key: cord-0795961-ubvbsu7k authors: Elliott, P.; Eales, O.; Bodinier, B.; Tang, D.; Wang, H.; Jonnerby, J.; Haw, D.; Elliott, J.; Whitaker, M.; Walters, C. E.; Atchinson, C.; Diggle, P. J.; Page, A. J.; Trotter, A.; Ashby, D.; Barclay, W.; Taylor, G.; Ward, H.; Darzi, A.; Cooke, G.; Chadeau-Hyam, M.; Donnelly, C. A. title: Post-peak dynamics of a national Omicron SARS-CoV-2 epidemic during January 2022 date: 2022-02-06 journal: nan DOI: 10.1101/2022.02.03.22270365 sha: 6729fe492d07b9f710c5aacbb174128943c6a599 doc_id: 795961 cord_uid: ubvbsu7k Background: Rapid transmission of the SARS-CoV-2 Omicron variant has led to the highest ever recorded case incidence levels in many countries around the world. Methods: The REal-time Assessment of Community Transmission-1 (REACT-1) study has been characterising the transmission of the SARS-CoV-2 virus using RT-PCR test results from self-administered throat and nose swabs from randomly-selected participants in England at ages 5 years and over, approximately monthly since May 2020. Round 17 data were collected between 5 and 20 January 2022 and provide data on the temporal, socio-demographic and geographical spread of the virus, viral loads and viral genome sequence data for positive swabs. Results: From 102,174 valid tests in round 17, weighted prevalence of swab positivity was 4.41% (95% credible interval [CrI], 4.25% to 4.56%), which is over three-fold higher than in December 2021 in England. Of 3,028 sequenced positive swabs, 2,393 lineages were determined and 2,374 (99.2%) were Omicron including 19 (0.80% of all Omicron lineages) cases of BA.2 sub-lineage and one BA.3 (0.04% of all Omicron) detected on 17 January 2022, and only 19 (0.79%) were Delta. The growth of the BA.2 Omicron sub-lineage against BA.1 and its sub-lineage BA.1.1 indicated a daily growth rate advantage of 0.14 (95% CrI, 0.03, 0.28) for BA.2, which corresponds to an additive R advantage of 0.46 (95% CrI, 0.10, 0.92). Within round 17, prevalence was decreasing overall (R=0.95, 95% CrI, 0.93, 0.97) but increasing in children aged 5 to 17 years (R=1.13, 95% CrI, 1.09, 1.18). Those 75 years and older had a swab-positivity prevalence of 2.46% (95% CI, 2.16%, 2.80%) reflecting a high level of infection among a highly vulnerable group. Among the 3,613 swab-positive individuals reporting whether or not they had had previous infection, 2,334 (64.6%) reported previous confirmed COVID-19. Of these, 64.4% reported a positive test from 1 to 30 days before their swab date. Risks of infection were increased among essential/key workers (other than healthcare or care home workers) with mutually adjusted Odds Ratio (OR) of 1.15 (95% CI, 1.05, 1.26), people living in large compared to single-person households (6+ household size OR 1.73; 95% CI, 1.44, 2.08), those living in urban vs rural areas (OR 1.24, 95% CI, 1.13, 1.35) and those living in the most vs least deprived areas (OR 1.34, 95% CI, 1.20, 1.49). Conclusions: We observed unprecedented levels of infection with SARS-CoV-2 in England in January 2022, an almost complete replacement of Delta by Omicron, and evidence for a growth advantage for BA.2 compared to BA.1. The increase in the prevalence of infection with Omicron among children (aged 5 to 17 years) during January 2022 could pose a risk to adults, despite the current trend for prevalence in adults to decline. (Funded by the Department of Health and Social Care in England.) November 2021 saw the identification of the Omicron variant in Botswana and South Africa, its rapid replacement of the Delta (B.1.617.2) variant within South Africa, 1 November the classification by WHO of Omicron as a variant of concern. 2 By 1 December 2021, Omicron had been identified in the UK 3 and the USA 4 as well as other countries including Belgium, Hong Kong and Israel, initially in travel-related cases. By mid-to late December 2021, Omicron had become the dominant variant in the UK 5,6 and had been detected in most European countries and US states. Not only was the increase in Omicron in England 5 and elsewhere 7 extremely rapid, but replacement of Omicron by Delta was over three-times faster than that of Alpha by Delta 5 . In some countries, social distancing policies were brought back into force 8 and vaccination programmes accelerated, 9 while some health care systems struggled to cope with the associated increased health care demands. 10, 11 The REal-time Assessment of Community Transmission-1 (REACT-1) study has been tracking the spread of the SARS-CoV-2 virus among randomly-selected community samples in England, approximately monthly since May 2020, avoiding the biases associated with case incidence data and the delays inherent in hospitalizations and deaths. 