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Wastewater sampling for the detection and monitoring of SARS-CoV-2 has been developed and applied at an unprecedented pace, however uncertainty remains when interpreting the measured viral RNA signals and their spatiotemporal variation. The proliferation of measurements that are below a quantifiable threshold, usually during non-endemic periods, poses a further challenge to interpretation and time-series analysis of the data. Inspired by research in the use of a custom Kalman smoother model to estimate the true level of SARS-CoV-2 RNA concentrations in wastewater, we propose an alternative left-censored dynamic linear model. Cross-validation of both models alongside a simple moving average, using data from 286 sewage treatment works across England, allows for a comprehensive validation of the proposed approach. The presented dynamic linear model is more parsimonious, has a faster computational time and is represented by a more flexible modelling framework than the equivalent Kalman smoother. Furthermore we show how the use of wastewater data, transformed by such models, correlates more closely with regional case rate positivity as published by the Office for National Statistics (ONS) Coronavirus (COVID-19) Infection Survey. The modelled output is more robust and is therefore capable of better complementing traditional surveillance than untransformed data or a simple moving average, providing additional confidence and utility for public health decision making. La détection et la surveillance du SARS-CoV-2 dans les eaux usées ont été développées et réalisées à un rythme sans précédent, mais l'interprétation des mesures de concentrations en ARN viral, et de leurs variations spatio-temporelles, pose question. En particulier, l'importante proportion de mesures en deçà du seuil de quantification, généralement pendant les périodes non endémiques, constitue un défi pour l'analyse de ces séries temporelles. Inspirés par un travail de recherche ayant produit un lisseur de Kalman adapté pour estimer les concentrations réelles en ARN de SARS-CoV-2 dans les eaux usées à partir de ce type de données, nous proposons un nouveau modèle linéaire dynamique avec censure à gauche. Une validation croisée de ces lisseurs, ainsi que d'un simple lissage par moyenne glissante, sur des données provenant de 286 stations d'épuration couvrant l'Angleterre, valide de façon complète l'approche proposée. Le modèle présenté est plus parcimonieux, offre un cadre de modélisation plus flexible et nécessite un temps de calcul réduit par rapport au Lisseur de Kalman équivalent. Les données issues des eaux usées ainsi lissées sont en outre plus fortement corrélées avec le taux d'incidence régional produit par le bureau des statistiques nationales (ONS) Coronavirus Infection Survey. Elles se montrent plus robustes que les données brutes, ou lissées par simple moyenne glissante, et donc plus à même de compléter la surveillance traditionnelle, renforçant ainsi la confiance en l'épidémiologie fondée sur les eaux usées et son utilité pour la prise de décisions de santé publique.

BackgroundPaediatric pneumococcal conjugate vaccination (PCV) has reduced adult PCV-serotype disease: PCV7 has greater indirect effects than PCV13. Ongoing surveillance is required to evaluate current vaccine usage and inform future vaccine deployment, particularly with respiratory infection epidemiology changing following SARS-CoV-2 emergence.Methods and FindingsA retrospective cohort study, all adults ≥16 years admitted to three UK hospitals, 2006-2022, with pneumococcal disease. Medical records were reviewed for each clinical episode and serotype data were obtained from the UK Health Security Agency national reference laboratory.We identified 1,501 (40.3%) cases of invasive pneumococcal disease (IPD) with known serotype, 134 (3.6%) IPD cases without serotype data, and 2,084 (56.0%) non-IPD cases, which are typically missed in national surveillance. Disease incidence increased progressively from 2006-2020, followed by a sudden decline after COVID-19 emergence and then a gradual increase to pre-pandemic levels.Paediatric PCV7 introduction reduced adult PCV7 serotype IPD from 29.4% [24.1–35.4] of IPD in 2006-09 to 7.0% [3.7–12.7] in 2021-22. PCV13 introduction also decreased adult vaccine serotype IPD, but considerable residual adult disease remains, causing 34.3% [28.6–40.4] of IPD in 2006-09 and 21.7% [15.5–29.6] 9 in 2021-22, respectively. Serotype replacement diminished the benefits of PCV introduction: PCV20-13 and non-PCV serotypes represented 27.0% [21.9–32.9] and 9.3% [6.3–13.5] of disease in 2006-2009, and 39.5% [31.5–48.2] and 31.8% [24.4–40.2] in 2021-2022, respectively.Serotype shifts have resulted in increasing disease caused by serotype 3 and 8, and the re-emergence of serotype 19F and 19A. These serotype shifts occurred as clinical disease severity changed, and whilst the COVID-19 pandemic disrupted disease severity trends, these have now largely reverted to previous trajectories. Patient age trended upwards and although CURB65 severity decreased there were increased ICU admission rates. Overall, inpatient mortality decreased and hospitalisation duration remained stable.ConclusionsAfter 17 years of PCV use, residual pneumococcal disease due to the vaccine serotypes among hospitalised adults remains. The sharp decline in pneumococcal disease during the COVID-19 pandemic has now reversed, with increasing cases due to vaccine serotypes, especially serotype 3. Around 68.2% of cases in 2022 were potentially covered by the recently licensed 20-valent PCV.

BACKGROUND: There is an urgent public health need to evaluate disease severity in adults hospitalised with Delta and Omicron SARS-CoV-2 variant infections. However, limited data exist assessing severity of disease in adults hospitalised with Omicron SARS-CoV-2 infections, and to what extent patient-factors, including vaccination, age, frailty and pre-existing disease, affect variant-dependent disease severity.METHODS: A prospective cohort study of adults (≥18 years of age) hospitalised with acute lower respiratory tract disease at acute care hospitals in Bristol, UK conducted over 10-months. Delta or Omicron SARS-CoV-2 infection was defined by positive SARS-CoV-2 PCR and variant identification or inferred by dominant circulating variant. We constructed adjusted regression analyses to assess disease severity using three different measures: FiO2 >28% (fraction inspired oxygen), World Health Organization (WHO) outcome score >5 (assessing need for ventilatory support), and hospital length of stay (LOS) >3 days following admission for Omicron or Delta infection.FINDINGS: Independent of other variables, including vaccination, Omicron variant infection in hospitalised adults was associated with lower severity than Delta. Risk reductions were 58%, 67%, and 16% for supplementary oxygen with >28% FiO2 [Relative Risk (RR) = 0.42 (95%CI: 0.34-0.52), P < 0.001], WHO outcome score >5 [RR = 0.33 (95%CI: 0.21-0.50), P < 0.001], and to have had a LOS > 3 days [RR = 0.84 (95%CI: 0.76-0.92), P < 0.001]. Younger age and vaccination with two or three doses were also independently associated with lower COVID-19 severity.INTERPRETATION: We provide reassuring evidence that Omicron infection results in less serious adverse outcomes than Delta in hospitalised patients. Despite lower severity relative to Delta, Omicron infection still resulted in substantial patient and public health burden and an increased admission rate of older patients with Omicron which counteracts some of the benefit arising from less severe disease.FUNDING: AvonCAP is an investigator-led project funded under a collaborative agreement by Pfizer.

BACKGROUND: Whilst other studies have reported the effectiveness of mRNA vaccination against hospitalisation, including emergency department or intensive care admission, few have assessed effectiveness against other more clinically robust indices of COVID-19 severity.METHODS: A prospective single-centre test-negative design case-control study of adults hospitalised with COVID-19 disease or other acute respiratory disease between 1 June 2021 and 20 July 2022. We assessed VE (vaccine effectiveness) against hospitalisation, length of stay [LOS] >3 days, WHO COVID Score >5 and supplementary oxygen FiO2 (fraction inspired oxygen) >28%, conducting regression analyses controlling for age, gender, index of multiple deprivation, Charlson comorbidity index, time, and community infection prevalence.FINDINGS: 935 controls and 546 cases were hospitalised during the Delta period, with 721 controls and 372 cases hospitalised during the Omicron study period. Two-dose BNT162b2 was associated with VE 82.5% [95% confidence interval 76.2%-87.2%] against hospitalisation following Delta infection, 63.3% [26.9-81.8%], 58.5% [24.8-77.3%], and 51.5% [16.7-72.1%] against LOS >3 days, WHO COVID Score >5, and requirement for FiO2 >28% respectively. Three-dose BNT162b2 protection against hospitalisation with Omicron infection was 30.9% [5.9-49.3%], with sensitivity analyses ranging from 28.8-72.6%. Protection against LOS >3 days, WHO COVID Score >5 and requirement for FiO2 >28% was 56.1% [20.6-76.5%], 58.8% [31.2-75.8%], and 41.5% [-0.4-66.3%], respectively. In the UK, BNT162b2 was prioritised for high-risk individuals and those aged >75 years. In the latter group we found a higher estimate of VE against hospitalisation of 47.2% [16.8-66.6%].INTERPRETATION: BNT162b2 vaccination results in risk reductions for hospitalisation and multiple patient outcomes following Delta and Omicron COVID-19 infection, particularly in older adults. BNT162b2 remains effective against severe SARS-CoV-2 disease.FUNDING: AvonCAP is an investigator-led project funded under a collaborative agreement by Pfizer.

