Publications
251 results found
Ward H, Atchison C, Whitaker M, et al., 2023, Design and implementation of a national program to monitor the prevalence of SARS-CoV-2 IgG antibodies in England using self-testing: the REACT-2 study, American Journal of Public Health, Pages: e1-e9, ISSN: 0090-0036
Data System. The UK Department of Health and Social Care funded the REal-time Assessment of Community Transmission-2 (REACT-2) study to estimate community prevalence of SARS-CoV-2 IgG (immunoglobulin G) antibodies in England. Data Collection/Processing. We obtained random cross-sectional samples of adults from the National Health Service (NHS) patient list (near-universal coverage). We sent participants a lateral flow immunoassay (LFIA) self-test, and they reported the result online. Overall, 905 991 tests were performed (28.9% response) over 6 rounds of data collection (June 2020-May 2021). Data Analysis/Dissemination. We produced weighted estimates of LFIA test positivity (validated against neutralizing antibodies), adjusted for test performance, at local, regional, and national levels and by age, sex, and ethnic group and area-level deprivation score. In each round, fieldwork occurred over 2 weeks, with results reported to policymakers the following week. We disseminated results as preprints and peer-reviewed journal publications. Public Health Implications. REACT-2 estimated the scale and variation in antibody prevalence over time. Community self-testing and -reporting produced rapid insights into the changing course of the pandemic and the impact of vaccine rollout, with implications for future surveillance. (Am J Public Health. Published online ahead of print September 21, 2023:e1-e9. https://doi.org/10.2105/AJPH.2023.307381).
Metcalf CJE, Klein SL, Read JM, et al., 2023, Survival at older ages: Are greater influenza antibody titers protective?, MEDICAL HYPOTHESES, Vol: 178, ISSN: 0306-9877
Whitaker M, Davies B, Atchison C, et al., 2023, SARS-CoV-2 rapid antibody test results and subsequent risk of hospitalisation and death in 361,801 people, Nature Communications, Vol: 14, ISSN: 2041-1723
The value of SARS-CoV-2 lateral flow immunoassay (LFIA) tests for estimating individual disease risk is unclear. The REACT-2 study in England, UK, obtained self-administered SARS-CoV-2 LFIA test results from 361,801 adults in January-May 2021. Here, we link to routine data on subsequent hospitalisation (to September 2021), and death (to December 2021). Among those who had received one or more vaccines, a negative LFIA is associated with increased risk of hospitalisation with COVID-19 (HR: 2.73 [95% confidence interval: 1.15,6.48]), death (all-cause) (HR: 1.59, 95% CI:1.07, 2.37), and death with COVID-19 as underlying cause (20.6 [1.83,232]). For people designated at high risk from COVID-19, who had received one or more vaccines, there is an additional risk of all-cause mortality of 1.9 per 1000 for those testing antibody negative compared to positive. However, the LFIA does not provide substantial predictive information over and above that which is available from detailed sociodemographic and health-related variables. Nonetheless, this simple test provides a marker which could be a valuable addition to understanding population and individual-level risk.
Atchison C, Whitaker M, Donnelly C, et al., 2023, Characteristics and predictors of persistent symptoms post COVID-19 in children and young people: a large community cross-sectional study in England, Archives of Disease in Childhood, Vol: 108, ISSN: 0003-9888
Objective: To estimate the prevalence of, and associated risk factors for, persistent symptoms post-COVID-19 among children aged 5–17 years in England.Design: Serial cross-sectional study.Setting: Rounds 10–19 (March 2021 to March 2022) of the REal-time Assessment of Community Transmission-1 study (monthly cross-sectional surveys of random samples of the population in England).Study population: Children aged 5–17 years in the community.Predictors: Age, sex, ethnicity, presence of a pre-existing health condition, index of multiple deprivation, COVID-19 vaccination status and dominant UK circulating SARS-CoV-2 variant at time of symptom onset.Main outcome measures: Prevalence of persistent symptoms, reported as those lasting ≥3 months post-COVID-19.Results: Overall, 4.4% (95% CI 3.7 to 5.1) of 3173 5–11 year-olds and 13.3% (95% CI 12.5 to 14.1) of 6886 12–17 year-olds with prior symptomatic infection reported at least one symptom lasting ≥3 months post-COVID-19, of whom 13.5% (95% CI 8.4 to 20.9) and 10.9% (95% CI 9.0 to 13.2), respectively, reported their ability to carry out day-to-day activities was reduced ‘a lot’ due to their symptoms. The most common symptoms among participants with persistent symptoms were persistent coughing (27.4%) and headaches (25.4%) in children aged 5–11 years and loss or change of sense of smell (52.2%) and taste (40.7%) in participants aged 12–17 years. Higher age and having a pre-existing health condition were associated with higher odds of reporting persistent symptoms.Conclusions: One in 23 5–11 year-olds and one in eight 12–17 year-olds post-COVID-19 report persistent symptoms lasting ≥3 months, of which one in nine report a large impact on performing day-to-day activities.