12 We use recent rounds of the REACT-1 study to document the transmission dynamics of SARS-CoV-2 in England with a particular focus on Omicron (including the BA.2 and BA.3 sub-lineages) during January 2022. The REACT-1 study involves a series of cross-sectional surveys of random samples of the population of England at ages 5 years and over, 13 conducted approximately monthly over a two-to three-week period since May 2020 (exceptions were December 2020 and August 2021). The present report is for round 17 (5 to 20 January 2022) involving N=102,174 participants with a valid self-administered throat and nose swab test result for SARS-CoV-2 by reverse transcription polymerase chain reaction (RT-PCR) (including 862 samples [36 positives] obtained between 21 and 24 January 2022). A positive test result was recorded if both N gene and E gene targets were detected or if N gene was detected with cycle threshold (Ct) value below 37. We also tested for influenza A and B using multiplex PCR. We compare results for SARS-CoV-2 with those obtained during round 15 (19 October to 5 November 2021, N=100,112, including 93 samples from 6-8 November) 14 and round 16 (23 November to 14 December 2021, N=97,089, including 661 samples from 15-17 December 2021). 5 We used as the sampling frame the general practitioner list of patients in England held by the National Health Service (NHS). Participants completed a brief registration and an online or telephone questionnaire. 15 We obtained information on age, sex, residential postcode, ethnicity, household size, occupation, potential contact with a COVID-19 case, symptoms and other variables. We used the postcode of residence to link to an area-level Index of Multiple Deprivation 16 and urban/rural status. 17 Initially we aimed to obtain approximately equal numbers of participants in each lower-tier local authority (LTLA) in England (N=315), but from round 12 (20 May to 7 June 2021) we switched to obtaining a random sample in proportion to population size at LTLA level. We use random iterative method (rim) weighting 18 to provide prevalence estimates for the population of England as a whole, adjusting for age, sex, deciles of the Index of Multiple Deprivation, LTLA counts, and ethnic group. Up to round 13 (24 June to 12 July 2021), we collected dry swabs sent by courier to the laboratory on a cold chain but from round 14 ( is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint We estimated weighted prevalence and 95% credible intervals overall and by socio-demographic and other variables, comparing round 17 to round 16. We used logistic regression to estimate the odds of testing positive by employment, ethnicity, household size, children in household, urban area, and deprivation, adjusting for age, region and the other variables examined. We fit a Bayesian logistic regression model to the proportion of BA.2 lineage compared to the BA.1 lineage (and its sub-lineage BA.1.1) during round 17 to investigate whether there was a daily growth rate advantage for the odds of BA.2 versus BA.1. The daily percentage growth in the odds of BA.2 infection was estimated from the exponential of the daily growth rate. The estimated additive R advantage was estimated as the daily growth rate advantage multiplied by the Omicron-specific mean generation time. 23 We used an exponential model of growth or decay to examine temporal trends in swab positivity assuming a binomial distribution for the numbers of positives out of the total number of samples per day. To estimate the growth rate and posterior credible intervals, we used day of sampling where reported (otherwise day of first scan of the swab by the Post Office if available) with a bivariate No-U-Turn Sampler and uniform prior distribution for the probability of swab positivity. 24 For the reproduction number R, we assumed a gamma distribution in the generation time with Omicron-specific mean 3.3 days and standard deviation 3.5 days, setting the shape parameter n to 0.89 and rate parameter to 0.27: 23 where r is the exponential growth (or decay) rate. To visualise temporal trends in swab positivity, we used a No-U-Turn Sampler in logit space to fit a Bayesian penalised-spline (P-spline) model 25 to the daily data, split into approximately 5-day sections by regularly spaced knots. Edge effects were minimised by adding further knots beyond the study period. We used fourth-order basis splines (b-splines) over the knots including a second-order random-walk prior distribution on the coefficients of the b-splines to guard against overfitting; the prior penalised against changes in growth rate unless 6 . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted February 6, 2022. ; supported by the data. 26 We also fit P-splines separately to three broad age groups (17 years and under, 18 to 54 years, 55 years and over) with smoothing parameter obtained from the model fit to all the data. We compared Ct values (using a Kruskal-Wallis test) among test-positive swabs (N gene and E gene where Ct>0) as a proxy for viral load, by vaccination status (lagged by a 14-day period from date of vaccination) and symptom status across rounds 15 to 17, where information on vaccination history and dates of vaccination was obtained (with consent) by linking to data from the national COVID-19 vaccination programme. We used a neighbourhood spatial smoothing method to examine geographical variation in SARS-CoV-2 prevalence at the LTLA level. For each of 15 randomly selected participants within an LTLA, we calculated the prevalence of infection among the nearest M people, where M was the median number of study participants within 30 km, and then estimated the smoothed neighbourhood prevalence in that area. We then compared swab positivity prevalence in REACT-1 with daily hospitalisations and (separately) deaths from (external) national data. Estimates of the lag and scaling parameters to fit these different datasets were obtained using REACT-1 data up to round 7 (13 November to 3 December 2020), before the vaccination programme in England began. To account for variant-specific lags, mostly reflecting the dynamics of disease progression, we estimated a second lag parameter from round 13 (24 June to 12 July 2021), when Delta became dominant in England, to round 16 (23 November to 14 December 2021 ). We used 1 the original estimated scaling factor throughout with the original lag estimate for round 1 (01 May to 01 June 2020) to round 12 (20 May to 07 June 2021), and the second lag estimate thereafter. We used R software 27 for the data analyses. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint Figure 2) . The growth rate advantage (0.14, 95% CrI 0.03, 0.28) corresponds to an additive R advantage of 0.46 (95% CrI, 0.10, 0.92). Using a P-spline constrained by parameters estimated for the whole period of REACT-1, we observed a substantial increase in weighted prevalence between round 16 (23 November to 14 December 2021) and round 17 (5 to 20 January 2022) ( Figure 1A ). Within-round 2 3 estimated reproduction number was 0.95 (95% CrI, 0.93, 0.97) with less than 0.01 posterior probability that R>1 (Table 1) . P-splines stratified by age group showed a within-round 17 increasing weighted prevalence in those aged 17 years and under ( Figure 1B) is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint Table 3A , Supplementary Figures 4 and 5) , ranging in round 17 from 6.86% (95% CrI, 5.99%, 7.84%) in North East to 2.92% (95% CrI, 2.58%, 3.30%) in South East. Our results are suggestive of within-household transmission with weighted prevalence increasing with (i) size of household from 3.15% (95% CrI, 2.86%, 3.48%) in single-person households to 7.72% (95% CrI, 6.47%, 9.18%) in households with 6 or more persons and (ii) number of children in the household from 3.57% (95% CrI, 3.41%, 3.74%) in households without children to 5.92% (95% CrI, 5.60%, 6.25%) in households with one or more children (Supplementary Table 3B ). We also found higher weighted prevalence in those having been in contact with a confirmed ( is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint Comparing the prevalence of swab-positivity in REACT-1 to publicly available data on hospitalisations, with appropriate scaling and lag between the two curves, showed a close is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted February 6, 2022. ; https://doi.org/10.1101/2022.02.03.22270365 doi: medRxiv preprint The Omicron epidemic is further advanced in England than in many other countries. During December 2021 Omicron almost completely replaced Delta, with the peak in prevalence coming around six weeks after the first Omicron infection was identified in England. As of mid-January 2022, 0.80% of Omicron infections were BA.2 sublineage which has been designated a variant under investigation. 