, Haowei Wang, Christina Atchison, Deborah Ashby, Helen Ward, and Marc Chadeu-Hyam are with the School of Public Health, Imperial College London, UK. Nicholas Steyn and Christl A. Donnelly are with the Department of Statistics, University of Oxford, Oxford, UK. Joshua Elliott is with the Imperial College Healthcare NHS Trust, London. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Wendy Barclay, Graham Taylor, and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Ara Darzi
- Paul Elliott, Matthew Whitaker, David Tang, Oliver Eales, Barbara Bodinier, Haowei Wang, Christina Atchison, Deborah Ashby, Helen Ward, and Marc Chadeu-Hyam are with the School of Public Health, Imperial College London, UK. Nicholas Steyn and Christl A. Donnelly are with the Department of Statistics, University of Oxford, Oxford, UK. Joshua Elliott is with the Imperial College Healthcare NHS Trust, London. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Wendy Barclay, Graham Taylor, and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Graham S Cooke
- Paul Elliott, Matthew Whitaker, David Tang, Oliver Eales, Barbara Bodinier, Haowei Wang, Christina Atchison, Deborah Ashby, Helen Ward, and Marc Chadeu-Hyam are with the School of Public Health, Imperial College London, UK. Nicholas Steyn and Christl A. Donnelly are with the Department of Statistics, University of Oxford, Oxford, UK. Joshua Elliott is with the Imperial College Healthcare NHS Trust, London. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Wendy Barclay, Graham Taylor, and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Helen Ward
- Paul Elliott, Matthew Whitaker, David Tang, Oliver Eales, Barbara Bodinier, Haowei Wang, Christina Atchison, Deborah Ashby, Helen Ward, and Marc Chadeu-Hyam are with the School of Public Health, Imperial College London, UK. Nicholas Steyn and Christl A. Donnelly are with the Department of Statistics, University of Oxford, Oxford, UK. Joshua Elliott is with the Imperial College Healthcare NHS Trust, London. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Wendy Barclay, Graham Taylor, and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Christl A Donnelly
- Paul Elliott, Matthew Whitaker, David Tang, Oliver Eales, Barbara Bodinier, Haowei Wang, Christina Atchison, Deborah Ashby, Helen Ward, and Marc Chadeu-Hyam are with the School of Public Health, Imperial College London, UK. Nicholas Steyn and Christl A. Donnelly are with the Department of Statistics, University of Oxford, Oxford, UK. Joshua Elliott is with the Imperial College Healthcare NHS Trust, London. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Wendy Barclay, Graham Taylor, and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Steven Riley
- Paul Elliott, Matthew Whitaker, David Tang, Oliver Eales, Barbara Bodinier, Haowei Wang, Christina Atchison, Deborah Ashby, Helen Ward, and Marc Chadeu-Hyam are with the School of Public Health, Imperial College London, UK. Nicholas Steyn and Christl A. Donnelly are with the Department of Statistics, University of Oxford, Oxford, UK. Joshua Elliott is with the Imperial College Healthcare NHS Trust, London. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Wendy Barclay, Graham Taylor, and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
| | - Marc Chadeau-Hyam
- Paul Elliott, Matthew Whitaker, David Tang, Oliver Eales, Barbara Bodinier, Haowei Wang, Christina Atchison, Deborah Ashby, Helen Ward, and Marc Chadeu-Hyam are with the School of Public Health, Imperial College London, UK. Nicholas Steyn and Christl A. Donnelly are with the Department of Statistics, University of Oxford, Oxford, UK. Joshua Elliott is with the Imperial College Healthcare NHS Trust, London. Ara Darzi is with the Institute of Global Health Innovation, Imperial College London. Wendy Barclay, Graham Taylor, and Graham S. Cooke are with the Department of Infectious Disease, Imperial College London
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Eales O, Page AJ, Tang SN, Walters CE, Wang H, Haw D, Trotter AJ, Le Viet T, Foster-Nyarko E, Prosolek S, Atchison C, Ashby D, Cooke G, Barclay W, Donnelly CA, O’Grady J, Volz E, Darzi A, Ward H, Elliott P, Riley S. The use of representative community samples to assess SARS-CoV-2 lineage competition: Alpha outcompetes Beta and wild-type in England from January to March 2021. Microb Genom 2023; 9:mgen000887. [PMID: 36745545 PMCID: PMC9997751 DOI: 10.1099/mgen.0.000887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Genomic surveillance for SARS-CoV-2 lineages informs our understanding of possible future changes in transmissibility and vaccine efficacy and will be a high priority for public health for the foreseeable future. However, small changes in the frequency of one lineage over another are often difficult to interpret because surveillance samples are obtained using a variety of methods all of which are known to contain biases. As a case study, using an approach which is largely free of biases, we here describe lineage dynamics and phylogenetic relationships of the Alpha and Beta variant in England during the first 3 months of 2021 using sequences obtained from a random community sample who provided a throat and nose swab for rt-PCR as part of the REal-time Assessment of Community Transmission-1 (REACT-1) study. Overall, diversity decreased during the first quarter of 2021, with the Alpha variant (first identified in Kent) becoming predominant, driven by a reproduction number 0.3 higher than for the prior wild-type. During January, positive samples were more likely to be Alpha in those aged 18 to 54 years old. Although individuals infected with the Alpha variant were no more likely to report one or more classic COVID-19 symptoms compared to those infected with wild-type, they were more likely to be antibody-positive 6 weeks after infection. Further, viral load was higher in those infected with the Alpha variant as measured by cycle threshold (Ct) values. The presence of infections with non-imported Beta variant (first identified in South Africa) during January, but not during February or March, suggests initial establishment in the community followed by fade-out. However, this occurred during a period of stringent social distancing. These results highlight how sequence data from representative community surveys such as REACT-1 can augment routine genomic surveillance during periods of lineage diversity.