Eales O, Haw D, Wang H, et al., 2023, Dynamics of SARS-CoV-2 infection hospitalisation and infection fatality ratios over 23 months in England, PLoS Biology, Vol: 21, Pages: 1-21, ISSN: 1544-9173
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
Elliott P, Whitaker M, Tang D, et al., 2023, Design and implementation of a national SARS-CoV-2 monitoring programme in England: REACT-1 Study, American Journal of Public Health, ISSN: 0090-0036
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 SARS-CoV-2 infection by time, person and place.Data Collection/Processing. The data were obtained by writing to named individuals aged 5 years and above in random cross-sections of the population of England, using the National Health Service (NHS) list of patients registered with a general practitioner (>99% coverage) as sampling frame. Data were collected 2-3 weekly approximately every month across 19distinct rounds of data collection from May 1, 2020 to March 31, 2022.Data Analysis/Dissemination. The data and study materials are widely disseminated via the study website, preprints, publications in peer-reviewed journals and the media. Data tabulations suitably anonymised to protect participant confidentiality are available on request to the study’s Data Access Committee.Implications. The study provided inter alia real-time data on SARS-CoV-2 prevalence over time, by area, and by socio-demographic variables; estimates of vaccine effectiveness; symptom profiles and detected emergence of new variants based on viral genome sequencing.
Elliott P, Ward H, Riley S, 2023, Population monitoring of SARS-CoV-2 infections via random sampling during the COVID-19 pandemic., American Journal of Public Health, Pages: e1-e3, ISSN: 0090-0036
Atchison C, Moshe M, Brown J, et al., 2023, Validity of self-testing at home with rapid SARS-CoV-2 antibody detection by lateral flow immunoassay, Clinical Infectious Diseases, Vol: 76, Pages: 658-666, ISSN: 1058-4838
Background: We explore severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody lateral flow immunoassay (LFIA) performance under field conditions compared to laboratory-based ELISA and live virus neutralisation. Methods: In July 2021, 3758 participants performed, at home, a self-administered LFIA on finger-prick blood, reported and submitted a photograph of the result, and provided a self-collected capillary blood sample for assessment of IgG antibodies using the Roche Elecsys® Anti-SARS-CoV-2 assay. We compared the self-reported LFIA result to the quantitative Roche assay and checked the reading of the LFIA result with an automated image analysis (ALFA). In a subsample of 250 participants, we compared the results to live virus neutralisation. Results: Almost all participants (3593/3758, 95.6%) had been vaccinated or reported prior infection. Overall, 2777/3758 (73.9%) were positive on self-reported LFIA, 2811/3457 (81.3%) positive by LFIA when ALFA-reported, and 3622/3758 (96.4%) positive on Roche (using the manufacturer reference standard threshold for positivity of 0.8 U ml−1). Live virus neutralisation was detected in 169 of 250 randomly selected samples (67.6%); 133/169 were positive with self-reported LFIA (sensitivity 78.7%; 95% CI 71.8, 84.6), 142/155 (91.6%; 86.1, 95.5) with ALFA, and 169 (100%; 97.8, 100.0) with Roche. There were 81 samples with no detectable virus neutralisation; 47/81 were negative with self-reported LFIA (specificity 58.0%; 95% CI 46.5, 68.9), 34/75 (45.3%; 33.8, 57.3) with ALFA, and 0/81 (0%; 0.0, 4.5) with Roche. Conclusions: Self-administered LFIA is less sensitive than a quantitative antibody test, but the positivity in LFIA correlates better than the quantitative ELISA with virus neutralisation.
Eales O, Page AJ, Tang SN, et al., 2023, 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., Microbial Genomics, Vol: 9, Pages: 1-14, ISSN: 2057-5858
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.