Background Predicting the likely size of future SARS-CoV-2 waves is necessary for public health planning. In England, voluntary “plan B” mitigation measures were introduced in December 2021 including increased home working and face coverings in shops but stopped short of restrictions on social contacts. The impact of voluntary risk mitigation behaviours on future SARS-CoV-2 burden is unknown. Methods We developed a rapid online survey of risk mitigation behaviours ahead of the winter 2021 festive period and deployed in two longitudinal cohort studies in the UK (Avon Longitudinal Study of Parents and Children (ALSPAC) and TwinsUK/COVID Symptom Study (CSS) Biobank) in December 2021. Using an individual-based, probabilistic model of COVID-19 transmission between social contacts with SARS-CoV-2 Omicron variant parameters and realistic vaccine coverage in England, we predicted the potential impact of the SARS-CoV-2 Omicron wave in England in terms of the effective reproduction number and cumulative infections, hospital admissions and deaths. Using survey results, we estimated in real-time the impact of voluntary risk mitigation behaviours on the Omicron wave in England, if implemented for the entire epidemic wave. Results Over 95% of survey respondents (NALSPAC = 2686 and NTwins = 6155) reported some risk mitigation behaviours, with vaccination and using home testing kits reported most frequently. Less than half of those respondents reported that their behaviour was due to “plan B”. We estimate that without risk mitigation behaviours, the Omicron variant is consistent with an effective reproduction number between 2.5 and 3.5. Due to the reduced vaccine effectiveness against infection with the Omicron variant, our modelled estimates suggest that between 55% and 60% of the English population could be infected during the current wave, translating into between 12,000 and 46,000 cumulative deaths, depending on assumptions about severity and vaccine effectiveness. The actual number of deaths was 15,208 (26 November 2021–1 March 2022). We estimate that voluntary risk reduction measures could reduce the effective reproduction number to between 1.8 and 2.2 and reduce the cumulative number of deaths by up to 24%. Conclusions Predicting future infection burden is affected by uncertainty in disease severity and vaccine effectiveness estimates. In addition to biological uncertainty, we show that voluntary measures substantially reduce the projected impact of the SARS-CoV-2 Omicron variant but that voluntary measures alone would be unlikely to completely control transmission.

The serial interval of an infectious disease, commonly interpreted as the time between the onset of symptoms in sequentially infected individuals within a chain of transmission, is a key epidemiological quantity involved in estimating the reproduction number. The serial interval is closely related to other key quantities, including the incubation period, the generation interval (the time between sequential infections), and time delays between infection and the observations associated with monitoring an outbreak such as confirmed cases, hospital admissions, and deaths. Estimates of these quantities are often based on small data sets from early contact tracing and are subject to considerable uncertainty, which is especially true for early coronavirus disease 2019 data. In this paper, we estimate these key quantities in the context of coronavirus disease 2019 for the UK, including a meta-analysis of early estimates of the serial interval. We estimate distributions for the serial interval with a mean of 5.9 (95% CI 5.2; 6.7) and SD 4.1 (95% CI 3.8; 4.7) days (empirical distribution), the generation interval with a mean of 4.9 (95% CI 4.2; 5.5) and SD 2.0 (95% CI 0.5; 3.2) days (fitted gamma distribution), and the incubation period with a mean 5.2 (95% CI 4.9; 5.5) and SD 5.5 (95% CI 5.1; 5.9) days (fitted log-normal distribution). We quantify the impact of the uncertainty surrounding the serial interval, generation interval, incubation period, and time delays, on the subsequent estimation of the reproduction number, when pragmatic and more formal approaches are taken. These estimates place empirical bounds on the estimates of most relevant model parameters and are expected to contribute to modeling coronavirus disease 2019 transmission.

espanolObjetivo. Estimar la seroprevalencia de SARS-CoV-2 en po-blacion de edad escolar en Mexico. Material y metodos. Se categorizaron a ninos y adolescentes que participaron en la Encuesta Nacional de Salud y Nutricion 2020 sobre Covid-19 (Ensanut 2020 Covid-19) por edad escolar y nivel educativo. En participantes seropositivos, se identifico la pro-porcion de infecciones asintomaticas. Se estimaron razones de prevalencia usando un modelo de regresion log-binomial. Resultados. La seroprevalencia en educacion basica y media fue de 18.7% (IC95%: 14.9, 22.5) y 26.7% (IC95%: 22.1, 31.3), respectivamente. La infeccion asintomatica fue mas frecuente en educacion basica (88.5% [IC95%: 80.5, 93.5]). Conclusiones. En poblacion de educacion basica la infeccion por SARS-CoV-2 es baja y usualmente asintomatica. EnglishObjective. To estimate the seroprevalence of SARS-CoV-2 in school aged children in Mexico. Materials and methods. We categorized children and adolescents who participated in 2020 National Health and Nutrition Survey on Covid-19 according to school age and educational level. In seropositive participants, we identified the proportion of as-ymptomatic infections. We estimated prevalence ratios using a log-binomial regression model. Results. Seroprevalence of SARS-CoV-2 for primary and secondary education were 18.7% (95%IC: 14.9, 22.5) and 26.7% (95%IC: 22.1, 31.3), re-spectively. Asymptomatic infection was more frequent among primary school children (88.5% [95%IC: 80.5, 93.5]). Conclu-sions. In primary schoolchildren seroprevalence for SARS-CoV-2 was low and infections were usually asymptomatic.

ObjectivesWe aimed to evaluate the safety and optimal dose of a novel inactivated whole-virus adjuvanted vaccine against SARS-CoV-2: VLA2001.MethodsWe conducted an open-label, dose-escalation study followed by a double-blind randomized trial using low, medium and high doses of VLA2001 (1:1:1). The primary safety outcome was the frequency and severity of solicited local and systemic reactions within 7 days after vaccination. The primary immunogenicity outcome was the geometric mean titre (GMT) of neutralizing antibodies against SARS-CoV-2 two weeks after the second vaccination. The study is registered as NCT04671017.ResultsBetween December 16, 2020, and June 3, 2021, 153 healthy adults aged 18–55 years were recruited in the UK. Overall, 81.7% of the participants reported a solicited AE, with injection site tenderness (58.2%) and headache (46.4%) being the most frequent. Only 2 participants reported a severe solicited event. Up to day 106, 131 (85.6%) participants had reported any AE. All observed incidents were transient and non-life threatening in nature. Immunogenicity measured at 2 weeks after completion of the two-dose priming schedule, showed significantly higher GMTs of SARS-CoV-2 neutralizing antibody titres in the highest dose group (GMT 545.6; 95% CI: 428.1, 695.4) which were similar to a panel of convalescent sera (GMT 526.9; 95% CI: 336.5, 825.1). Seroconversion rates of neutralizing antibodies were also significantly higher in the high-dose group (>90%) compared to the other dose groups. In the high dose group, antigen-specific IFN-γ expressing T-cells reactive against the S, M and N proteins were observed in 76, 36 and 49%, respectively.ConclusionsVLA2001 was well tolerated in all tested dose groups, and no safety signal of concern was identified. The highest dose group showed statistically significantly stronger immunogenicity with similar tolerability and safety, and was selected for phase 3 clinical development.

BackgroundThe emergence of COVID-19 and public health measures implemented to reduce SARS-CoV-2 infections have both affected acute lower respiratory tract disease (aLRTD) epidemiology and incidence trends. The severity of COVID-19 and non-SARS-CoV-2 aLRTD during this period have not been compared in detail.MethodsWe conducted a prospective cohort study of adults age ≥18 years admitted to either of two acute care hospitals in Bristol, UK, from August 2020 to November 2021. Patients were included if they presented with signs or symptoms of aLRTD (e.g., cough, pleurisy), or a clinical or radiological aLRTD diagnosis.Findings12,557 adult aLRTD hospitalisations occurred: 10,087 were associated with infection (pneumonia or non-pneumonic lower respiratory tract infection [NP-LRTI]), 2161 with no infective cause, with 306 providing a minimal surveillance dataset. Confirmed SARS-CoV-2 infection accounted for 32% (3178/10,087) of respiratory infections. Annual incidences of overall, COVID-19, and non- SARS-CoV-2 pneumonia were 714.1, 264.2, and 449.9, and NP-LRTI were 346.2, 43.8, and 302.4 per 100,000 adults, respectively. Weekly incidence trends in COVID-19 aLRTD showed large surges (median 6.5 [IQR 0.7–10.2] admissions per 100,000 adults per week), while other infective aLRTD events were more stable (median 14.3 [IQR 12.8–16.4] admissions per 100,000 adults per week) as were non-infective aLRTD events (median 4.4 [IQR 3.5–5.5] admissions per 100,000 adults per week).InterpretationWhile COVID-19 disease was a large component of total aLRTD during this pandemic period, non- SARS-CoV-2 infection still caused the majority of respiratory infection hospitalisations. COVID-19 disease showed significant temporal fluctuations in frequency, which were less apparent in non-SARS-CoV-2 infection. Despite public health interventions to reduce respiratory infection, disease incidence remains high.FundingAvonCAP is an investigator-led project funded under a collaborative agreement by Pfizer.