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Affiliation(s)
- Oliver Eales
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
- *Correspondence: Oliver Eales,
| | | | - Sonja N. Tang
- School of Public Health, Imperial College London, London, UK
| | - Caroline E. Walters
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Haowei Wang
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - David Haw
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | | | | | | | | | | | - Deborah Ashby
- School of Public Health, Imperial College London, London, UK
| | - Graham Cooke
- Department of Infectious Disease, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
| | - Wendy Barclay
- Department of Infectious Disease, Imperial College London, London, UK
| | - Christl A. Donnelly
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | | | - Erik Volz
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | | | - Ara Darzi
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- Institute of Global Health Innovation at Imperial College London, London, UK
| | - Helen Ward
- School of Public Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
| | - Paul Elliott
- School of Public Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Health Data Research (HDR) UK London at Imperial College, London, UK
- UK Dementia Research Institute at Imperial College, London, UK
- *Correspondence: Paul Elliott,
| | - Steven Riley
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
- *Correspondence: Steven Riley,
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6
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Eales O, Wang H, Haw D, Ainslie KEC, Walters CE, Atchison C, Cooke G, Barclay W, Ward H, Darzi A, Ashby D, Donnelly CA, Elliott P, Riley S. Trends in SARS-CoV-2 infection prevalence during England's roadmap out of lockdown, January to July 2021. PLoS Comput Biol 2022; 18:e1010724. [PMID: 36417468 PMCID: PMC9728904 DOI: 10.1371/journal.pcbi.1010724] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 12/07/2022] [Accepted: 11/07/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Following rapidly rising COVID-19 case numbers, England entered a national lockdown on 6 January 2021, with staged relaxations of restrictions from 8 March 2021 onwards. AIM We characterise how the lockdown and subsequent easing of restrictions affected trends in SARS-CoV-2 infection prevalence. METHODS On average, risk of infection is proportional to infection prevalence. The REal-time Assessment of Community Transmission-1 (REACT-1) study is a repeat cross-sectional study of over 98,000 people every round (rounds approximately monthly) that estimates infection prevalence in England. We used Bayesian P-splines to estimate prevalence and the time-varying reproduction number (Rt) nationally, regionally and by age group from round 8 (beginning 6 January 2021) to round 13 (ending 12 July 2021) of REACT-1. As a comparator, a separate segmented-exponential model was used to quantify the impact on Rt of each relaxation of restrictions. RESULTS Following an initial plateau of 1.54% until mid-January, infection prevalence decreased until 13 May when it reached a minimum of 0.09%, before increasing until the end of the study to 0.76%. Following the first easing of restrictions, which included schools reopening, the reproduction number Rt increased by 82% (55%, 108%), but then decreased by 61% (82%, 53%) at the second easing of restrictions, which was timed to match the Easter school holidays. Following further relaxations of restrictions, the observed Rt increased steadily, though the increase due to these restrictions being relaxed was offset by the effects of vaccination and also affected by the rapid rise of Delta. There was a high degree of synchrony in the temporal patterns of prevalence between regions and age groups. CONCLUSION High-resolution prevalence data fitted to P-splines allowed us to show that the lockdown was effective at reducing risk of infection with school holidays/closures playing a significant part.
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Affiliation(s)
- Oliver Eales
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - Haowei Wang
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - David Haw
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - Kylie E. C. Ainslie
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Caroline E. Walters
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - Christina Atchison
- School of Public Health, Imperial College London, London, United Kingdom
| | - Graham Cooke
- Department of Infectious Disease, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London
| | - Wendy Barclay
- Department of Infectious Disease, Imperial College London, London, United Kingdom
| | - Helen Ward
- School of Public Health, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London
| | - Ara Darzi
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London
- Institute of Global Health Innovation at Imperial College London, London, United Kingdom
| | - Deborah Ashby
- School of Public Health, Imperial College London, London, United Kingdom
| | - Christl A. Donnelly
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Paul Elliott
- School of Public Health, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Health Data Research (HDR) UK London at Imperial College, London, United Kingdom
- UK Dementia Research Institute at Imperial College, London, United Kingdom
| | - Steven Riley
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
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7
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Chadeau-Hyam M, Tang D, Eales O, Bodinier B, Wang H, Jonnerby J, Whitaker M, Elliott J, Haw D, Walters CE, Atchison C, Diggle PJ, Page AJ, Ashby D, Barclay W, Taylor G, Cooke G, Ward H, Darzi A, Donnelly CA, Elliott P. Omicron SARS-CoV-2 epidemic in England during February 2022: A series of cross-sectional community surveys. Lancet Reg Health Eur 2022; 21:100462. [PMID: 35915784 PMCID: PMC9330654 DOI: 10.1016/j.lanepe.2022.100462] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Background The Omicron wave of COVID-19 in England peaked in January 2022 resulting from the rapid transmission of the Omicron BA.1 variant. We investigate the spread and dynamics of the SARS-CoV-2 epidemic in the population of England during February 2022, by region, age and main SARS-CoV-2 sub-lineage. Methods In the REal-time Assessment of Community Transmission-1 (REACT-1) study we obtained data from a random sample of 94,950 participants with valid throat and nose swab results by RT-PCR during round 18 (8 February to 1 March 2022). Findings We estimated a weighted mean SARS-CoV-2 prevalence of 2.88% (95% credible interval [CrI] 2.76-3.00), with a within-round effective reproduction number (R) overall of 0.94 (0·91-0.96). While within-round weighted prevalence fell among children (aged 5 to 17 years) and adults aged 18 to 54 years, we observed a level or increasing weighted prevalence among those aged 55 years and older with an R of 1.04 (1.00-1.09). Among 1,616 positive samples with sublineages determined, one (0.1% [0.0-0.3]) corresponded to XE BA.1/BA.2 recombinant and the remainder were Omicron: N=1047, 64.8% (62.4-67.2) were BA.1; N=568, 35.2% (32.8-37.6) were BA.2. We estimated an R additive advantage for BA.2 (vs BA.1) of 0.38 (0.34-0.41). The highest proportion of BA.2 among positives was found in London. Interpretation In February 2022, infection prevalence in England remained high with level or increasing rates of infection in older people and an uptick in hospitalisations. Ongoing surveillance of both survey and hospitalisations data is required. Funding Department of Health and Social Care, England.
