Combining models to generate consensus medium-term projections of hospital admissions, occupancy and deaths relating to COVID-19 in England (preprint)

Mathematical modelling has played an important role in offering informed advice during the COVID-19 pandemic. In England, a cross government and academia collaboration generated Medium-Term Projections (MTPs) of possible epidemic trajectories over the future 4-6 weeks from a collection of epidemiological models.In this paper we outline this collaborative modelling approach and evaluate the accuracy of the combined and individual model projections against the data over the period November 2021-December 2022 when various Omicron subvariants were spreading across England. Using a number of statistical methods, we quantify the predictive performance of the model projections for both the combined and individual MTPs, by evaluating the point and probabilistic accuracy. Our results illustrate that the combined MTPs, produced from an ensemble of heterogeneous epidemiological models, were a closer fit to the data than the individual models during the periods of epidemic growth or decline, with the 90% confidence intervals widest around the epidemic peaks. We also show that the combined MTPs increase the robustness and reduce the biases associated with a single model projection. Learning from our experience of ensemble modelling during the COVID-19 epidemic, our findings highlight the importance of developing cross-institutional multi-model infectious disease hubs for future outbreak control.

SARS-CoV-2, influenza A/B and respiratory syncytial virus positivity and association with influenza-like illness and self-reported symptoms, over the 2022/23 winter season in the UK: a longitudinal surveillance cohort (preprint)

Background Syndromic surveillance often relies on patients presenting to healthcare. Community cohorts, although more challenging to recruit, could provide additional population-wide insights, particularly with SARS-CoV-2 co-circulating with other respiratory viruses. Methods We estimated positivity and incidence of SARS-CoV-2, influenza A/B, and RSV, and trends in self-reported symptoms including influenza-like illness (ILI), over the 2022/23 winter season in a broadly representative UK community cohort (COVID-19 Infection Survey), using negative-binomial generalised additive models. We estimated associations between test positivity and each of symptoms and influenza vaccination, using adjusted logistic and multinomial models. Findings Swabs taken at 32,937/1,352,979 (2.4%) assessments tested positive for SARS-CoV-2, 181/14,939 (1.2%) for RSV and 130/14,939 (0.9%) for influenza A/B, varying by age over time. Positivity and incidence peaks were earliest for RSV, then influenza A/B, then SARS-CoV-2, and were highest for RSV in the youngest and for SARS-CoV-2 in the oldest age-groups. Many test-positives did not report key symptoms: middle-aged participants were generally more symptomatic than older or younger participants, but still only ~25% reported ILI-WHO and ~60% ILI-ECDC. Most symptomatic participants did not test positive for any of the three viruses. Influenza A/B-positivity was lower in participants reporting influenza vaccination in the current and previous seasons (odds ratio=0.55 (95% CI 0.32,0.95)) versus neither season. Interpretation Symptom profiles varied little by aetiology, making distinguishing SARS-CoV-2, influenza and RSV using symptoms challenging. Most symptoms were not explained by these viruses, indicating the importance of other pathogens in syndromic surveillance. Influenza vaccination was associated with lower rates of community influenza test positivity. Funding UK Health Security Agency, Department of Health and Social Care, National Institute for Health Research.

Trends in COVID-19 hospital outcomes in England before and after vaccine introduction, a cohort study (preprint)

Widespread vaccination campaigns have changed the landscape for COVID-19, vastly altering symptoms and reducing morbidity and mortality. We estimate trends in mortality by month of admission and vaccination status among those hospitalised with COVID-19 in England between March 2020 to September 2021, controlling for demographic factors and hospital load. Among 259,727 hospitalised COVID-19 cases, 51,948 (20.0%) experienced mortality in hospital. Hospitalised fatality risk ranged from 40.3% (95% confidence interval 39.4-41.3%) in March 2020 to 8.1% (7.2-9.0%) in June 2021. Older individuals and those with multiple co-morbidities were more likely to die or else experienced longer stays prior to discharge. Compared to unvaccinated people, the hazard of hospitalised mortality was 0.71 (0.67-0.77) with a first vaccine dose, and 0.56 (0.52-0.61) with a second vaccine dose. Compared to hospital load at 0-20% of the busiest week, the hazard of hospitalised mortality during periods of peak load (90-100%), was 1.23 (1.12-1.34). The prognosis for people hospitalised with COVID-19 in England has varied substantially throughout the pandemic and according to case-mix, vaccination, and hospital load. Our estimates provide an indication for demands on hospital resources, and the relationship between hospital burden and outcomes.

Estimating the Risk of Paediatric Multisystem Inflammatory Syndrome (PIMS-TS) Following SARS-CoV-2 Infection in Children (preprint)