Accurate surveillance of the COVID-19 pandemic can be weakened by under-reporting of cases, particularly due to asymptomatic or pre-symptomatic infections, resulting in bias. Quantification of SARS-CoV-2 RNA in wastewater can be used to infer infection prevalence, but uncertainty in sensitivity and considerable variability has meant that accurate measurement remains elusive. Here, we use data from 45 sewage sites in England, covering 31% of the population, and estimate SARS-CoV-2 prevalence to within 1.1% of estimates from representative prevalence surveys (with 95% confidence). Using machine learning and phenomenological models, we show that differences between sampled sites, particularly the wastewater flow rate, influence prevalence estimation and require careful interpretation. We find that SARS-CoV-2 signals in wastewater appear 4–5 days earlier in comparison to clinical testing data but are coincident with prevalence surveys suggesting that wastewater surveillance can be a leading indicator for symptomatic viral infections. Surveillance for viruses in wastewater complements and strengthens clinical surveillance, with significant implications for public health.

Limited data exist assessing severity of disease in adults hospitalised with Omicron SARS-CoV-2 variant infections, and to what extent patient-factors, including vaccination and pre-existing disease, affect variant-dependent disease severity. This prospective cohort study of all adults (≥18 years of age) hospitalised at acute care hospitals in Bristol, UK assessed disease severity using 3 different measures: FiO2 >28%, World Health Organization (WHO) outcome score >5, and hospital length of stay (LOS) >3 days following admission for Omicron or Delta variant infection. Independent of other variables, including vaccination, Omicron variant infection was associated with a statistically lower severity compared to Delta; risk reductions were 58%, 67%, and 16% for FiO2, WHO score, and LOS, respectively. Younger age and vaccination with two or three doses were also independently associated with lower COVID-19 severity. Despite lower severity relative to Delta, Omicron infection still resulted in substantial patient and public health burden following admission.

BackgroundThere is an urgent public health need to evaluate disease severity in adults hospitalised with Delta and Omicron SARS-CoV-2 variant infections. However, limited data exist assessing severity of disease in adults hospitalised with Omicron SARS-CoV-2 infections, and to what extent patient-factors, including vaccination, age, frailty and pre-existing disease, affect variant-dependent disease severity.MethodsA prospective cohort study of adults (≥18 years of age) hospitalised with acute lower respiratory tract disease at acute care hospitals in Bristol, UK conducted over 10-months. Delta or Omicron SARS-CoV-2 infection was defined by positive SARS-CoV-2 PCR and variant identification or inferred by dominant circulating variant. We constructed adjusted regression analyses to assess disease severity using three different measures: FiO2>28% (fraction inspired oxygen), World Health Organization (WHO) outcome score >5 (assessing need for ventilatory support), and hospital length of stay (LOS) >3 days following admission for Omicron or Delta infection.FindingsIndependent of other variables, including vaccination, Omicron variant infection in hospitalised adults was associated with lower severity than Delta. Risk reductions were 58%, 67%, and 16% for supplementary oxygen with >28% FiO2[Relative Risk (RR)=0·42 (95%CI: 0·34-0·52),P5 [RR=0·33 (95%CI: 0·21-0·50),P3 days [RR=0·84 (95%CI: 0·76-0·92),PInterpretationWe provide reassuring evidence that Omicron infection results in less serious adverse outcomes than Delta in hospitalised patients. Despite lower severity relative to Delta, Omicron infection still resulted in substantial patient and public health burden and an increased admission rate of older patients with Omicron which counteracts some of the benefit arising from less severe disease.FundingAvonCAP is an investigator-led project funded under a collaborative agreement by Pfizer.RESEARCH IN CONTEXTEvidence before this studyThe burden of COVID-19 on hospital services is determined by the prevalence and severity of SARS-CoV-2 variants, and modified by individual factors such as age, frailty and vaccination status. Real world data suggest that vaccine effectiveness is lower and may wane faster over time against symptomatic disease with Omicron (B.1.1.529) than with Delta (B.1.617.2) SARS-CoV-2 variant. However, numbers of hospitalisations as a case proportion during the Omicron wave have been considerably lower than previous waves. Several reports have compared the risk of hospitalisation or severe disease based on SARS-CoV-2 variant, some suggesting that Omicron is probably less severe than Delta in vaccinated and unvaccinated individuals.Added value of this studyThis study provides robust data assessing the relative severity of Delta and Omicron SARS-CoV-2 variants in patients admitted to hospital, including the first analysis assessing risk for any positive pressure ventilatory support, as well as risk of supplementary oxygen requirement and extended hospital admission, that may guide resource planning in hospitals. We found evidence that infection with Omicron was associated with a milder clinical course following hospital admission than that caused by Delta and that vaccination was independently associated with lower in-hospital disease severity using these three separate severity measures. Specifically, compared to Delta, Omicron-related hospitalizations were 58%, 67%, and 16% less likely to require high flow oxygen >28% FiO2, positive pressure ventilatory support or more critical care, and to have a hospital stay lasting more than three days, respectively.This study reports the considerable morbidity resulting from Omicron infection, with 18% of Omicron admissions requiring oxygen supplementation FiO2>28%, 6% requiring positive pressure ventilation, 62% needing hospitalization ≥four days, and 4% in-hospital mortality. In determining the reduced requirement of increased oxygen requirement and total positive pressure requirement, including non-invasive ventilation, this analysis should contribute to future hospital care and service planning assessments.Implications of all the available evidenceThe risk of severe outcomes following SARS-CoV-2 infection is substantially lower for Omicron than for Delta, with greater reductions for more severe disease outcomes. Significant variation in risk occurs with age and vaccination status, with older and unvaccinated individuals remaining at particular risk of adverse outcome. These results highlight the importance of maintaining high levels of vaccine coverage in patient groups at risk of severe disease.The impact of lower severity Omicron-related hospitalization must be balanced against increased transmissibility and overall higher numbers of infections with this variant and there remains a substantial patient and public health burden. The increased admission rate of older patients with Omicron counteracts some of the benefit arising from less severe disease. Despite the risk reduction in high level oxygen supplementation requirement and high dependency care with Omicron compared to earlier variants at the individual level, healthcare systems could still be overwhelmed.

Most of the current evidence regarding pneumococcal upper respiratory colonization in adults suggests that despite high disease burden, carriage prevalence is low. Contemporary studies on adult pne...

Background Delay or failure to view test results in a hospital setting can lead to delayed diagnosis, risk of patient harm, and represents inefficiency. Factors influencing this were investigated to identify how timeliness and completeness of test review could be improved through an evidence-based redesign of the use of clinical test review software. Methods A cross-section of all abnormal hematology and biochemistry results which were published on a digital test review platform over a 3-year period were investigated. The time it took for clinicians to view these results, and the results that were not viewed within 30 days, were analyzed relative to time of the week, the detailed type of test, and an indicator of patient record data quality. Results The majority of results were viewed within 90 min, and 93.9% of these results viewed on the digital platform within 30 days. There was significant variation in results review throughout the week, shown to be due to an interplay between technical and clinical workflow factors. Routine results were less likely to be reviewed, as were those with patient record data quality issues. Conclusion The evidence suggests that test result review would be improved by stream-lining access to the result platform, differentiating between urgent and routine results, improving handover of responsibility for result review, and improving search for temporary patient records. Altering the timing of phlebotomy rounds and a review of the appropriateness of routine test requests at the weekend may also improve result review rates.

RationaleStreptococcus pneumoniaeepidemiology is changing in response to vaccination and some data suggest empyema incidence is increasing. However, differences exist between UK and USA studies. We describe trends in the clinical phenotype of adult pneumococcal pleural infection, including simple parapneumonic effusions (SPE) in the pneumococcal conjugate vaccination (PCV) era.ObjectivesTo determine whether there were differences in pneumococcal disease presentation and severity associated with pleural infection.MethodsA retrospective cohort study, all adults ≥16 years admitted to three large UK hospitals, 2006-2018 with pneumococcal disease. 2477 invasive pneumococcal cases were identified: 459 SPE and 100 pleural infection cases. Medical records were reviewed for each clinical episode. Serotype data were obtained from the UK Health Security Agency national reference laboratory.ResultsIncidence increased over time, including non-PCV-serotype disease. PCV7-serotype disease declined following paediatric PCV7 introduction, but the effect of PCV13 was less apparent as disease caused by the additional six serotypes plateaued with serotypes 1 and 3 causing such parapneumonic effusions from 2011 onwards.Patients with pleural infection had a median survival 468 days (95% CI 340-590), versus 286 days (95% CI 274-335) in those with SPE. Pleural infection associated with frank pus had lower 90-day mortality than pleural infection without pus (0% versus 29%,PP=0.049).ConclusionsPneumococcal infection continues to cause severe disease despite the introduction of PCVs. The predominance of serotype 1 and 3 in this adult UK cohort is in keeping with previous studies in paediatric and non-UK studies. Rising non-PCV serotype disease and limited impact of PCV13 on cases caused by serotypes 1 and 3 offset the reductions in adult pneumococcal parapneumonic effusion disease burden observed following introduction of the childhood PCV7 programme.KEY MESSAGESWhat is already known on this topicThe epidemiology of pneumococcal infection is changing in both adults and children following pneumococcal conjugate vaccine (PCV) introduction, as a result of direct and indirect vaccine effects. Other studies have reported that serotypes 1 and 3 disproportionately cause pneumococcal pleural disease; however, the clinical phenotype of parapneumonic effusions associated with pneumococcal infection in adults following PCV introduction is not well described.What this study addsIn this study which presents the largest cohort of patients with a single-organism pleural infection, we demonstrate an increasing incidence of parapneumonic effusions related toStreptococcus pneumoniaein adults, attributable to serotype 1 and 3 disease, despite the introduction of PCV13 in the UK childhood vaccination programme. Interestingly, our data suggest that pneumococcal pleural infection is associated with improved survival up to one-year compared to patients with pneumococcal simple parapneumonic effusions.How this study might affect research, practice or policyCareful assessment of the need for specialist respiratory and thoracic surgical intervention in the context of increasing incidence of adult parapneumonic effusions related toStreptococcus pneumoniaewill be required, in addition to ongoing monitoring of the effect on serotype distribution and clinical phenotype of current and future vaccines against pneumococcus.