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Affiliation(s)
- Marc Chadeau-Hyam
- School of Public Health, Imperial College London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, UK
| | - David Tang
- School of Public Health, Imperial College London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, UK
| | - Oliver Eales
- School of Public Health, Imperial College London, UK
- MRC Centre for Global infectious Disease Analysis
- Jameel Institute, Imperial College London, UK
| | - Barbara Bodinier
- School of Public Health, Imperial College London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, UK
| | - Haowei Wang
- School of Public Health, Imperial College London, UK
- MRC Centre for Global infectious Disease Analysis
- Jameel Institute, Imperial College London, UK
| | - Jakob Jonnerby
- School of Public Health, Imperial College London, UK
- MRC Centre for Global infectious Disease Analysis
- Jameel Institute, Imperial College London, UK
| | - Matthew Whitaker
- School of Public Health, Imperial College London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, UK
| | | | - David Haw
- School of Public Health, Imperial College London, UK
- MRC Centre for Global infectious Disease Analysis
- Jameel Institute, Imperial College London, UK
| | - Caroline E. Walters
- School of Public Health, Imperial College London, UK
- MRC Centre for Global infectious Disease Analysis
- Jameel Institute, Imperial College London, UK
| | | | - Peter J. Diggle
- CHICAS, Lancaster Medical School, Lancaster University, UK and Health Data Research, UK
| | | | - Deborah Ashby
- School of Public Health, Imperial College London, UK
| | - Wendy Barclay
- Department of Infectious Disease, Imperial College London, UK
| | - Graham Taylor
- Department of Infectious Disease, Imperial College London, UK
| | - Graham Cooke
- Imperial College Healthcare NHS Trust, UK
- National Institute for Health Research Imperial Biomedical Research Centre, UK
| | - Helen Ward
- School of Public Health, Imperial College London, UK
- Imperial College Healthcare NHS Trust, UK
- National Institute for Health Research Imperial Biomedical Research Centre, UK
| | - Ara Darzi
- Imperial College Healthcare NHS Trust, UK
- Institute of Global Health Innovation, Imperial College London, UK
| | - Christl A. Donnelly
- School of Public Health, Imperial College London, UK
- MRC Centre for Global infectious Disease Analysis
- Jameel Institute, Imperial College London, UK
- Department of Statistics, University of Oxford, UK
| | - Paul Elliott
- School of Public Health, Imperial College London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, UK
- Imperial College Healthcare NHS Trust, UK
- National Institute for Health Research Imperial Biomedical Research Centre, UK
- Health Data Research (HDR) UK, Imperial College London, UK
- UK Dementia Research Institute, Imperial College London, UK
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8
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Eales O, Ainslie KEC, Walters CE, Wang H, Atchison C, Ashby D, Donnelly CA, Cooke G, Barclay W, Ward H, Darzi A, Elliott P, Riley S. Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number. Epidemics 2022; 40:100604. [PMID: 35780515 PMCID: PMC9220254 DOI: 10.1016/j.epidem.2022.100604] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 05/31/2022] [Accepted: 06/17/2022] [Indexed: 02/09/2023] Open
Abstract
The time-varying reproduction number (Rt) can change rapidly over the course of a pandemic due to changing restrictions, behaviours, and levels of population immunity. Many methods exist that allow the estimation of Rt from case data. However, these are not easily adapted to point prevalence data nor can they infer Rt across periods of missing data. We developed a Bayesian P-spline model suitable for fitting to a wide range of epidemic time-series, including point-prevalence data. We demonstrate the utility of the model by fitting to periodic daily SARS-CoV-2 swab-positivity data in England from the first 7 rounds (May 2020-December 2020) of the REal-time Assessment of Community Transmission-1 (REACT-1) study. Estimates of Rt over the period of two subsequent rounds (6-8 weeks) and single rounds (2-3 weeks) inferred using the Bayesian P-spline model were broadly consistent with estimates from a simple exponential model, with overlapping credible intervals. However, there were sometimes substantial differences in point estimates. The Bayesian P-spline model was further able to infer changes in Rt over shorter periods tracking a temporary increase above one during late-May 2020, a gradual increase in Rt over the summer of 2020 as restrictions were eased, and a reduction in Rt during England's second national lockdown followed by an increase as the Alpha variant surged. The model is robust against both under-fitting and over-fitting and is able to interpolate between periods of available data; it is a particularly versatile model when growth rate can change over small timescales, as in the current SARS-CoV-2 pandemic. This work highlights the importance of pairing robust methods with representative samples to track pandemics.
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Affiliation(s)
- Oliver Eales
- School of Public Health, Imperial College London, London, United Kingdom; MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom.
| | - Kylie E C Ainslie
- School of Public Health, Imperial College London, London, United Kingdom; MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom; Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
| | - Caroline E Walters
- School of Public Health, Imperial College London, London, United Kingdom; MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom.
| | - Haowei Wang
- School of Public Health, Imperial College London, London, United Kingdom; MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom.
| | - Christina Atchison
- School of Public Health, Imperial College London, London, United Kingdom.
| | - Deborah Ashby
- School of Public Health, Imperial College London, London, United Kingdom.
| | - Christl A Donnelly
- School of Public Health, Imperial College London, London, United Kingdom; MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom; Department of Statistics, University of Oxford, Oxford, United Kingdom.
| | - Graham Cooke
- Department of Infectious Disease, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, Imperial College London, London, United Kingdom; National Institute for Health Research, Imperial Biomedical Research Centre, Imperial College London, London, United Kingdom.
| | - Wendy Barclay
- Department of Infectious Disease, Imperial College London, London, United Kingdom.
| | - Helen Ward
- School of Public Health, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, Imperial College London, London, United Kingdom; National Institute for Health Research, Imperial Biomedical Research Centre, Imperial College London, London, United Kingdom.
| | - Ara Darzi
- Imperial College Healthcare NHS Trust, Imperial College London, London, United Kingdom; National Institute for Health Research, Imperial Biomedical Research Centre, Imperial College London, London, United Kingdom; Institute of Global Health Innovation, Imperial College London, London, United Kingdom.
| | - Paul Elliott
- School of Public Health, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, Imperial College London, London, United Kingdom; National Institute for Health Research, Imperial Biomedical Research Centre, Imperial College London, London, United Kingdom; MRC Centre for Environment and Health, Imperial College London, London, United Kingdom; Health Data Research (HDR), Imperial College London, London, United Kingdom; UK Dementia Research Institute, Imperial College London, London, United Kingdom.
| | - Steven Riley
- School of Public Health, Imperial College London, London, United Kingdom; MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom.
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9
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Eales O, de Oliveira Martins L, Page AJ, Wang H, Bodinier B, Tang D, Haw D, Jonnerby J, Atchison C, Ashby D, Barclay W, Taylor G, Cooke G, Ward H, Darzi A, Riley S, Elliott P, Donnelly CA, Chadeau-Hyam M. Dynamics of competing SARS-CoV-2 variants during the Omicron epidemic in England. Nat Commun 2022; 13:4375. [PMID: 35902613 PMCID: PMC9330949 DOI: 10.1038/s41467-022-32096-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 07/14/2022] [Indexed: 12/15/2022] Open
Abstract
The SARS-CoV-2 pandemic has been characterised by the regular emergence of genomic variants. With natural and vaccine-induced population immunity at high levels, evolutionary pressure favours variants better able to evade SARS-CoV-2 neutralising antibodies. The Omicron variant (first detected in November 2021) exhibited a high degree of immune evasion, leading to increased infection rates worldwide. However, estimates of the magnitude of this Omicron wave have often relied on routine testing data, which are prone to several biases. Using data from the REal-time Assessment of Community Transmission-1 (REACT-1) study, a series of cross-sectional surveys assessing prevalence of SARS-CoV-2 infection in England, we estimated the dynamics of England's Omicron wave (from 9 September 2021 to 1 March 2022). We estimate an initial peak in national Omicron prevalence of 6.89% (5.34%, 10.61%) during January 2022, followed by a resurgence in SARS-CoV-2 infections as the more transmissible Omicron sub-lineage, BA.2 replaced BA.1 and BA.1.1. Assuming the emergence of further distinct variants, intermittent epidemics of similar magnitudes may become the 'new normal'.
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Affiliation(s)
- Oliver Eales
- School of Public Health, Imperial College London, London, UK.
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK.
| | | | | | - Haowei Wang
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Barbara Bodinier
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - David Tang
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - David Haw
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Jakob Jonnerby
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | | | - Deborah Ashby
- School of Public Health, Imperial College London, London, UK
| | - Wendy Barclay
- Department of Infectious Disease, Imperial College London, London, UK
| | - Graham Taylor
- Department of Infectious Disease, Imperial College London, London, UK
| | - Graham Cooke
- Department of Infectious Disease, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
| | - Helen Ward
- School of Public Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
| | - Ara Darzi
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Steven Riley
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Paul Elliott
- School of Public Health, Imperial College London, London, UK.
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK.
- Imperial College Healthcare NHS Trust, London, UK.
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK.