Background: Paediatric Multisystem Inflammatory Syndrome (PIMS-TS) is a rare life-threatening complication that occurs in some children two to four weeks after SARS-CoV-2 infection. Although the precise causal mechanisms underpinning the relationship between SARS-CoV-2 and PIMS-TS are unclear, several recent studies have confirmed a strong temporal association. This study provides further evidence in support of a causal and temporal link. A novel methodology is presented whereby PIMS-TS incidence parameters estimated from data published on SARS-CoV-2 in the first wave of the COVID-19 pandemic in England were used to make accurate projections of PIMS-TS cases in the second wave. Methods: Case classifications and data on PIMS-TS cases were obtained from the British Paediatric Surveillance Unit (BPSU) in an endeavour initiated by Public Heath England (PHE). The dataset contained all PIMS-TS cases presenting as symptomatic in England in the first wave of the pandemic. PIMS-TS incidence rates in children aged <15 years were estimated for the first wave and expressed as a fraction of SARS-CoV-2 cases. Data on SARS-CoV-2 cases were extracted from the PHE-Cambridge real-time model. Temporal analysis was performed to estimate the lag-time between peak SARS-CoV-2 incidence and peak PIMS-TS. The incidence and lag-time parameters estimated during the first wave were used to produce weekly projections of PIMS-TS cases in the second wave. These projections were then employed operationally in a clinical setting. Statistical analyses were performed to assess the accuracy of the forecasts once data on PIMS-TS cases were published by the BPSU approximately three months after the PIMS-TS forecasts were generated. Findings: Statistical analyses show that the PIMS-TS parameters estimated from the first wave produced accurate projections of PIMS-TS incidence in the second wave. Results at the aggregated national level showed there were no statistically significant differences observed between the PIMS-TS admission data and forecasts in England. Forecasts generated at the disaggregated regional level were also accurate, with no statistically significant differences observed between the PIMS-TS admissions data and forecasts in five of the nine Public Health England Centres (PHECs). However, a statistically significant divergence was observed between the PIMS-TS admissions data and the second wave forecasts in the regions of London and in the East, North West, and South West of England.Interpretation: This study provides further evidence in support of a causal and temporal association between SARS-CoV-2 and PIMS-TS, since data on SARS-CoV-2 incidence in the first wave of the COVID-19 pandemic in England have been shown to be a good baseline from which to generate forecasts of PIMS-TS incidence in the second wave, at both aggregated national and disaggregated regional levels.Funding Information: : Department of Health and Social Care (DHSC) Grant-in-aid funding to Public Health England (PHE).Declaration of Interests: None; this study did not receive any specific grant funding from external agencies in the public, commercial or not-for-profit sectors.Ethics Approval Statement: : PHE has legal permission under Regulation 3 of The Health Service (Control of Patient Information) Regulations 2002, to conduct national surveillance of communicable diseases in England and, as such, individual patient consent is not required. Public Health Wales, through the established order legislation, is required to conduct surveillance of communicable diseases in Wales and, as such, individual patient consent is not required. The surveillance protocol was approved by the Public Benefit and Privacy Panel for Health and Social Care in Scotland (Ref: 20210041, 19 May 2020).

Community prevalence of SARS-CoV-2 in England during April to September 2020: Results from the ONS Coronavirus Infection Survey (preprint)

Background: Decisions regarding the continued need for control measures to contain the spread of SARS-CoV-2 rely on accurate and up-to-date information about the number of people and risk factors for testing positive. Existing surveillance systems are not based on population samples and are generally not longitudinal in design. Methods: From 26 April to 19 September2020, 514,794 samples from 123,497 individuals were collected from individuals aged 2 years and over from a representative sample of private households from England. Participants completed a questionnaire and nose and throat swab were taken. The percentage of individuals testing positive for SARS-CoV-2 RNA was estimated over time using dynamic multilevel regression and post-stratification, to account for potential residual non-representativeness. Potential changes in risk factors for testing positive over time were also evaluated using multilevel regression models. Findings: Between 26 April and 19 September 2020, in total, results were available from 514,794 samples from 123,497 individuals, of which 489 were positive overall from 398 individuals. The percentage of people testing positive for SARS-CoV-2 changed substantially over time, with an initial decrease between end of April and June, followed by low levels during the summer, before marked increases end of August and September 2020. Having a patient-facing role and working outside your home were important risk factors for testing positive in the first period but not (yet) in the second period of increased positivity rates, and age (young adults) being an important driver of the second period of increased positivity rates. A substantial proportion of infections were in individuals not reporting symptoms (53%-70%, dependent on calendar time). Interpretation: Important risk factors for testing positive varied substantially between the initial and second periods of higher positivity rates, and a substantial proportion of infections were in individuals not reporting symptoms, indicating that continued monitoring for SARS-CoV-2 in the community will be important for managing the epidemic moving forwards.

Community prevalence of SARS-CoV-2 in England: Results from the ONS Coronavirus Infection Survey Pilot (preprint)

Objective: To estimate the percentage of individuals infected with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) over time in the community in England and to quantify risk factors. Design: Repeated cross-sectional surveys of population-representative households with longitudinal follow-up if consent given. Setting: England. Participants: 34,992 Individuals aged 2 years and over from 16,722 private residential households. Data were collected in a pilot phase of the survey between 26 April and 28 June 2020. Main outcome measures: Percentage of individuals in the community testing positive for SARS-CoV-2 RNA using throat and nose swabs. Individuals were asked about any symptoms and potential risk factors. Results: The percentage of people in private-residential households testing positive for SARS-CoV-2 reduced from 0.32% (95% credible interval (CrI) 0.19% to 0.52%) on 26 April to 0.08% (95% CrI 0.05% to 0.12%) on 28 June, although the prevalence stabilised near the end of the pilot. Factors associated with an increased risk of testing positive included having a job with direct patient contact (relative exposure (RE) 4.06, 95% CrI 2.42 to 6.77)), working outside the home (RE 2.49, 95% CrI 1.39 to 4.45), and having had contact with a hospital (RE 2.20, 95% CrI 1.09 to 4.16 for having been to a hospital individually and RE 1.95, 95% CrI 0.81 to 4.09 for a household member having been to a hospital). In 133 visits where individuals tested positive, 82 (61%, 95% CrI 53% to 69%) reported no symptoms, stably over time. Conclusion: The percentage of SARS-CoV-2 positive individuals declined between 26 April and 28 June 2020. Positive tests commonly occurred without symptoms being reported. Working outside your home was an important risk factor, indicating that continued monitoring for SARS-CoV-2 in the community will be essential for early detection of increases in infections following return to work and other relaxations of control measures.