BackgroundThe Omicron variant of SARS-CoV-2 infection poses substantial challenges to public health. In England, “plan B” mitigation measures were introduced in December 2021 including increased home working and face coverings in shops, but stopped short of restrictions on social contacts. The impact of voluntary risk mitigation behaviours on future SARS-CoV-2 burden is unknown.MethodsWe developed a rapid online survey of risk mitigation behaviours during the winter 2021 festive period and deployed in two longitudinal cohort studies in the UK (Avon Longitudinal Study of Parents and Children (ALSPAC) and TwinsUK/Covid Symptom Study (CSS) Biobank) in December 2021. Using an individual-based, probabilistic model of COVID-19 transmission between social contacts with SARS-CoV-2 Omicron variant parameters and realistic vaccine coverage in England, we describe the potential impact of the SARS-CoV-2 Omicron wave in England in terms of the effective reproduction number and cumulative infections, hospital admissions and deaths. Using survey results, we estimated in real-time the impact of voluntary risk mitigation behaviours on the Omicron wave in England, if implemented for the entire epidemic wave.ResultsOver 95% of survey respondents (NALSPAC=2,686 and NTwins=6,155) reported some risk mitigation behaviours, with vaccination and using home testing kits reported most frequently. Less than half of those respondents reported that their behaviour was due to “plan B”. We estimate that without risk mitigation behaviours, the Omicron variant is consistent with an effective reproduction number between 2.5 and 3.5. Due to the reduced vaccine effectiveness against infection with the Omicron variant, our modelled estimates suggest that between 55% and 60% of the English population could be infected during the current wave, translating into between 15,000 and 46,000 cumulative deaths, depending on assumptions about vaccine effectiveness. We estimate that voluntary risk reduction measures could reduce the effective reproduction number to between 1.8 and 2.2 and reduce the cumulative number of deaths by up to 24%.ConclusionsWe conclude that voluntary measures substantially reduce the projected impact of the SARS-CoV-2 Omicron variant, but that voluntary measures alone would be unlikely to completely control transmission.

Throughout the ongoing COVID-19 pandemic, the worldwide transmission and replication of SARS-COV-2, the causative agent of COVID-19 disease, has resulted in the opportunity for multiple mutations to occur that may alter the virus transmission characteristics, the effectiveness of vaccines and the severity of disease upon infection. The Omicron variant (B.1.1.529) was first reported to the WHO by South Africa on 24 November 2021 and was declared a variant of concern by the WHO on 26 November 2021. The variant was first detected in the UK on 27 November 2021 and has since been reported in a number of countries globally where it is frequently associated with rapid increase in cases. Here we present analyses of UK data showing the earliest signatures of the Omicron variant and mathematical modelling that uses the UK data to simulate the potential impact of this variant in the UK. In order to account for the uncertainty in transmission advantage, vaccine escape and severity at the time of writing, we carry out a sensitivity analysis to assess the impact of these variant characteristics on future risk.

We investigate the impact of vaccination and asymptomatic testing uptake on SARS-CoV-2 transmission in a university student population using a stochastic compartmental model. We find that the magnitude and timing of outbreaks is highly variable depending on the transmissibility of the most dominant strain of SARS CoV-2 and under different vaccine uptake levels and efficacies. When delta is the dominant strain, low level interventions (no asymptomatic testing, 30% vaccinated with a vaccine that is 80% effective at reducing infection) lead to 53-71% of students become infected during the first term. Asymptomatic testing is most useful when vaccine uptake is low: when 30% of students are vaccinated, 90% uptake of asymptomatic testing leads to almost half the case numbers. With high interventions (90% using asymptomatic testing, 90% vaccinated) cumulative incidence is 7-9%, with around 80% of these cases estimated to be asymptomatic. However, under emergence of a new variant that is at least twice as transmissible as delta and with the vaccine efficacy against infection reduced to 55%, large outbreaks are likely in universities, even with very high (90%) uptake of vaccination and 100% uptake of asymptomatic testing. If vaccine efficacy against infection against this new variant is higher (70%), then outbreaks can be mitigated if there is least 50% uptake of asymptomatic testing additional to 90% uptake of vaccination. Our findings suggest that effective vaccination is critical for controlling SARS-CoV-2 transmission in university settings with asymptomatic testing ranging from additionally useful to critical, depending on effectiveness and uptake of vaccination. Other measures may be necessary to control outbreaks under the emergence of a more transmissible variant with vaccine escape.

UK universities re-opened in September 2020, amidst the coronavirus epidemic. During the first term, various national social distancing measures were introduced, including banning groups of >6 people and the second lockdown in November; however, outbreaks among university students occurred. We aimed to measure the University of Bristol staff and student contact patterns via an online, longitudinal survey capturing self-reported contacts on the previous day. We investigated the change in contacts associated with COVID-19 guidance periods: post-first lockdown (23/06/2020–03/07/2020), relaxed guidance period (04/07/2020–13/09/2020), ‘rule-of-six’ period (14/09/2020–04/11/2020) and the second lockdown (05/11/2020–25/11/2020). In total, 722 staff (4199 responses) and 738 students (1906 responses) were included in the study. For staff, daily contacts were higher in the relaxed guidance and ‘rule-of-six’ periods than the post-first lockdown and second lockdown. Mean student contacts dropped between the ‘rule-of-six’ and second lockdown periods. For both staff and students, the proportion meeting with groups larger than six dropped between the ‘rule-of-six’ period and the second lockdown period, although was higher for students than for staff. Our results suggest university staff and students responded to national guidance by altering their social contacts. Most contacts during the second lockdown were household contacts. The response in staff and students was similar, suggesting that students can adhere to social distancing guidance while at university. The number of contacts recorded for both staff and students were much lower than those recorded by previous surveys in the UK conducted before the COVID-19 pandemic.

As a countermeasure to the SARS-CoV-2 pandemic, there has been swift development and clinical trial assessment of candidate vaccines, with subsequent deployment as part of mass vaccination campaigns. However, the SARS-CoV-2 virus has demonstrated the ability to mutate and develop variants, which can modify epidemiological properties and potentially also the effectiveness of vaccines. The widespread deployment of highly effective vaccines may rapidly exert selection pressure on the SARS-CoV-2 virus directed towards mutations that escape the vaccine-induced immune response. This is particularly concerning while infection is widespread. By developing and analysing a mathematical model of two population groupings with differing vulnerability and contact rates, we explore the impact of the deployment of vaccines among the population on the reproduction ratio, cases, disease abundance and vaccine escape pressure. The results from this model illustrate two insights: (i) vaccination aimed at reducing prevalence could be more effective at reducing disease than directly vaccinating the vulnerable; (ii) the highest risk for vaccine escape can occur at intermediate levels of vaccination. This work demonstrates a key principle: the careful targeting of vaccines towards particular population groups could reduce disease as much as possible while limiting the risk of vaccine escape.

Many countries have banned groups and gatherings as part of their response to the pandemic caused by the coronavirus, SARS-CoV-2. Although there are outbreak reports involving mass gatherings, the contribution to overall transmission is unknown. We used data from a survey of social contact behaviour that specifically asked about contact with groups to estimate the population attributable fraction (PAF) due to groups as the relative change in the basic reproduction number when groups are prevented. Groups of 50+ individuals accounted for 0.5% of reported contact events, and we estimate that the PAF due to groups of 50+ people is 5.4% (95% confidence interval 1.4%, 11.5%). The PAF due to groups of 20+ people is 18.9% (12.7%, 25.7%) and the PAF due to groups of 10+ is 25.2% (19.4%, 31.4%). Under normal circumstances with pre-COVID-19 contact patterns, large groups of individuals have a relatively small epidemiological impact; small- and medium-sized groups between 10 and 50 people have a larger impact on an epidemic. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

An outbreak of a novel coronavirus was first reported in China on 31 December 2019. As of 9 February 2020, cases have been reported in 25 countries, including probable human-to-human transmission in England. We adapted an existing national-scale metapopulation model to capture the spread of COVID-19 in England and Wales. We used 2011 census data to inform population sizes and movements, together with parameter estimates from the outbreak in China. We predict that the epidemic will peak 126 to 147 days (approx. 4 months) after the start of person-to-person transmission in the absence of controls. Assuming biological parameters remain unchanged and transmission persists from February, we expect the peak to occur in June. Starting location and model stochasticity have a minimal impact on peak timing. However, realistic parameter uncertainty leads to peak time estimates ranging from 78 to 241 days following sustained transmission. Seasonal changes in transmission rate can substantially impact the timing and size of the epidemic. We provide initial estimates of the epidemic potential of COVID-19. These results can be refined with more precise parameters. Seasonal changes in transmission could shift the timing of the peak into winter, with important implications for healthcare capacity planning. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK.