- Health Data Research (HDR) UK, Imperial College London, London, UK.
- UK Dementia Research Institute Centre at Imperial, Imperial College London, London, UK.
| | - Christl A Donnelly
- School of Public Health, Imperial College London, London, UK.
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK.
- Department of Statistics, University of Oxford, Oxford, UK.
| | - Marc Chadeau-Hyam
- School of Public Health, Imperial College London, London, UK.
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.
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10
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Eales O, Page AJ, de Oliveira Martins L, Wang H, Bodinier B, Haw D, Jonnerby J, Atchison C, Ashby D, Barclay W, Taylor G, Cooke G, Ward H, Darzi A, Riley S, Chadeau-Hyam M, Donnelly CA, Elliott P. SARS-CoV-2 lineage dynamics in England from September to November 2021: high diversity of Delta sub-lineages and increased transmissibility of AY.4.2. BMC Infect Dis 2022; 22:647. [PMID: 35896970 PMCID: PMC9326417 DOI: 10.1186/s12879-022-07628-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 07/04/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Since the emergence of SARS-CoV-2, evolutionary pressure has driven large increases in the transmissibility of the virus. However, with increasing levels of immunity through vaccination and natural infection the evolutionary pressure will switch towards immune escape. Genomic surveillance in regions of high immunity is crucial in detecting emerging variants that can more successfully navigate the immune landscape. METHODS We present phylogenetic relationships and lineage dynamics within England (a country with high levels of immunity), as inferred from a random community sample of individuals who provided a self-administered throat and nose swab for rt-PCR testing as part of the REal-time Assessment of Community Transmission-1 (REACT-1) study. During round 14 (9 September-27 September 2021) and 15 (19 October-5 November 2021) lineages were determined for 1322 positive individuals, with 27.1% of those which reported their symptom status reporting no symptoms in the previous month. RESULTS We identified 44 unique lineages, all of which were Delta or Delta sub-lineages, and found a reduction in their mutation rate over the study period. The proportion of the Delta sub-lineage AY.4.2 was increasing, with a reproduction number 15% (95% CI 8-23%) greater than the most prevalent lineage, AY.4. Further, AY.4.2 was less associated with the most predictive COVID-19 symptoms (p = 0.029) and had a reduced mutation rate (p = 0.050). Both AY.4.2 and AY.4 were found to be geographically clustered in September but this was no longer the case by late October/early November, with only the lineage AY.6 exhibiting clustering towards the South of England. CONCLUSIONS As SARS-CoV-2 moves towards endemicity and new variants emerge, genomic data obtained from random community samples can augment routine surveillance data without the potential biases introduced due to higher sampling rates of symptomatic individuals.
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Affiliation(s)
- Oliver Eales
- School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | | | | | - Haowei Wang
- School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Barbara Bodinier
- School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - David Haw
- School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Jakob Jonnerby
- School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Christina Atchison
- School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
| | - Deborah Ashby
- School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
| | - Wendy Barclay
- Department of Infectious Disease, Imperial College London, London, UK
| | - Graham Taylor
- Department of Infectious Disease, Imperial College London, London, UK
| | - Graham Cooke
- Department of Infectious Disease, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
| | - Helen Ward
- School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
| | - Ara Darzi
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Steven Riley
- School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Marc Chadeau-Hyam
- School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Christl A Donnelly
- School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK.
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK.
- Department of Statistics, University of Oxford, Oxford, UK.
| | - Paul Elliott
- School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK.
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK.
- Imperial College Healthcare NHS Trust, London, UK.
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK.
- Health Data Research (HDR) UK, Imperial College London, London, UK.
- UK Dementia Research Institute Centre at Imperial, Imperial College London, London, UK.
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11
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Elliott P, Eales O, Steyn N, Tang D, Bodinier B, Wang H, Elliott J, Whitaker M, Atchison C, Diggle PJ, Page AJ, Trotter AJ, Ashby D, Barclay W, Taylor G, Ward H, Darzi A, Cooke GS, Donnelly CA, Chadeau-Hyam M. Twin peaks: The Omicron SARS-CoV-2 BA.1 and BA.2 epidemics in England. Science 2022; 376:eabq4411. [PMID: 35608440 PMCID: PMC9161371 DOI: 10.1126/science.abq4411] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 05/20/2022] [Indexed: 12/11/2022]
Abstract
Rapid transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has led to record-breaking incidence rates around the world. The Real-time Assessment of Community Transmission-1 (REACT-1) study has tracked SARS-CoV-2 infection in England using reverse transcription polymerase chain reaction (RT-PCR) results from self-administered throat and nose swabs from randomly selected participants aged 5 years and older approximately monthly from May 2020 to March 2022. Weighted prevalence in March 2022 was the highest recorded in REACT-1 at 6.37% (N = 109,181), with the Omicron BA.2 variant largely replacing the BA.1 variant. Prevalence was increasing overall, with the greatest increase in those aged 65 to 74 years and 75 years and older. This was associated with increased hospitalizations and deaths, but at much lower levels than in previous waves against a backdrop of high levels of vaccination.
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Affiliation(s)
- Paul Elliott
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- Health Data Research (HDR) UK, Imperial College London, London, UK
- UK Dementia Research Institute, Imperial College London, London, UK
| | - Oliver Eales
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Nicholas Steyn
- Department of Statistics, University of Oxford, Oxford, UK
- Department of Mathematics, Imperial College London, London, UK
| | - David Tang
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Barbara Bodinier
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Haowei Wang
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Joshua Elliott
- Imperial College Healthcare NHS Trust, London, UK
- Department of Infectious Disease, Imperial College London, London, UK
| | - Matthew Whitaker
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Christina Atchison
- School of Public Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
| | - Peter J. Diggle
- CHICAS, Lancaster Medical School, Lancaster University, UK and Health Data Research, Lancaster, UK
| | | | | | - Deborah Ashby
- School of Public Health, Imperial College London, London, UK
| | - Wendy Barclay
- Department of Infectious Disease, Imperial College London, London, UK
| | - Graham Taylor
- Department of Infectious Disease, Imperial College London, London, UK
| | - Helen Ward
- School of Public Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Ara Darzi
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Graham S. Cooke
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- Department of Infectious Disease, Imperial College London, London, UK
| | - Christl A. Donnelly
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Marc Chadeau-Hyam
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
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12
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Chadeau-Hyam M, Eales O, Bodinier B, Wang H, Haw D, Whitaker M, Elliott J, Walters CE, Jonnerby J, Atchison C, Diggle PJ, Page AJ, Ashby D, Barclay W, Taylor G, Cooke G, Ward H, Darzi A, Donnelly CA, Elliott P. Breakthrough SARS-CoV-2 infections in double and triple vaccinated adults and single dose vaccine effectiveness among children in Autumn 2021 in England: REACT-1 study. EClinicalMedicine 2022; 48:101419. [PMID: 35572721 PMCID: PMC9076030 DOI: 10.1016/j.eclinm.2022.101419] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/15/2022] [Accepted: 04/08/2022] [Indexed: 11/24/2022] Open
Abstract
Background Prevalence of SARS-CoV-2 infection with Delta variant was increasing in England in late summer 2021 among children aged 5 to 17 years, and adults who had received two vaccine doses. In September 2021, a third (booster) dose was offered to vaccinated adults aged 50 years and over, vulnerable adults and healthcare/care-home workers, and a single vaccine dose already offered to 16 and 17 year-olds was extended to children aged 12 to 15 years. Methods SARS-CoV-2 community prevalence in England was available from self-administered throat and nose swabs using reverse transcriptase polymerase chain reaction (RT-PCR) in round 13 (24 June to 12 July 2021, N = 98,233), round 14 (9 to 27 September 2021, N = 100,527) and round 15 (19 October to 5 November 2021, N = 100,112) from the REACT-1 study randomised community surveys. Linking to National Health Service (NHS) vaccination data for consenting participants, we estimated vaccine effectiveness in children aged 12 to 17 years and compared swab-positivity rates in adults who received a third dose with those who received two doses. Findings Weighted SARS-CoV-2 prevalence was 1.57% (1.48%, 1.66%) in round 15 compared with 0.83% (0.76%, 0.89%) in round 14, and the previously observed link between infections and hospitalisations and deaths had weakened. Vaccine effectiveness against infection in children aged 12 to 17 years was estimated (round 15) at 64.0% (50.9%, 70.6%) and 67.7% (53.8%, 77.5%) for symptomatic infections. Adults who received a third vaccine dose were less likely to test positive compared to those who received two doses, with adjusted OR of 0.36 (0.25, 0.53). Interpretation Vaccination of children aged 12 to 17 years and third (booster) doses in adults were effective at reducing infection risk. High rates of vaccination, including booster doses, are a key part of the strategy to reduce infection rates in the community. Funding Department of Health and Social Care, England.