Riley P, Ben-Nun M, Turtle J, et al., 2023, COVID-19: On the Disparity in Outcomes Between Military and Civilian Populations, MILITARY MEDICINE, Vol: 188, Pages: E311-E315, ISSN: 0026-4075
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Bhatia S, Parag KV, Wardle J, et al., 2023, Retrospective evaluation of real-time estimates of global COVID-19 transmission trends and mortality forecasts., PLoS One, Vol: 18
Since 8th March 2020 up to the time of writing, we have been producing near real-time weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for all countries with evidence of sustained transmission, shared online. We also developed a novel heuristic to combine weekly estimates of transmissibility to produce forecasts over a 4-week horizon. Here we present a retrospective evaluation of the forecasts produced between 8th March to 29th November 2020 for 81 countries. We evaluated the robustness of the forecasts produced in real-time using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3% and 45.6% of the observations lying in the 50% Credible Interval in 1-week and 4-week ahead forecasts respectively. The retrospective evaluation of our models shows that simple transmission models calibrated using routine disease surveillance data can reliably capture the epidemic trajectory in multiple countries. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax public health measures.
Yang B, Garcia-Carreras B, Lessler J, et al., 2022, Long term intrinsic cycling in human life course antibody responses to influenza A(H3N2): an observational and modeling study, ELIFE, Vol: 11, ISSN: 2050-084X
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Kwok KO, Chan EYY, Riley S, et al., 2022, Carriage prevalence of antimicrobial resistance in Hong Kong: a longitudinal study (abridged secondary publication), HONG KONG MEDICAL JOURNAL, Vol: 28, Pages: 25-28, ISSN: 1024-2708
Riley S, 2022, Steven Riley's discussion contribution to papers in Session 1 of the Royal Statistical Society's Special Topic Meeting on COVID-19 transmission: 9 June 2021, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, Vol: 185, Pages: S53-S54, ISSN: 0964-1998
Riley S, 2022, Steven Riley's discussion contribution to papers in Session 3 of the Royal Statistical Society's Special Topic Meeting on COVID-19 transmission: 11 June 2021, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, Vol: 185, Pages: S148-S149, ISSN: 0964-1998
Eales O, Haw D, Wang H, et al., 2022, Quantifying changes in the IFR and IHR over 23 months of the SARS-CoV-2 pandemic in England
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>The relationship between prevalence of infection and severe outcomes such as hospitalisation and death changed over the course of the COVID-19 pandemic. The REal-time Assessment of Community Transmission-1 (REACT-1) study estimated swab positivity in England approximately monthly from May 2020 to 31 March 2022. This period covers widespread circulation of the original strain, the emergence of the Alpha, Delta and Omicron variants and the rollout of England’s mass vaccination campaign.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Here, we explore this changing relationship between prevalence of swab positivity and the infection fatality rate (IFR) and infection hospitalisation rate (IHR) over 23 months of the pandemic 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 two sub-groups: those aged 65 and over and those aged 64 and under.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>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. 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 of the virus 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.</jats:
Eales O, Ainslie KEC, Walters CE, et al., 2022, Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number, Epidemics: the journal of infectious disease dynamics, Vol: 40, ISSN: 1755-4365
The time-varying reproduction number () 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 from case data. However, these are not easily adapted to point prevalence data nor can they infer 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 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 over shorter periods tracking a temporary increase above one during late-May 2020, a gradual increase in over the summer of 2020 as restrictions were eased, and a reduction in 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.
Tildesley MJ, Vassall A, Riley S, et al., 2022, Optimal health and economic impact of non-pharmaceutical intervention measures prior and post vaccination in England: a mathematical modelling study, ROYAL SOCIETY OPEN SCIENCE, Vol: 9, ISSN: 2054-5703
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Ben-Nun M, Riley P, Turtle J, et al., 2022, Consistent pattern of epidemic slowing across many geographies led to longer, flatter initial waves of the COVID-19 pandemic, PLOS COMPUTATIONAL BIOLOGY, Vol: 18, ISSN: 1553-734X
Eales O, Martins LDO, Page AJ, et al., 2022, Dynamics of competing SARS-CoV-2 variants during the Omicron epidemic in England, Nature Communications, Vol: 13, ISSN: 2041-1723
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’.