On March 23 2020, the UK enacted an intensive, nationwide lockdown to mitigate transmission of COVID-19. As restrictions began to ease, more localized interventions were used to target resurgences in transmission. Understanding the spatial scale of networks of human interaction, and how these networks change over time, is critical to targeting interventions at the most at-risk areas without unnecessarily restricting areas at low risk of resurgence. We use detailed human mobility data aggregated from Facebook users to determine how the spatially-explicit network of movements changed before and during the lockdown period, in response to the easing of restrictions, and to the introduction of locally-targeted interventions. We also apply community detection techniques to the weighted, directed network of movements to identify geographically-explicit movement communities and measure the evolution of these community structures through time. We found that the mobility network became more sparse and the number of mobility communities decreased under the national lockdown, a change that disproportionately affected long distance connections central to the mobility network. We also found that the community structure of areas in which locally-targeted interventions were implemented following epidemic resurgence did not show reorganization of community structure but did show small decreases in indicators of travel outside of local areas. We propose that communities detected using Facebook or other mobility data be used to assess the impact of spatially-targeted restrictions and may inform policymakers about the spatial extent of human movement patterns in the UK. These data are available in near real-time, allowing quantification of changes in the distribution of the population across the UK, as well as changes in travel patterns to inform our understanding of the impact of geographically-targeted interventions., Author summary Large-scale intensive interventions in response to the COVID-19 pandemic have been implemented globally, significantly affecting human movement patterns. Mobility data provide an empirical measurement of UK travel within a spatially-explicit network, but it is not clear how the structure of that network changed in response to national or locally-targeted interventions. We use daily mobility data aggregated from Facebook users to quantify changes in the travel network in the UK during the national lockdown, and in response to local interventions. We identified changes in human behaviour in response to interventions and identified the community structure inherent in these networks. This approach can help quantify the extent of strongly connected communities of interaction and their relationship to the extent of spatially-explicit interventions. We show that spatial mobility data available in near real-time can give information on connectivity that can be used to understand the impact of geographically-targeted interventions and in the future, to inform these intervention strategies.

Mobility data have demonstrated major changes in human movement patterns in response to COVID-19 and associated interventions in many countries. This can involve sub-national redistribution, short-term relocations as well as international migration. In this paper, we combine detailed location data from Facebook measuring the location of approximately 6 million daily active Facebook users in 5km2 tiles in the UK with census-derived population estimates to measure population mobility and redistribution. We provide time-varying population estimates and assess spatial population changes with respect to population density and four key reference dates in 2020 (First lockdown, End of term, Beginning of term, Christmas). We also show how population estimates derived from the distribution of Facebook users vary compared to mid-2020 small area population estimates by the UK national statistics agencies. We estimate that between March 2020 and March 2021, the total population of the UK declined and we identify important spatial variations in this population change, showing that low-density areas have experienced lower population decreases than urban areas. We estimate that, for the top 10% highest population tiles, the population has decreased by 6.6%. Further, we provide evidence that geographic redistributions of population within the UK coincide with dates of non-pharmaceutical interventions including lockdowns and movement restrictions, as well as seasonal patterns of migration around holiday dates. The methods used in this study reveal significant changes in population distribution at high spatial and temporal resolutions that have not previously been quantified by available demographic surveys in the UK. We found early indicators of potential longer-term changes in the population distribution of the UK although it is not clear if these changes may persist after the COVID-19 pandemic.

The rapid emergence of SARS-CoV-2 mutants with new phenotypic properties is a critical challenge to the control of the ongoing pandemic. B.1.1.7 was monitored in the UK through routine testing and S-gene target failures (SGTF), comprising over 90% of cases by March 2021. Now, the reverse is occurring: SGTF cases are being replaced by an S-gene positive variant, which we associate with B.1.617.2. Evidence from the characteristics of S-gene positive cases demonstrates that, following importation, B.1.617.2 is transmitted locally, growing at a rate higher than B.1.1.7 and a doubling time between 5-14 days. S-gene positive cases should be prioritised for sequencing and aggressive control in any countries in which this variant is newly detected.One-Sentence SummaryThe B.1.617.2 variant of SARS-CoV-2 is replacing B.1.1.7 and emerging as the dominant variant in England, evidenced by sustained local transmission.

University students have unique living, learning and social arrangements which may have implications for infectious disease transmission. To address this data gap, we created CONQUEST (COroNavirus QUESTionnaire), a longitudinal online survey of contacts, behaviour, and COVID-19 symptoms for University of Bristol (UoB) staff/students. Here, we analyse results from 740 students providing 1261 unique records from the start of the 2020/2021 academic year (14/09/2020–01/11/2020), where COVID-19 outbreaks led to the self-isolation of all students in some halls of residences. Although most students reported lower daily contacts than in pre-COVID-19 studies, there was heterogeneity, with some reporting many (median = 2, mean = 6.1, standard deviation = 15.0; 8% had ≥ 20 contacts). Around 40% of students’ contacts were with individuals external to the university, indicating potential for transmission to non-students/staff. Only 61% of those reporting cardinal symptoms in the past week self-isolated, although 99% with a positive COVID-19 test during the 2 weeks before survey completion had self-isolated within the last week. Some students who self-isolated had many contacts (mean = 4.3, standard deviation = 10.6). Our results provide context to the COVID-19 outbreaks seen in universities and are available for modelling future outbreaks and informing policy.

Infectious disease modelling has played an integral part of the scientific evidence used to guide the response to the COVID-19 pandemic. In the UK, modelling evidence used for policy is reported to the Scientific Advisory Group for Emergencies (SAGE) modelling subgroup, SPI-M-O (Scientific Pandemic Influenza Group on Modelling-Operational). This Special Issue contains 20 articles detailing evidence that underpinned advice to the UK government during the SARS-CoV-2 pandemic in the UK between January 2020 and July 2020. Here, we introduce the UK scientific advisory system and how it operates in practice, and discuss how infectious disease modelling can be useful in policy making. We examine the drawbacks of current publishing practices and academic credit and highlight the importance of transparency and reproducibility during an epidemic emergency. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

Background Social distancing measures, including school closures, are being used to control SARS-CoV-2 transmission in many countries. Once “lockdown” has driven incidence to low levels, selected activities are being permitted. Re-opening schools is a priority because of the welfare and educational impact of closures on children. However, the impact of school re-opening needs to be considered within the context of other measures. Methods We use social contact data from the UK to predict the impact of social distancing policies on the reproduction number. We calibrate our tool to the COVID-19 epidemic in the UK using publicly available death data and Google Community Mobility Reports. We focus on the impact of re-opening schools against a back-drop of wider social distancing easing. Results We demonstrate that pre-collected social contact data, combined with incidence data and Google Community Mobility Reports, is able to provide a time-varying estimate of the reproduction number (R). From an pre-control setting when R=2.7 (95%CI 2.5, 2.9), we estimate that the minimum reproduction number that can be achieved in the UK without limiting household contacts is 0.45 (95%CI: 0.41 − 0.50); in the absence of other changes, preventing leisure contacts has a smaller impact (R = 2.0, 95%CI: 1.8 − 2.4) than preventing work contacts (R = 1.5, 95%CI: 1.4 − 1.7). We find that following lockdown (when R=0.7 (95% CI 0.6, 0.8)), opening primary schools in isolation has a modest impact on transmission R = 0.83 (95%CI: 0.77 − 0.90) but that high adherence to other measures is needed. Opening secondary schools as well as primary school is predicted to have a larger overall impact (R = 0.95, 95%CI: 0.85 − 1.07), however transmission could still be controlled with effective contact tracing. Conclusions Our findings suggest that primary school children can return to school without compromising transmission, however other measures, such as social distancing and contract tracing, are required to control transmission if all age groups are to return to school. Our tool provides a mapping from policies to the reproduction number and can be used by policymakers to compare the impact of social-easing measures, dissect mitigation strategies and support careful localized control strategies.

As a counter measure to the SARS-CoV-2 pandemic there has been swift development and clinical trial assessment of candidate vaccines, with subsequent deployment as part of mass vaccination campaigns. However, the SARS-CoV-2 virus has demonstrated the ability to mutate and develop variants, which can modify epidemiological properties and potentially also the effectiveness of vaccines.The widespread deployment of highly effective vaccines may rapidly exert selection pressure on the SARS-CoV-2 virus directed towards mutations that escape the vaccine induced immune response. This is particularly concerning whilst infection is widespread. By developing and analysing a mathematical model of two population groupings with differing vulnerability and contact rates, we explore the impact of the deployment of vaccine amongst the population on R, cases, disease abundance and vaccine escape pressure.The results from this model illustrate two insights (i) vaccination aimed at reducing prevalence could be more effective at reducing disease than directly vaccinating the vulnerable; (ii) the highest risk for vaccine escape can occur at intermediate levels of vaccination. This work demonstrates a key principle that the careful targeting of vaccines towards particular population groups could reduce disease as much as possible whilst limiting the risk of vaccine escape.