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Affiliation(s)
- Marc Chadeau-Hyam
- School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, UK
| | - Oliver Eales
- School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
- MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, UK
| | - Barbara Bodinier
- School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, UK
| | - Haowei Wang
- School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
- MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, UK
| | - David Haw
- School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
- MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, UK
| | - Matthew Whitaker
- School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, UK
| | - Joshua Elliott
- Department of Infectious Disease, Imperial College London, UK
- Imperial College Healthcare NHS Trust, UK
| | - Caroline E. Walters
- School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
- MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, UK
| | - Jakob Jonnerby
- School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
- National Heart and Lung Institute, Imperial College Healthcare NHS Trust, UK
| | - Christina Atchison
- School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
- Imperial College Healthcare NHS Trust, UK
| | - Peter J. Diggle
- CHICAS, Lancaster Medical School, UK and Health Data Research, Lancaster University, UK
| | | | - Deborah Ashby
- School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
| | - Wendy Barclay
- Department of Infectious Disease, Imperial College London, UK
| | - Graham Taylor
- Department of Infectious Disease, Imperial College London, UK
| | - Graham Cooke
- Department of Infectious Disease, Imperial College London, UK
- Imperial College Healthcare NHS Trust, UK
- National Institute for Health Research Imperial Biomedical Research Centre, UK
| | - Helen Ward
- School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
- MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, UK
- Imperial College Healthcare NHS Trust, UK
- National Institute for Health Research Imperial Biomedical Research Centre, UK
| | - Ara Darzi
- Imperial College Healthcare NHS Trust, UK
- National Institute for Health Research Imperial Biomedical Research Centre, UK
- Institute of Global Health Innovation, Imperial College London, UK
| | - Christl A. Donnelly
- School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
- MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, UK
- Department of Statistics, University of Oxford, UK
| | - Paul Elliott
- School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, UK
- Imperial College Healthcare NHS Trust, UK
- National Institute for Health Research Imperial Biomedical Research Centre, UK
- Health Data Research (HDR) UK, Imperial College London, UK
- UK Dementia Research Institute, Imperial College London, UK
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13
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Chadeau-Hyam M, Wang H, Eales O, Haw D, Bodinier B, Whitaker M, Walters CE, Ainslie KEC, Atchison C, Fronterre C, Diggle PJ, Page AJ, Trotter AJ, Ashby D, Barclay W, Taylor G, Cooke G, Ward H, Darzi A, Riley S, Donnelly CA, Elliott P. SARS-CoV-2 infection and vaccine effectiveness in England (REACT-1): a series of cross-sectional random community surveys. Lancet Respir Med 2022; 10:355-366. [PMID: 35085490 PMCID: PMC8786320 DOI: 10.1016/s2213-2600(21)00542-7] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 11/18/2021] [Accepted: 11/22/2021] [Indexed: 11/19/2022]
Abstract
BACKGROUND England has experienced a third wave of the COVID-19 epidemic since the end of May, 2021, coinciding with the rapid spread of the delta (B.1.617.2) variant, despite high levels of vaccination among adults. Vaccination rates (single dose) in England are lower among children aged 16-17 years and 12-15 years, whose vaccination in England commenced in August and September, 2021, respectively. We aimed to analyse the underlying dynamics driving patterns in SARS-CoV-2 prevalence during September, 2021, in England. METHODS The REal-time Assessment of Community Transmission-1 (REACT-1) study, which commenced data collection in May, 2020, involves a series of random cross-sectional surveys in the general population of England aged 5 years and older. Using RT-PCR swab positivity data from 100 527 participants with valid throat and nose swabs in round 14 of REACT-1 (Sept 9-27, 2021), we estimated community-based prevalence of SARS-CoV-2 and vaccine effectiveness against infection by combining round 14 data with data from round 13 (June 24 to July 12, 2021; n=172 862). FINDINGS During September, 2021, we estimated a mean RT-PCR positivity rate of 0·83% (95% CrI 0·76-0·89), with a reproduction number (R) overall of 1·03 (95% CrI 0·94-1·14). Among the 475 (62·2%) of 764 sequenced positive swabs, all were of the delta variant; 22 (4·63%; 95% CI 3·07-6·91) included the Tyr145His mutation in the spike protein associated with the AY.4 sublineage, and there was one Glu484Lys mutation. Age, region, key worker status, and household size jointly contributed to the risk of swab positivity. The highest weighted prevalence was observed among children aged 5-12 years, at 2·32% (95% CrI 1·96-2·73) and those aged 13-17 years, at 2·55% (2·11-3·08). The SARS-CoV-2 epidemic grew in those aged 5-11 years, with an R of 1·42 (95% CrI 1·18-1·68), but declined in those aged 18-54 years, with an R of 0·81 (0·68-0·97). At ages 18-64 years, the adjusted vaccine effectiveness against infection was 62·8% (95% CI 49·3-72·7) after two doses compared to unvaccinated people, for all vaccines combined, 44·8% (22·5-60·7) for the ChAdOx1 nCov-19 (Oxford-AstraZeneca) vaccine, and 71·3% (56·6-81·0) for the BNT162b2 (Pfizer-BioNTech) vaccine. In individuals aged 18 years and older, the weighted prevalence of swab positivity was 0·35% (95% CrI 0·31-0·40) if the second dose was administered up to 3 months before their swab but 0·55% (0·50-0·61) for those who received their second dose 3-6 months before their swab, compared to 1·76% (1·60-1·95) among unvaccinated individuals. INTERPRETATION In September, 2021, at the start of the autumn school term in England, infections were increasing exponentially in children aged 5-17 years, at a time when vaccination rates were low in this age group. In adults, compared to those who received their second dose less than 3 months ago, the higher prevalence of swab positivity at 3-6 months following two doses of the COVID-19 vaccine suggests an increased risk of breakthrough infections during this period. The vaccination programme needs to reach children as well as unvaccinated and partially vaccinated adults to reduce SARS-CoV-2 transmission and associated disruptions to work and education. FUNDING Department of Health and Social Care, England.