Eales O, Wang H, Haw D, et al., 2022, Trends in SARS-CoV-2 infection prevalence during England’s roadmap out of lockdown, January to July 2021
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Aim</jats:title><jats:p>We characterise how the lockdown and subsequent easing of restrictions affected trends in SARS-CoV-2 infection prevalence.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>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 (<jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub>) 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 <jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub> of each relaxation of restrictions.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>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 <jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub> incre
Cann A, Clarke C, Brown J, et al., 2022, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody lateral flow assay for antibody prevalence studies following vaccination: a diagnostic accuracy study [version 2; peer review: 2 approved], Wellcome Open Research, Vol: 6, ISSN: 2398-502X
Background: Lateral flow immunoassays (LFIAs) are able to achieve affordable, large scale antibody testing and provide rapid results without the support of central laboratories. As part of the development of the REACT programme extensive evaluation of LFIA performance was undertaken with individuals following natural infection. Here we assess the performance of the selected LFIA to detect antibody responses in individuals who have received at least one dose of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine. Methods: This was a prospective diagnostic accuracy study. Sampling was carried out at renal outpatient clinic and healthcare worker testing sites at Imperial College London NHS Trust. Two cohorts of patients were recruited; the first was a cohort of 108 renal transplant patients attending clinic following two doses of SARS-CoV-2 vaccine, the second cohort comprised 40 healthcare workers attending for first SARS-CoV-2 vaccination and subsequent follow up. During the participants visit, finger-prick blood samples were analysed on LFIA device, while paired venous sampling was sent for serological assessment of antibodies to the spike protein (anti-S) antibodies. Anti-S IgG was detected using the Abbott Architect SARS-CoV-2 IgG Quant II CMIA. A total of 186 paired samples were collected. The accuracy of Fortress LFIA in detecting IgG antibodies to SARS-CoV-2 compared to anti-spike protein detection on Abbott Assay Results: The LFIA had an estimated sensitivity of 92.0% (114/124; 95% confidence interval [CI] 85.7% to 96.1%) and specificity of 93.6% (58/62; 95% CI 84.3% to 98.2%) using the Abbott assay as reference standard (using the threshold for positivity of 7.10 BAU/ml) Conclusions: Fortress LFIA performs well in the detection of antibody responses for intended purpose of population level surveillance but does not meet criteria for individual testing.
Ainslie KEC, Riley S, 2022, Is annual vaccination best? A modelling study of influenza vaccination strategies in children, VACCINE, Vol: 40, Pages: 2940-2948, ISSN: 0264-410X
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Cramer EY, Ray EL, Lopez VK, et al., 2022, Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 119, ISSN: 0027-8424
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Whitaker M, Elliott J, Chadeau M, et al., 2022, Persistent COVID-19 symptoms in a community study of 606,434 people in England, Nature Communications, Vol: 13, ISSN: 2041-1723
Long COVID remains a broadly defined syndrome, with estimates of prevalence and duration varying widely. We use data from rounds 3–5 of the REACT-2 study (n=508,707; September 2020 – February 2021), a representative community survey of adults in England, and replication data from round 6 (n=97,717; May 2021) to estimate the prevalence and identify predictors of persistent symptoms lasting 12 weeks or more; and unsupervised learning to cluster individuals by reported symptoms. At 12 weeks in rounds 3–5, 37.7% experienced at least one symptom, falling to 21.6% in round 6. Female sex, increasing age, obesity, smoking, vaping, hospitalisation with COVID-19, deprivation, and being a healthcare worker are associated with higher probability of persistent symptoms in rounds 3–5, and Asian ethnicity with lower probability. Clustering analysis identifies a subset of participants with predominantly respiratory symptoms. Managing the long-term sequelae of COVID-19 will remain a major challenge for affected individuals and their families and for health services.