Background:On Dec 8, 2020, deployment of the first SARS-CoV-2 vaccination authorised for UK use (BNT162b2 mRNA vaccine) began, followed by an adenoviral vector vaccine ChAdOx1 nCoV-19 on Jan 4, 2021. Care home residents and staff, frontline health-care workers, and adults aged 80 years and older were vaccinated first. However, few data exist regarding the effectiveness of these vaccines in older people with many comorbidities. In this post-implementation evaluation of two COVID-19 vaccines, we aimed to determine the effectiveness of one dose in reducing COVID-19-related admissions to hospital in people of advanced age.Methods:This prospective test-negative case-control study included adults aged at least 80 years who were admitted to hospital in two NHS trusts in Bristol, UK with signs and symptoms of respiratory disease. Patients who developed symptoms before receiving their vaccine or those who received their vaccine after admission to hospital were excluded, as were those with symptoms that started more than 10 days before hospital admission. We did logistic regression analysis, controlling for time (week), sex, index of multiple deprivations, and care residency status, and sensitivity analyses matched for time and sex using a conditional logistic model adjusting for index of multiple deprivations and care residency status. This study is registered with ISRCTN, number 39557.Findings:Between Dec 18, 2020, and Feb 26, 2021, 466 adults were eligible (144 test-positive and 322 test-negative). 18 (13%) of 135 people with SARS-CoV-2 infection and 90 (34%) of 269 controls received one dose of BNT162b2. The adjusted vaccine effectiveness was 71·4% (95% CI 46·5–90·6). Nine (25%) of 36 people with COVID-19 infection and 53 (59%) of 90 controls received one dose of ChAdOx1 nCoV-19. The adjusted vaccine effectiveness was 80·4% (95% CI 36·4–94·5). When BNT162b2 effectiveness analysis was restricted to the period covered by ChAdOx1 nCoV-19, the estimate was 79·3% (95% CI 47·0–92·5).Interpretation:One dose of either BNT162b2 or ChAdOx1 nCoV-19 resulted in substantial risk reductions of COVID-19-related hospitalisation in people aged at least 80 years.Funding:Pfizer.

ObjectivesTo establish whether there is any change in mortality associated with infection of a new variant of SARS-CoV-2, designated a Variant of Concern in December 2020 (VOC-202012/1) compared to that associated with infection with circulating SARS-CoV-2 variants.DesignMatched cohort study. Cases are matched by age, gender, ethnicity, index of multiple deprivation, lower tier local authority region, and sample date of positive specimen, and differing only by detectability of the spike protein gene using the TaqPath assay - a proxy measure of VOC-202012/1 infection.SettingUnited Kingdom, community - based (Pillar 2) COVID-19 testing centres using the TaqPath assay.Participants54,906 pairs of participants testing positive for SARS-CoV-2 in Pillar 2 between 1st October 2020 and 29th January 2021, followed up until the 12th February 2021.Main outcome measuresDeath within 28 days of first positive SARS-CoV-2 test.ResultsThere is a high probability that the risk of mortality is increased by infection with VOC-202012/01 (p ConclusionsIf this finding is generalisable to other populations, VOC-202012/1 infections have the potential to cause substantial additional mortality compared to previously circulating variants. Healthcare capacity planning, national and international control policies are all impacted by this finding, with increased mortality lending weight to the argument that further coordinated and stringent measures are justified to reduce deaths from SARS-CoV-2.

IntroductionUK universities re-opened in September 2020, despite the on-going coronavirus epidemic. During the first term, various national social distancing measures were introduced, including banning groups of >6 people and the second lockdown in November. COVID-19 can spread rapidly in university-settings, and students’ adherence to social distancing measures is critical for controlling transmission.MethodsWe measured university staff and student contact patterns via an online, longitudinal survey capturing self-reported contacts on the previous day. We investigated the change in contacts associated with COVID-19 guidance periods: post-first lockdown (23/06/2020-03/07/2020), relaxed guidance period (04/07/2020-13/09/2020), “rule-of-six” period (14/09/2020-04/11/2020), and the second lockdown (05/11/2020-25/11/2020).Results722 staff (4199 responses) (mean household size: 2.6) and 738 students (1906 responses) (mean household size: 4.5) were included in the study. Contact number decreased with age. Staff in single-person households reported fewer contacts than individuals in 2-and 3-person households, and individuals in 4-and 5-person households reported more contacts.For staff, daily contacts were higher in the relaxed guidance and “rule-of-six” periods (means: 3.2 and 3.5, respectively; medians: 3) than the post-first lockdown and second lockdown periods (means: 4.5 and 5.4, respectively; medians: 2). Few students responded until 05/10/2020, after which the median student contacts was 2 and the mean was 5.7, until the second lockdown when it dropped to 3.1.DiscussionUniversity staff and students responded to national guidance by altering their social contacts. The response in staff and students was similar, suggesting that students are able to adhere to social distancing guidance while at university.

ObjectivesTo develop a regional model of COVID-19 dynamics, for use in estimating the number of infections, deaths and required acute and intensive care (IC) beds using the South West of England (SW) as an example case.DesignOpen-source age-structured variant of a susceptible-exposed-infectious-recovered (SEIR) deterministic compartmental mathematical model. Latin hypercube sampling and maximum likelihood estimation were used to calibrate to cumulative cases and cumulative deaths.SettingSW at a time considered early in the pandemic, where National Health Service (NHS) authorities required evidence to guide localised planning and support decision-making.ParticipantsPublicly-available data on COVID-19 patients.Primary and secondary outcome measuresThe expected numbers of infected cases, deaths due to COVID-19 infection, patient occupancy of acute and IC beds and the reproduction (“R”) number over time.ResultsSW model projections indicate that, as of the 11th May 2020 (when ‘lockdown’ measures were eased), 5,793 (95% credible interval, CrI, 2,003 – 12,051) individuals were still infectious (0.10% of the total SW England population, 95%CrI 0.04 – 0.22%), and a total of 189,048 (95%CrI 141,580 – 277,955) had been infected with the virus (either asymptomatically or symptomatically), but recovered, which is 3.4% (95%CrI 2.5 – 5.0%) of the SW population. The total number of patients in acute and IC beds in the SW on the 11th May 2020 was predicted to be 701 (95%CrI 169 – 1,543) and 110 (95%CrI 8 – 464) respectively. The R value in SW England was predicted to be 2.6 (95%CrI 2.0 – 3.2) prior to any interventions, with social distancing reducing this to 2.3 (95%CrI 1.8 – 2.9) and lockdown/ school closures further reducing the R value to 0.6 (95CrI% 0.5 – 0.7).ConclusionsThe developed model has proved a valuable asset for local and regional healthcare services. The model will be used further in the SW as the pandemic evolves, and – as open source software – is portable to healthcare systems in other geographies.Future work/ applicationsOpen-source modelling tool available for wider use and re-use.Customisable to a number of granularities such as at the local, regional and national level.Supports a more holistic understanding of intervention efficacy through estimating unobservable quantities, e.g. asymptomatic population.While not presented here, future use of the model could evaluate the effect of various interventions on transmission of COVID-19.Further developments could consider the impact of bedded capacity in terms of resulting excess deaths.

Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c. 5280319., The support of the Royal Society education policy unit and the RAMP initiative for COVID-19 are acknowledged. Funding to L.D. and E.B.-P. from MRC grants MR/V038613/1 and MC-PC-19067 is acknowledged., Personal contacts drive COVID-19 infections. After being closed (23 March 2020) UK primary schools partially re-opened on 1 June 2020 with social distancing and new risk mitigation strategies. We conducted a structured expert elicitation of teachers to quantify primary school contact patterns and how contact rates changed upon re-opening with risk mitigation measures in place. These rates, with uncertainties, were determined using a performance-based algorithm. We report mean number of contacts per day for four cohorts within schools, with associated 90% confidence ranges. Prior to lockdown, younger children (Reception and Year 1) made 15 contacts per day [range 8.35] within school, older children (Year 6) 18 contacts [range 5.55], teaching staff 25 contacts [range 4.55] and non-classroom staff 11 contacts [range 2.27]. After re-opening, the mean number of contacts was reduced by 53% for young children, 62% for older children, 60% for classroom staff and 64% for other staff. Contacts between teaching and non-teaching staff reduced by 80%. The distributions of contacts per person are asymmetric with heavy tail reflecting a few individuals with high contact numbers. Questions on risk mitigation and supplementary structured interviews elucidated how new measures reduced daily contacts in-school and contribute to infection risk reduction., Royal Society education policy unit, RAMP initiative for COVID-19, UK Research & Innovation (UKRI) Medical Research Council UK (MRC) MR/V038613/1 MC-PC-19067

As the number of cases of COVID-19 continues to grow, local health services are at risk of being overwhelmed with patients requiring intensive care. We develop and implement an algorithm to provide optimal re-routing strategies to either transfer patients requiring Intensive Care Units (ICU) or ventilators, constrained by feasibility of transfer. We validate our approach with realistic data from the United Kingdom and Spain. In the UK, we consider the National Health Service at the level of trusts and define a 4-regular geometric graph which indicates the four nearest neighbours of any given trust. In Spain we coarse-grain the healthcare system at the level of autonomous communities, and extract similar contact networks. Through random search optimisation we identify the best load sharing strategy, where the cost function to minimise is based on the total number of ICU units above capacity. Our framework is general and flexible allowing for additional criteria, alternative cost functions, and can be extended to other resources beyond ICU units or ventilators. Assuming a uniform ICU demand, we show that it is possible to enable access to ICU for up to 1000 additional cases in the UK in a single step of the algorithm. Under a more realistic and heterogeneous demand, our method is able to balance about 600 beds per step in the Spanish system only using local sharing, and over 1300 using countrywide sharing, potentially saving a large percentage of these lives that would otherwise not have access to ICU.