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Affiliation(s)
- Marc Chadeau-Hyam
- School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Haowei Wang
- School of Public Health, Imperial College London, London, UK; MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Oliver Eales
- School of Public Health, Imperial College London, London, UK; MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - David Haw
- School of Public Health, Imperial College London, London, UK; MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Barbara Bodinier
- School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Matthew Whitaker
- School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Caroline E Walters
- School of Public Health, Imperial College London, London, UK; MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Kylie E C Ainslie
- School of Public Health, Imperial College London, London, UK; MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK; Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, Netherlands
| | | | - Claudio Fronterre
- CHICAS, Lancaster Medical School, Lancaster University, UK and Health Data Research, Lancaster, UK
| | - Peter J Diggle
- CHICAS, Lancaster Medical School, Lancaster University, UK and Health Data Research, Lancaster, UK
| | | | | | - Deborah Ashby
- School of Public Health, Imperial College London, London, UK
| | - Wendy Barclay
- Department of Infectious Disease, Imperial College London, London, UK
| | - Graham Taylor
- Department of Infectious Disease, Imperial College London, London, UK
| | - Graham Cooke
- Department of Infectious Disease, Imperial College London, London, UK; Imperial College Healthcare NHS Trust, London, UK; National Institute for Health Research Imperial Biomedical Research Centre, London, UK
| | - Helen Ward
- School of Public Health, Imperial College London, London, UK; Imperial College Healthcare NHS Trust, London, UK; National Institute for Health Research Imperial Biomedical Research Centre, London, UK
| | - Ara Darzi
- Imperial College Healthcare NHS Trust, London, UK; National Institute for Health Research Imperial Biomedical Research Centre, London, UK; Institute of Global Health Innovation, Imperial College London, London, UK
| | - Steven Riley
- School of Public Health, Imperial College London, London, UK; MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Christl A Donnelly
- School of Public Health, Imperial College London, London, UK; MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK; Department of Statistics, University of Oxford, Oxford, UK.
| | - Paul Elliott
- School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK; Imperial College Healthcare NHS Trust, London, UK; National Institute for Health Research Imperial Biomedical Research Centre, London, UK; Health Data Research (HDR) UK, Imperial College London, London, UK; UK Dementia Research Institute, Imperial College London, London, UK.
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14
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Elliott P, Bodinier B, Eales O, Wang H, Haw D, Elliott J, Whitaker M, Jonnerby J, Tang D, Walters CE, Atchison C, Diggle PJ, Page AJ, Trotter AJ, Ashby D, Barclay W, Taylor G, Ward H, Darzi A, Cooke GS, Chadeau-Hyam M, Donnelly CA. Rapid increase in Omicron infections in England during December 2021: REACT-1 study. Science 2022; 375:1406-1411. [PMID: 35133177 PMCID: PMC8939772 DOI: 10.1126/science.abn8347] [Citation(s) in RCA: 72] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 02/02/2022] [Indexed: 11/04/2022]
Abstract
The unprecedented rise in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections during December 2021 was concurrent with rapid spread of the Omicron variant in England and globally. We analyzed the prevalence of SARS-CoV-2 and its dynamics in England from the end of November to mid-December 2021 among almost 100,000 participants in the REACT-1 study. Prevalence was high with rapid growth nationally and particularly in London during December 2021, with an increasing proportion of infections due to Omicron. We observed large decreases in swab positivity among mostly vaccinated older children (12 to 17 years) relative to unvaccinated younger children (5 to 11 years), and in adults who received a third (booster) vaccine dose versus two doses. Our results reinforce the importance of vaccination and booster campaigns, although additional measures have been needed to control the rapid growth of the Omicron variant.
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Affiliation(s)
- Paul Elliott
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- Health Data Research (HDR) UK, Imperial College London, London, UK
- UK Dementia Research Institute, Imperial College London, London, UK
| | - Barbara Bodinier
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Oliver Eales
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Haowei Wang
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - David Haw
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Joshua Elliott
- Imperial College Healthcare NHS Trust, London, UK
- Department of Infectious Disease, Imperial College London, London, UK
| | - Matthew Whitaker
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Jakob Jonnerby
- School of Public Health, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College Healthcare NHS Trust, London, UK
| | - David Tang
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Caroline E. Walters
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Christina Atchison
- School of Public Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
| | - Peter J. Diggle
- Health Data Research (HDR) UK, Imperial College London, London, UK
- CHICAS, Lancaster Medical School, Lancaster University, Lancaster, UK
| | | | | | - Deborah Ashby
- School of Public Health, Imperial College London, London, UK
| | - Wendy Barclay
- Department of Infectious Disease, Imperial College London, London, UK
| | - Graham Taylor
- Department of Infectious Disease, Imperial College London, London, UK
| | - Helen Ward
- School of Public Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Ara Darzi
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Graham S. Cooke
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- Department of Infectious Disease, Imperial College London, London, UK
| | - Marc Chadeau-Hyam
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Christl A. Donnelly
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
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15
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Eales O, Walters CE, Wang H, Haw D, Ainslie KEC, Atchison CJ, Page AJ, Prosolek S, Trotter AJ, Le Viet T, Alikhan NF, Jackson LM, Ludden C, Ashby D, Donnelly CA, Cooke G, Barclay W, Ward H, Darzi A, Elliott P, Riley S. Characterising the persistence of RT-PCR positivity and incidence in a community survey of SARS-CoV-2. Wellcome Open Res 2022. [DOI: 10.12688/wellcomeopenres.17723.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Background: The REal-time Assessment of Community Transmission-1 (REACT-1) study has provided unbiased estimates of swab-positivity in England approximately monthly since May 2020 using RT-PCR testing of self-administered throat and nose swabs. However, estimating infection incidence requires an understanding of the persistence of RT-PCR swab-positivity in the community. Methods: During round 8 of REACT-1 from 6 January to 22 January 2021, we collected up to two additional swabs from 896 initially RT-PCR positive individuals approximately 6 and 9 days after their initial swab. Results: Test sensitivity and duration of positivity were estimated using an exponential decay model, for all participants and for subsets by initial N-gene cycle threshold (Ct) value, symptom status, lineage and age. A P-spline model was used to estimate infection incidence for the entire duration of the REACT-1 study. REACT-1 test sensitivity was estimated at 0.79 (0.77, 0.81) with median duration of positivity at 9.7 (8.9, 10.6) days. We found greater duration of positivity in those exhibiting symptoms, with low N-gene Ct values, or infected with the Alpha variant. Test sensitivity was found to be higher for those who were pre-symptomatic or with low N-gene Ct values. Compared to swab-positivity, our estimates of infection incidence included sharper features with evident transient increases around the time of changes in social distancing measures. Conclusions: These results validate previous efforts to estimate incidence of SARS-CoV-2 from swab-positivity data and provide a reliable means to obtain community infection estimates to inform policy response.