28 Our data show an increase in the proportion of daily infections from BA.2 compared to BA.1 and its sublineage BA.1.1 with an R advantage of 0.46. We also detected one of the earliest instances of BA.3 in England 29 . During round 17, we observed a drop in prevalence from the peak with a levelling off from mid-January, but still at extremely high levels. However, the dynamics underlying these population-level trends are complex with the prevalence falling in adults but rising in children through January 2022, likely the consequence of the peak occurring during the end-of-year school break, causing a delay to school-based transmission among children. As a result of the rapid rise in Omicron infections, we saw the highest prevalence ever observed in the REACT-1 study, nearly three-fold higher than at the peak of the second wave in January 2021, with a near twelve-fold increase in the oldest age group (75 years and over) since December 2021. An estimated 20 to 40 times greater antibody titre is required for neutralisation of Omicron than for Delta, 30 is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted February 6, 2022. ; https://doi.org/10.1101/2022.02.03.22270365 doi: medRxiv preprint Our study has limitations. In round 17, 12.2% of the invited participants returned swabs producing valid RT-PCR test results, which is similar to what was observed in round 16 (response rate 12.1%). We use weights, calculated for each participant in each round, to adjust for differential response rates in calculating prevalence estimates, but these corrections may not fully eliminate all biases. Our results on reported previous COVID-19 are based on self-reported data. While it is uncertain what proportion of these are reinfections or recent infections picked up due to the sensitivity of PCR testing, among the swab-positive participants reporting previous COVID-19, 64.4% reported a date of most recent positive test within 30 days prior to swabbing, most likely due to residual infection. On the other hand, it is likely that some previous infections were under-reported, especially those occurring in the first wave when routine PCR testing was not readily available. Changes in the way the swab samples were transported and tested may have introduced small changes in results across rounds, although these should not have affected within-round trends. In conclusion, we have documented a substantial and rapid rise in infections from early December 2021 through January 2022 as the Omicron variant took hold and almost completely replaced Delta in England. Although we have subsequently seen falls in prevalence in adults, prevalence remains very high. Among school-aged children there has been a rise in prevalence as they returned to school in January after the end-of-year break. Vaccination (including the booster campaign) remains the mainstay of the defence against SARS-CoV-2 given the high levels of protection against hospitalisations. 28, 33, 34 However, further measures beyond vaccination may be required if the very high rates of Omicron infection persist, despite Omicron appearing to be intrinsically less likely to cause severe disease. 28, 33, 34 12 . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted February 6, 2022. ; Access to REACT-1 individual-level data is restricted to protect participants' anonymity. Summary statistics, descriptive tables, and code from the current REACT-1 study are available at https://github.com/mrc-ide/reactidd (doi 10.5281/zenodo.5574472). REACT-1 study materials are available for each round at https://www.imperial.ac.uk/medicine/research-and-impact/groups/react-study/react-1-stud y-materials/ Sequence read data are available without restriction from the European Nucleotide Archive is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint Tables and Figures Table 1 . Table of growth rates per day (r), reproduction numbers (R) and doubling/halving times (in days) of SARS-CoV-2 swab-positivity from exponential model fits on data from round 17 (05 to 20 January 2022) 4 Table 2 . Multivariable logistic regression for SARS-CoV-2 swab-positivity in round 17. Results are presented as Odds Ratios (95% confidence interval) adjusted for age and sex and additionally, for region and all other variables (mutually adjusted OR). Comparison of an exponential model fit to SARS-CoV-2 swab-positivity data in round 17 (red), and a P-spline model fit to all rounds of REACT-1 (black, shown here only for rounds 14, 15, 16 and 17) (A). Shaded red region shows the 95% posterior credible interval for the exponential models, and the shaded grey region shows 50% (dark grey) and 95% (light grey) posterior credible interval for the P-spline model. Results are presented for each day (X axis) of sampling for round 14, round 15, round 16 and round 17 and the weighted prevalence of swab-positivity is shown (Y axis) on a log scale. Weighted observations (black dots) and 95% confidence intervals (vertical lines) are also shown. Results from similar P-spline models for those aged 17 years and under (red), those aged 18 to 54 years inclusive (blue) and those aged 55 years and over (green) (B). Results are presented for round 16 and 17. . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted February 6, 2022. ; Figure 2 . Distribution of the Ct value for the N gene (A) and E gene (B) in swab-positive samples from round 15 (Delta), round 16 (predominantly Delta) and round 17 (predominantly Omicron). Within each round, distributions are compared (i) for vaccinated vs. unvaccinated participants aged 17 years and under, (ii) those having received a three vs two vaccine doses in adults aged 18 to 54 years, and (iii) for those reporting any symptoms vs those not reporting any symptom in the month prior to swabbing. For each comparison, we report the P-value from a non-parametric (Kruskal-Wallis) test. . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted February 6, 2022. ; Figure 3 . Comparison of COVID-19 daily deaths and hospitalisations with SARS-CoV-2 swab-positivity as measured in REACT-1. Daily swab-positivity for all 17 rounds of the REACT-1 study (black points with 95% confidence intervals, left-hand y-axis) with P-spline estimates for swab-positivity (solid black line, shaded area is 95% confidence interval). (A) Daily deaths in England (red points, right-hand y-axis) and P-spline model estimates for expected daily deaths in England (solid red line, shaded area is 95% confidence interval, right-hand y-axis). Daily deaths have been shifted by 25 days (95% CI, 25, 26) is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted February 6, 2022. ; Network for Genomic Surveillance in South Africa (NGS-SA) SARS-CoV-2 variant of Concern Technical briefing: Update on hospitalisation and vaccine effectiveness for Omicron VOC-21NOV-01 (B.1.1.529) [Internet]. UKHSA; 2021 First confirmed case of Omicron variant detected in the United States Omicron infections in England during December 2021: REACT-1 study SARS-CoV-2 variants of concern and variants under investigation in England: Technical briefing Take all measures to prevent further spread of Omicron -WHO Slowing the spread of the Omicron variant: lockdown in the Netherlands Get boosted now Hospitals Fill Up, but I.C.U.s May Not [Internet]. The New York Times Resurgence of SARS-CoV-2: Detection by community viral surveillance REal-time Assessment of Community Transmission (REACT) of SARS-CoV-2 virus: Study protocol REACT-1 round 15 final report: Increased breakthrough SARS-CoV-2 infections among adults who had received two doses of vaccine, but booster doses and first doses in children are providing important protection REACT 1 study materials Mapping income deprivation at a local authority level: 2019 -Office for National Statistics rural/urban classification Weighting survey results nf: A Nextflow pipeline for running the ARTIC network's fieldbioinformatics tools CoronaHiT: high-throughput sequencing of SARS-CoV-2 genomes Software package for assigning SARS-CoV-2 genome sequences to global lineages Estimation of the test to test distribution as a proxy for generation interval distribution for the Omicron variant in England The no-U-Turn Sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo Exponential growth, high prevalence of SARS-CoV-2, and vaccine effectiveness associated with the Delta variant R: A language and environment for statistical computing SARS-CoV-2 variants of concern and variants under investigation in England: Technical briefing 34 Early assessment of the clinical severity of the SARS-CoV-2 omicron variant in South Africa: a data linkage study SARS-CoV-2 variants of concern and variants under investigation in England: Technical Briefing 31 Plasma neutralization properties of the SARS-CoV-2 Omicron variant. medRxiv Booster of mRNA-1273 vaccine reduces SARS-CoV-2 Omicron escape from neutralizing antibodies Effectiveness of SARS-CoV-2 vaccines in England in 2021: a whole population survival analysis Severity of Omicron variant of concern and vaccine effectiveness against symptomatic disease: national cohort with nested test negative design study in Scotland Proportion of each of the N=2,393 SARS-CoV-2 lineage detected in positive samples with at least 50% genome coverage from round 17 Supplementary Figure 1. Flow chart showing numbers of participants in round 15 Supplementary Table 1 . Unweighted and weighted prevalence of SARS-CoV-2 swab-positivity from REACT-1 across rounds 1 to 17. Table 3A . Weighted prevalence of SARS-CoV-2 swab-positivity in round 16 and round 17 by sex, age, region, urban/rural area, employment type, and ethnic group. Table 3B . Weighted prevalence of SARS-CoV-2 swab-positivity in round 16 and round 17 by household size, number of children in the household, contact with a COVID-19 case, reported previous COVID-19, protective behaviours, symptom status and neighbourhood deprivation. Table 4 . Distribution of self-reported dates of most recent positive SARS-CoV-2 test in REACT-1 participants from round 17. Results are presented for those reporting previous COVID-19 and those who did not, and for positive and negative swabs separately.