This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK'., The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reproduction number has become an essential parameter for monitoring disease transmission across settings and guiding interventions. The UK published weekly estimates of the reproduction number in the UK starting in May 2020 which are formed from multiple independent estimates. In this paper, we describe methods used to estimate the time-varying SARS-CoV-2 reproduction number for the UK. We used multiple data sources and estimated a serial interval distribution from published studies. We describe regional variability and how estimates evolved during the early phases of the outbreak, until the relaxing of social distancing measures began to be introduced in early July. Our analysis is able to guide localized control and provides a longitudinal example of applying these methods over long timescales., Support for R.C. and K.T.A.'s research is provided by the EPSRC via grant no. EP/N014391/1; R.C. is also funded by TSFT as part of the NHS Global Digital Exemplar programme (GDE); there were no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years, no other relationships or activities that could appear to have influenced the submitted work. L.D. and K.T.A. gratefully acknowledge the financial support of The Alan Turing Institute under the EPSRC grant no. EP/N510129/1. L.L. acknowledges the financial support of the EPSRC via Early Career Fellowship EP/P01660X/1. L.D. and E.B.P. are supported by Medical Research Council (MRC) (MC/PC/19067). E.B.P. was partly supported by the NIHR Health Protection Research Unit in Behavioural Science and Evaluation at University of Bristol, in partnership with PHE.

Background: On 8th December 2020, deployment of the first vaccine against SARS-CoV-2 authorised for UK use, the mRNA-based vaccine BNT162b2, began, followed by the adenoviral vector vaccine ChAdOx1nCoV-19 on 4th January 2021. Initially care home-residents and staff, frontline healthcare workers and adults from age 80 were targeted. In phase 3 trials, BNT162b2 and ChAdOx1nCoV-19 demonstrated 95% and 70% efficacy, respectively, after two doses against symptomatic SARS-CoV-2 infection. However, few data exist regarding the effectiveness of these vaccines in elderly frail people. Evaluation following implementation to determine the effectiveness of one dose in reducing hospitalisations due to SARS-CoV-2 infection in elderly adults is urgent. Methods: A prospective single-centre test-negative design case-control study of adults aged ≥80 years hospitalised with COVID-19 disease or other acute respiratory disease. We conducted logistic regression controlling for time (week), gender, index of multiple deprivations (IMD), and care residency status (CRS), and sensitivity analyses matched for time and gender using a conditional logistic model adjusting for IMD and CRS. Findings: First dose vaccine effectiveness of BNT162b2 was 71.4% (95% confidence interval [CI] 46.5-90.6). ChAdOx1nCoV-19 first dose vaccine effectiveness was 80.4% (95% CI 36.4-94.5). When effectiveness analysis for BNT162b2 was restricted to the period covered by ChAdOx1nCoV-19, the estimate was 79.3% (95% CI 47.0-92.5). Interpretation: A single dose of either BNT162b2 or ChAdOx1nCoV-19 vaccine resulted in substantial reductions in the risk of COVID-19-related hospitalisation in elderly, frail patients with extensive co-morbid disease. Funding: The AvonCAP study is an investigator-led project funded under a collaborative agreement by Pfizer. Conflict of Interest: CH is Principal Investigator of the Avon CAP study which is an investigator-led University of Bristol study funded by Pfizer and has previously received support from the NIHR in an Academic Clinical Fellowship. JO is a Co-Investigator on the Avon CAP Study. AF is a member of the Joint Committee on Vaccination and Immunization (JCVI) and chair of the World Health Organization European Technical Advisory Group of Experts on Immunization (ETAGE) committee. In addition to receiving funding from Pfizer as Chief Investigator of this study, he leads another project investigating transmission of respiratory bacteria in families jointly funded by Pfizer and the Gates Foundation. The other authors have no relevant conflicts of interest to declare. Ethical Approval: The study was approved by the Health Research Authority Research Ethics Committee (East of England, Essex), REC 20/EE/0157, including data collection under Section 251 of the 2006 NHS Act authorised by the Confidentiality Advisory Group.

CONQUEST (COroNavirus QUESTionnaire) is an online survey of contacts, behaviour, and COVID-19 symptoms for University of Bristol (UoB) staff/students. We analysed survey results from the start of the 2020/2021 academic year, prior to the second national lockdown (14/09/2020-01/11/2020), where COVID-19 outbreaks led to lockdown of some student halls of residence. The aim of these analyses was to enhance knowledge of student contact patterns to inform infection disease mathematical modelling approaches.Responses captured information on demographics, contacts on the previous day, symptoms and self-isolation during the prior week, and COVID-19 status.740 students provided 1261 unique records. Of 42 (3%) students testing positive in the prior fortnight, 99% had been self-isolating. The median number of contacts on the previous day was 2 (interquartile range: 1-5), mode: 1, mean: 6.1; 8% had ≥20 contacts. 57% of student contacts were other UoB students/staff.Most students reported few daily contacts but there was heterogeneity, and some reported many. Around 40% of student contacts were with individuals not affiliated with UoB, indicating potential for transmission to non-students/staff.

Importance: A 4-component meningococcus group B vaccine (4CMenB) is the only vaccine in use to prevent group B invasive meningococcal disease in young children, but no matched controlled studies have evaluated it. Objective: To determine the association between receipt of 4CMenB and invasive group B meningococcal disease. Design, setting, and participants: Matched incidence density case-control study. Patients presenting from October 2014 to March 2019 were ascertained, with follow-up until death or discharge (last follow-up in June 2019) in 31 pediatric services in Portugal. Children and adolescent residents in Portugal with laboratory-confirmed invasive meningococcal disease were included. Controls, usually 2 per case, with unrelated conditions who were at the same hospital at the same time were matched for sex, age, and residence. Exposures: Immunization with 4CMenB, ascertained from the national database (2-4 doses are recommended, depending on age). Main outcomes and measures: The primary outcome was group B invasive meningococcal disease in fully vaccinated cases compared with controls. The secondary outcomes were all serogroup invasive meningococcal disease in fully vaccinated cases compared with controls and group B and all serogroup invasive meningococcal disease in cases compared with controls who received at least 1 vaccine dose. Results: Of 117 patients with invasive meningococcal disease, 98 were eligible for inclusion and 82 had group B invasive meningococcal disease; 69 were old enough to have been fully vaccinated and considered protected. Among these 69 cases, the median (interquartile range) age was 24 (4.5-196) months, 42 were male, and the median (interquartile range) duration of hospitalization was 8 (0-86) days. Five of 69 cases (7.2%) and 33 of 142 controls (23.1%) were fully vaccinated (difference, -16.0% [95% CI, -26.3% to -5.7%]; odds ratio [OR], 0.21 [95% CI, 0.08-0.55]). For all serogroup invasive meningococcal disease, 6 of 85 cases (7.1%) and 39 of 175 controls (22.3%) were fully vaccinated (difference, -15.2% [95% CI, -24.3% to -6.1%]; OR, 0.22 [95% CI, 0.09-0.53]). For group B disease, 8 of 82 cases (9.8%) and 50 of 168 controls (29.8%) received at least 1 vaccine dose (difference, -20.0% [95% CI, -30.3% to -9.7%]; OR, 0.18 [95% CI, 0.08-0.44]) and for all serogroup invasive meningococcal disease, 11 of 98 cases (11.2%) and 61 of 201 controls (30.3%) received at least 1 vaccine dose (difference, -19.1% [95% CI, -28.8% to -9.5%]; OR, 0.23 [95% CI, 0.11-0.49]). Conclusions and relevance: During the first 5 years of vaccine availability in Portugal, vaccination with 4CMenB was less likely among children who developed invasive meningococcal disease compared with matched controls without invasive meningococcal disease. These findings may help inform the use of the 4CMenB vaccine in clinical practice. Trial registration: ISRCTN Identifier: ISRCTN10901628. info:eu-repo/semantics/publishedVersion

The serial interval of an infectious disease, commonly interpreted as the time between onset of symptoms in sequentially infected individuals within a chain of transmission, is a key epidemiological quantity involved in estimating the reproduction number. The serial interval is closely related to other key quantities, including the incubation period, the generation interval (the time between sequential infections) and time delays between infection and the observations associated with monitoring an outbreak such as confirmed cases, hospital admissions and deaths. Estimates of these quantities are often based on small data sets from early contact tracing and are subject to considerable uncertainty, which is especially true for early COVID-19 data. In this paper we estimate these key quantities in the context of COVID-19 for the UK, including a meta-analysis of early estimates of the serial interval. We estimate distributions for the serial interval with a mean 5.6 (95% CrI 5.1–6.2) and SD 4.2 (95% CrI 3.9–4.6) days (empirical distribution), the generation interval with a mean 4.8 (95% CrI 4.3–5.41) and SD 1.7 (95% CrI 1.0–2.6) days (fitted gamma distribution), and the incubation period with a mean 5.5 (95% CrI 5.1–5.8) and SD 4.9 (95% CrI 4.5–5.3) days (fitted log normal distribution). We quantify the impact of the uncertainty surrounding the serial interval, generation interval, incubation period and time delays, on the subsequent estimation of the reproduction number, when pragmatic and more formal approaches are taken. These estimates place empirical bounds on the estimates of most relevant model parameters and are expected to contribute to modelling COVID-19 transmission.