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16
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Kapwata T, Breetzke G, Wright CY, Marcus TS, Eales O. Demographic and socio-economic risk factors associated with self-reported TB. Int J Tuberc Lung Dis 2022; 26:33-37. [PMID: 34969426 DOI: 10.5588/ijtld.21.0247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND: The infectiousness of Mycobacterium tuberculosis is known to be shaped by the human environment, with research showing positive associations with poverty, homelessness and overcrowding, among other factors. In this study, the focus is primarily on environmental health risks for TB, particularly on those associated with sociodemographic and household living conditions in South Africa.METHODS: Data for this study were collected between 2014 and 2019 from a number of sites implementing community-oriented primary care (COPC) in the Gauteng Province of the country. Community health workers (CHWs) used AitaHealthtm, a custom-built mobile information management application, to obtain data on the TB status and environmental conditions of households. Statistical models were used to determine associations between various demographic, socio-economic and environmental risk factors, and TB.RESULTS: Approximately 12,503 TB cases were reported among 7,769 households. Substance use and male-headed households were found to have significant associations in households with at least one individual with TB. Overcrowding, as well as lack of access to piped water and adequate sanitation were also found to be positively associated with a 'TB-household.´CONCLUSION: Improvements in housing and services, particularly the provision of piped water and reticulated flush toilets, are needed to control and prevent TB in South Africa.
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Affiliation(s)
- T Kapwata
- Environment and Health Research Unit, South African Medical Research Council, Johannesburg, South Africa, Environmental Health Department, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa
| | - G Breetzke
- Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria, South Africa
| | - C Y Wright
- Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria, South Africa, Environment and Health Research Unit, South African Medical Research Council, Pretoria, South Africa
| | - T S Marcus
- Department of Family Medicine, University of Pretoria, Pretoria, South Africa
| | - O Eales
- Department of Family Medicine, University of Pretoria, Pretoria, South Africa
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17
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Elliott P, Haw D, Wang H, Eales O, Walters CE, Ainslie KEC, Atchison C, Fronterre C, Diggle PJ, Page AJ, Trotter AJ, Prosolek SJ, Ashby D, Donnelly CA, Barclay W, Taylor G, Cooke G, Ward H, Darzi A, Riley S. Exponential growth, high prevalence of SARS-CoV-2, and vaccine effectiveness associated with the Delta variant. Science 2021; 374:eabl9551. [PMID: 34726481 PMCID: PMC10763627 DOI: 10.1126/science.abl9551] [Citation(s) in RCA: 80] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 10/29/2021] [Indexed: 01/05/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections were rising during early summer 2021 in many countries as a result of the Delta variant. We assessed reverse transcription polymerase chain reaction swab positivity in the Real-time Assessment of Community Transmission–1 (REACT-1) study in England. During June and July 2021, we observed sustained exponential growth with an average doubling time of 25 days, driven by complete replacement of the Alpha variant by Delta and by high prevalence at younger, less-vaccinated ages. Prevalence among unvaccinated people [1.21% (95% credible interval 1.03%, 1.41%)] was three times that among double-vaccinated people [0.40% (95% credible interval 0.34%, 0.48%)]. However, after adjusting for age and other variables, vaccine effectiveness for double-vaccinated people was estimated at between ~50% and ~60% during this period in England. Increased social mixing in the presence of Delta had the potential to generate sustained growth in infections, even at high levels of vaccination.
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Affiliation(s)
- Paul Elliott
- School of Public Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Health Data Research UK London at Imperial College London, London, UK
- UK Dementia Research Institute Centre at Imperial, London, UK
| | - David Haw
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Haowei Wang
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Oliver Eales
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Caroline E. Walters
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
| | - Kylie E. C. Ainslie
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, Netherlands
| | | | - Claudio Fronterre
- CHICAS, Lancaster Medical School, Lancaster University, and Health Data Research UK, Lancaster, UK
| | - Peter J. Diggle
- CHICAS, Lancaster Medical School, Lancaster University, and Health Data Research UK, Lancaster, UK
| | | | | | | | - The COVID-19 Genomics UK (COG-UK) Consortium11‡
- School of Public Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Health Data Research UK London at Imperial College London, London, UK
- UK Dementia Research Institute Centre at Imperial, London, UK
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, Netherlands
- CHICAS, Lancaster Medical School, Lancaster University, and Health Data Research UK, Lancaster, UK
- Quadram Institute, Norwich, UK
- www.cogconsortium.uk
- Department of Statistics, University of Oxford, Oxford, UK
- Department of Infectious Disease, Imperial College London, London, UK
- Institute of Global Health Innovation at Imperial College London, London, UK
- Health Security Initiative, Flagship Pioneering UK Ltd., Bristol, UK
| | - Deborah Ashby
- School of Public Health, Imperial College London, London, UK
| | - Christl A. Donnelly
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Wendy Barclay
- Department of Infectious Disease, Imperial College London, London, UK
| | - Graham Taylor
- Department of Infectious Disease, Imperial College London, London, UK
| | - Graham Cooke
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- Department of Infectious Disease, Imperial College London, London, UK
| | - Helen Ward
- School of Public Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
| | - Ara Darzi
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- Institute of Global Health Innovation at Imperial College London, London, UK
- Health Security Initiative, Flagship Pioneering UK Ltd., Bristol, UK
| | - Steven Riley
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, UK
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Elliott J, Whitaker M, Bodinier B, Eales O, Riley S, Ward H, Cooke G, Darzi A, Chadeau-Hyam M, Elliott P. Predictive symptoms for COVID-19 in the community: REACT-1 study of over 1 million people. PLoS Med 2021; 18:e1003777. [PMID: 34582457 PMCID: PMC8478234 DOI: 10.1371/journal.pmed.1003777] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 08/20/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Rapid detection, isolation, and contact tracing of community COVID-19 cases are essential measures to limit the community spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to identify a parsimonious set of symptoms that jointly predict COVID-19 and investigated whether predictive symptoms differ between the B.1.1.7 (Alpha) lineage (predominating as of April 2021 in the US, UK, and elsewhere) and wild type. METHODS AND FINDINGS We obtained throat and nose swabs with valid SARS-CoV-2 PCR test results from 1,147,370 volunteers aged 5 years and above (6,450 positive cases) in the REal-time Assessment of Community Transmission-1 (REACT-1) study. This study involved repeated community-based random surveys of prevalence in England (study rounds 2 to 8, June 2020 to January 2021, response rates 22%-27%). Participants were asked about symptoms occurring in the week prior to testing. Viral genome sequencing was carried out for PCR-positive samples with N-gene cycle threshold value < 34 (N = 1,079) in round 8 (January 2021). In univariate analysis, all 26 surveyed symptoms were associated with PCR positivity compared with non-symptomatic people. Stability selection (1,000 penalized logistic regression models with 50% subsampling) among people reporting at least 1 symptom identified 7 symptoms as jointly and positively predictive of PCR positivity in rounds 2-7 (June to December 2020): loss or change of sense of smell, loss or change of sense of taste, fever, new persistent cough, chills, appetite loss, and muscle aches. The resulting model (rounds 2-7) predicted PCR positivity in round 8 with area under the curve (AUC) of 0.77. The same 7 symptoms were selected as jointly predictive of B.1.1.7 infection in round 8, although when comparing B.1.1.7 with wild type, new persistent cough and sore throat were more predictive of B.1.1.7 infection while loss or change of sense of smell was more predictive of the wild type. The main limitations of our study are (i) potential participation bias despite random sampling of named individuals from the National Health Service register and weighting designed to achieve a representative sample of the population of England and (ii) the necessary reliance on self-reported symptoms, which may be prone to recall bias and may therefore lead to biased estimates of symptom prevalence in England. CONCLUSIONS Where testing capacity is limited, it is important to use tests in the most efficient way possible. We identified a set of 7 symptoms that, when considered together, maximize detection of COVID-19 in the community, including infection with the B.1.1.7 lineage.