Eales O, de Oliveira Martins L, Page AJ, et al., 2022, The new normal? Dynamics and scale of the SARS-CoV-2 variant Omicron epidemic in England
<jats:title>Summary</jats:title><jats:p>The SARS-CoV-2 pandemic has been characterised by the regular emergence of genomic variants which have led to substantial changes in the epidemiology of the virus. 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 was first detected in late November 2021 and exhibited a high degree of immune evasion, leading to increased infection rates in many countries. However, estimates of the magnitude of the Omicron wave have relied mainly on routine testing data, which are prone to several biases. Here we infer the dynamics of the Omicron wave in England using PCR testing and genomic sequencing obtained by the REal-time Assessment of Community Transmission-1 (REACT-1) study, a series of cross-sectional surveys testing random samples of the population of England. 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 in England during February-March 2022 as the more transmissible Omicron sub-lineage, BA.2 replaced BA.1 and BA.1.1. Assuming the emergence of further distinct genomic variants, intermittent epidemics of similar magnitude as the Omicron wave may become the ‘new normal’.</jats:p>
Chadeau-Hyam M, Wang H, Eales O, et al., 2022, SARS-CoV-2 infection and vaccine effectiveness in England (REACT-1): a series of cross-sectional random community surveys, The Lancet Respiratory Medicine, Vol: 10, Pages: 355-366, ISSN: 2213-2600
SummaryBackground England has experienced a third wave of the COVID-19 epidemic since the end of May, 2021, coincidingwith the rapid spread of the delta (B.1.617.2) variant, despite high levels of vaccination among adults. Vaccinationrates (single dose) in England are lower among children aged 16–17 years and 12–15 years, whose vaccination inEngland commenced in August and September, 2021, respectively. We aimed to analyse the underlying dynamicsdriving 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 datacollection in May, 2020, involves a series of random cross-sectional surveys in the general population of Englandaged 5 years and older. Using RT-PCR swab positivity data from 100 527 participants with valid throat and noseswabs in round 14 of REACT-1 (Sept 9–27, 2021), we estimated community-based prevalence of SARS-CoV-2 andvaccine 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 areproduction number (R) overall of 1·03 (95% CrI 0·94–1·14). Among the 475 (62·2%) of 764 sequenced positiveswabs, all were of the delta variant; 22 (4·63%; 95% CI 3·07–6·91) included the Tyr145His mutation in the spikeprotein 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 observedamong 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&m
Eales O, Ainslie KEC, Walters CE, et al., 2022, Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number
<jats:title>Abstract</jats:title><jats:p>The time-varying reproduction number (<jats:bold><jats:italic>R</jats:italic></jats:bold><jats:sub><jats:bold><jats:italic>t</jats:italic></jats:bold></jats:sub>) 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 <jats:bold><jats:italic>R</jats:italic></jats:bold><jats:sub><jats:bold><jats:italic>t</jats:italic></jats:bold></jats:sub> from case data. However, these are not easily adapted to point prevalence data nor can they infer <jats:bold><jats:italic>R</jats:italic></jats:bold><jats:sub><jats:bold><jats:italic>t</jats:italic></jats:bold></jats:sub> 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 <jats:bold><jats:italic>R</jats:italic></jats:bold><jats:sub><jats:bold><jats:italic>t</jats:italic></jats:bold></jats:sub> 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 <jats:bold><jats:italic>R</jats:italic></jats:bold><jats:sub><jats
Liang J-B, Yuan H-Y, Li K-K, et al., 2022, Path to normality: Assessing the level of social-distancing measures relaxation against antibody-resistant SARS-CoV-2 variants in a partially- vaccinated population, COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, Vol: 20, Pages: 4052-4059, ISSN: 2001-0370
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Eales O, Page AJ, de Oliveira Martins L, et al., 2021, 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
<jats:title>Abstract</jats:title><jats:p>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. Here 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. From 9 to 27 September 2021 (round 14) and 19 October to 5 November 2021 (round 15), all lineages sequenced within REACT-1 were Delta or a Delta sub-lineage with 44 unique lineages identified. The proportion of the original Delta variant (B.1.617.2) was found to be increasing between September and November 2021, which may reflect an increasing number of sub-lineages which have yet to be identified. The proportion of B.1.617.2 was greatest in London, which was further identified as a region with an increased level of genetic diversity. The Delta sub-lineage AY.4.2 was found to be robustly increasing in proportion, with a reproduction number 15% (8%, 23%) greater than its parent and most prevalent lineage, AY.4. 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. Though no difference in the viral load based on cycle threshold (Ct) values was identified, a lower proportion of those infected with AY.4.2 had symptoms for which testing is usually recommend (loss or change of sense of taste, loss or change of sense of smell, new persistent cough, fever), compared to AY.4 (p = 0.026). The evolutionary rate of SARS-CoV-2, as measured by the mutation rate, was fou
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