BackgroundIn 2020, the UK enacted an intensive, nationwide lockdown on March 23 to mitigate transmission of COVID-19. As restrictions began to ease, resurgences in transmission were targeted by geographically-limited interventions of various stringencies. Understanding the spatial scale of networks of human interaction, and how these networks change over time, is critical to inform interventions targeted at the most at-risk areas without unnecessarily restricting areas at low risk of resurgence.MethodsWe use detailed human mobility data aggregated from Facebook users to determine how the spatially-explicit network of movements changed before and during the lockdown period, in response to the easing of restrictions, and to the introduction of locally-targeted interventions. We also apply community detection techniques to the weighted, directed network of movements to identify geographically-explicit movement communities and measure the evolution of these community structures through time.FindingsWe found that the mobility network became more sparse and the number of mobility communities decreased under the national lockdown, a change that disproportionately affected long distance journeys central to the mobility network. We also found that the community structure of areas in which locally-targeted interventions were implemented following epidemic resurgence did not show reorganization of community structure but did show small decreases in indicators of travel outside of local areas.InterpretationWe propose that communities detected using Facebook or other mobility data be used to assess the impact of spatially-targeted restrictions and may inform policymakers about the spatial extent of human movement patterns in the UK. These data are available in near real-time, allowing quantification of changes in the distribution of the population across the UK, as well as changes in travel patterns to inform our understanding of the impact of geographically-targeted interventions.Putting Research Into ContextEvidence before this studyLarge-scale intensive interventions in response to the COVID-19 pandemic have been implemented globally, significantly affecting human movement patterns. Mobility data show spatially-explicit network structure, but it is not clear how that structure changed in response to national or locally-targeted interventions.Added value of this studyWe used daily mobility data aggregated from Facebook users to quantify changes in the travel network in the UK during the national lockdown, and in response to local interventions. We identified changes in human behaviour in response to interventions and identified the community structure inherent in these networks. This approach to understanding changes in the travel network can help quantify the extent of strongly connected communities of interaction and their relationship to the extent of spatially-explicit interventions.Implications of all the available evidenceWe show that spatial mobility data available in near real-time can give information on connectivity that can be used to understand the impact of geographically-targeted interventions and in the future, to inform spatially-targeted intervention strategies.Data SharingData used in this study are available from the Facebook Data for Good Partner Program by application. Code and supplementary information for this paper are available online (https://github.com/hamishgibbs/facebook_mobility_uk), alongside publication.

Controlling COVID-19 transmission in universities poses challenges due to the complex social networks and potential for asymptomatic spread. We developed a stochastic transmission model based on realistic mixing patterns and evaluated alternative mitigation strategies. We predict, for plausible model parameters, that if asymptomatic cases are half as infectious as symptomatic cases, then 15% (98% Prediction Interval: 6–35%) of students could be infected during the first term without additional control measures. First year students are the main drivers of transmission with the highest infection rates, largely due to communal residences. In isolation, reducing face-to-face teaching is the most effective intervention considered, however layering multiple interventions could reduce infection rates by 75%. Fortnightly or more frequent mass testing is required to impact transmission and was not the most effective option considered. Our findings suggest that additional outbreak control measures should be considered for university settings., Reopening of universities to students following COVID-19 restrictions risks increased transmission due to high numbers of social contacts and the potential for asymptomatic transmission. Here, the authors use a mathematical model with social contact data to estimate the impacts of reopening a typical non-campus based university in the UK.

Managing COVID-19 within a university setting presents unique challenges. At the start of term, students arrive from geographically diverse locations and potentially have higher numbers of social contacts than the general population, particularly if living in university halls of residence accommodation. Mathematical models are useful tools for understanding the potential spread of infection and are being actively used to inform policy about the management of COVID-19. Our aim was to provide a rapid review and appraisal of the literature on mathematical models investigating COVID-19 infection in a university setting. We searched PubMed, Web of Science, bioRxiv/ medRxiv and sought expert input via social media to identify relevant papers. BioRxiv/ medRxiv and PubMed/Web of Science searches took place on 3 and 6 July 2020, respectively. Papers were restricted to English language. Screening of peer-reviewed and pre-print papers and contact with experts yielded five relevant papers – all of which were pre-prints. All models suggest a significant potential for transmission of COVID-19 in universities. Testing of symptomatic persons and screening of the university community regardless of symptoms, combined with isolation of infected individuals and effective contact tracing were critical for infection control in the absence of other mitigation interventions. When other mitigation interventions were considered (such as moving teaching online, social/physical distancing, and the use of face coverings) the additional value of screening for infection control was limited. Multiple interventions will be needed to control infection spread within the university setting and the interaction with the wider community is an important consideration. Isolation of identified cases and quarantine of contacts is likely to lead to large numbers of students requiring educational, psychological and behavioural support and will likely have a large impact on the attendance of students (and staff), necessitating online options for teaching, even where in-person classes are taking place. Models were highly sensitive to assumptions in the parameters, including the number and type of individuals’ contacts, number of contacts traced, frequency of screening and delays in testing. Future models could aid policy decisions by considering the incremental benefit of multiple interventions and using empirical data on mixing within the university community and with the wider community where available. Universities will need to be able to adapt quickly to the evolving situation locally to support the health and wellbeing of the university and wider communities.

Tuberculosis (TB) remains a public health threat in low TB incidence countries, through a combination of reactivated disease and onward transmission. Using surveillance data from the United Kingdom (UK) and the Netherlands (NL), we demonstrate a simple and predictable relationship between the probability of observing a cluster and its size (the number of cases with a single genotype). We demonstrate that the full range of observed cluster sizes can be described using a modified branching process model with the individual reproduction number following a Poisson lognormal distribution. We estimate that, on average, between 2010 and 2015, a TB case generated 0.41 (95% CrI 0.30,0.60) secondary cases in the UK, and 0.24 (0.14,0.48) secondary cases in the NL. A majority of cases did not generate any secondary cases. Recent transmission accounted for 39% (26%,60%) of UK cases and 23%(13%,37%) of NL cases. We predict that reducing UK transmission rates to those observed in the NL would result in 538(266,818) fewer cases annually in the UK. In conclusion, while TB in low incidence countries is strongly associated with reactivated infections, we demonstrate that recent transmission remains sufficient to warrant policies aimed at limiting local TB spread., Author summary Multiple tuberculosis (TB) cases infected with a single strain are known as a TB cluster. In the United Kingdom (UK) for example, TB clusters vary in size from two cases up to over 200 cases. Previous work on cluster sizes demonstrated that highly infectious individuals influence cluster size, but the analysis did not include the largest clusters. Here, we show that the chance of observing a cluster of a given size follows the same pattern in the UK and the NL. Using a new mathematical description of how clusters are formed, we are able to predict the chance of observing the full range of cluster sizes. Using the model, we estimate how many cases are due to recent transmission and how many other cases each case generates. Although we estimate that a minority of cases (39% (26%,60%) in the UK) are due to recent in-country transmission, we find that reducing the onward transmission in the UK to levels in the NL would result in 538 (266,818) fewer cases annually in the UK.

We describe regional variation in the reproduction number of SARS-CoV-2 infections observed using publicly reported data in the UK, with a view to understanding both if there are clear hot spots in viral spread in the country, or if there are any clear spatial patterns. We estimate that the viral replication number remains slightly above 1 overall but that its trend is to decrease, based on case data up to the 8 April. This suggests the peak of the first wave of COVID-19 patients is imminent. We find that there is significant regional variation in different regions of the UK and that this is changing over time. Within England currently the reproductive ratio is lowest in the Midlands (1.11 95% CI 1.07; 1.14), and highest in the North East of England (1.38 95% CI 1.33-1.42). It remains unclear whether the overall reduction in the reproductive number is a result of social distancing measures, due to the long and variable time delays between infection and positive detection of cases. As we move forwards, if we are to prevent further outbreaks, it is critical that we can both reduce the time taken for detection and improve our ability to predict the regional spread of outbreaks