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Affiliation(s)
- Joshua Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, Imperial College London, London, United Kingdom
- Royal Surrey NHS Foundation Trust, Guildford, United Kingdom
| | - Matthew Whitaker
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, Imperial College London, London, United Kingdom
| | - Barbara Bodinier
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, Imperial College London, London, United Kingdom
| | - Oliver Eales
- MRC Centre for Global infectious Disease Analysis, Imperial College London, London, United Kingdom
- Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - Steven Riley
- MRC Centre for Global infectious Disease Analysis, Imperial College London, London, United Kingdom
- Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - Helen Ward
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London, United Kingdom
| | - Graham Cooke
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London, United Kingdom
- Department of Infectious Disease, Imperial College London, London, United Kingdom
| | - Ara Darzi
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London, United Kingdom
- Institute of Global Health Innovation, Imperial College London, London, United Kingdom
| | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, Imperial College London, London, United Kingdom
- Health Data Research UK London, Imperial College London, London, United Kingdom
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London, United Kingdom
- Health Data Research UK London, Imperial College London, London, United Kingdom
- UK Dementia Research Institute, Imperial College London, London, United Kingdom
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Riley S, Ainslie KEC, Eales O, Walters CE, Wang H, Atchison C, Fronterre C, Diggle PJ, Ashby D, Donnelly CA, Cooke G, Barclay W, Ward H, Darzi A, Elliott P. Resurgence of SARS-CoV-2: Detection by community viral surveillance. Science 2021; 372:990-995. [PMID: 33893241 PMCID: PMC8158959 DOI: 10.1126/science.abf0874] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 04/20/2021] [Indexed: 12/14/2022]
Abstract
Surveillance of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has mainly relied on case reporting, which is biased by health service performance, test availability, and test-seeking behaviors. We report a community-wide national representative surveillance program in England based on self-administered swab results from ~594,000 individuals tested for SARS-CoV-2, regardless of symptoms, between May and the beginning of September 2020. The epidemic declined between May and July 2020 but then increased gradually from mid-August, accelerating into early September 2020 at the start of the second wave. When compared with cases detected through routine surveillance, we report here a longer period of decline and a younger age distribution. Representative community sampling for SARS-CoV-2 can substantially improve situational awareness and feed into the public health response even at low prevalence.
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Affiliation(s)
- Steven Riley
- School of Public Health, Imperial College London, London, UK.
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Kylie E C Ainslie
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Oliver Eales
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Caroline E Walters
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Haowei Wang
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | | | - Claudio Fronterre
- Centre for Health Informatics, Computing, and Statistics (CHICAS), Lancaster Medical School, Lancaster University, Lancaster, UK
- Health Data Research UK, London, UK
| | - Peter J Diggle
- Centre for Health Informatics, Computing, and Statistics (CHICAS), Lancaster Medical School, Lancaster University, Lancaster, UK
- Health Data Research UK, London, UK
| | - Deborah Ashby
- School of Public Health, Imperial College London, London, UK
| | - Christl A Donnelly
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Graham Cooke
- Department of Infectious Disease, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
| | - Wendy Barclay
- Department of Infectious Disease, Imperial College London, London, UK
| | - Helen Ward
- School of Public Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
| | - Ara Darzi
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Paul Elliott
- School of Public Health, Imperial College London, London, UK.
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
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20
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Jeffrey B, Walters CE, Ainslie KEC, Eales O, Ciavarella C, Bhatia S, Hayes S, Baguelin M, Boonyasiri A, Brazeau NF, Cuomo-Dannenburg G, FitzJohn RG, Gaythorpe K, Green W, Imai N, Mellan TA, Mishra S, Nouvellet P, Unwin HJT, Verity R, Vollmer M, Whittaker C, Ferguson NM, Donnelly CA, Riley S. Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK. Wellcome Open Res 2020; 5:170. [PMID: 32954015 PMCID: PMC7479499 DOI: 10.12688/wellcomeopenres.15997.1] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/29/2020] [Indexed: 01/08/2023] Open
Abstract
Background: Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which have resulted in reduced mobility across different regions. Crowd level data on mobile phone usage can be used as a proxy for actual population mobility patterns and provide a way of quantifying the impact of social distancing measures on changes in mobility. Methods: Here, we use two mobile phone-based datasets (anonymised and aggregated crowd level data from O2 and from the Facebook app on mobile phones) to assess changes in average mobility, both overall and broken down into high and low population density areas, and changes in the distribution of journey lengths. Results: We show that there was a substantial overall reduction in mobility, with the most rapid decline on the 24th March 2020, the day after the Prime Minister's announcement of an enforced lockdown. The reduction in mobility was highly synchronized across the UK. Although mobility has remained low since 26th March 2020, we detect a gradual increase since that time. We also show that the two different datasets produce similar trends, albeit with some location-specific differences. We see slightly larger reductions in average mobility in high-density areas than in low-density areas, with greater variation in mobility in the high-density areas: some high-density areas eliminated almost all mobility. Conclusions: These analyses form a baseline from which to observe changes in behaviour in the UK as social distancing is eased and inform policy towards the future control of SARS-CoV-2 in the UK.
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Affiliation(s)
- Benjamin Jeffrey
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Caroline E. Walters
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Kylie E. C. Ainslie
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Oliver Eales
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Constanze Ciavarella
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Sarah Hayes
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Adhiratha Boonyasiri
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | - Nicholas F. Brazeau
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Richard G. FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Katy Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - William Green
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Thomas A. Mellan
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Swapnil Mishra
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
- School of Life Sciences, University of Sussex, Brighton, UK
| | - H. Juliette T. Unwin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Robert Verity
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Michaela Vollmer
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Neil M. Ferguson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Christl A. Donnelly
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
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