Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number

The time-varying reproduction number () can change rapidly over the course of a pandemic due to changing restrictions, behaviours, and levels of population immunity. Many methods exist that allow the estimation of from case data. However, these are not easily adapted to point prevalence data nor can they infer across periods of missing data. We developed a Bayesian P-spline model suitable for fitting to a wide range of epidemic time-series, including point-prevalence data. We demonstrate the utility of the model by fitting to periodic daily SARS-CoV-2 swab-positivity data in England from the first 7 rounds (May 2020–December 2020) of the REal-time Assessment of Community Transmission-1 (REACT-1) study. Estimates of over the period of two subsequent rounds (6–8 weeks) and single rounds (2–3 weeks) inferred using the Bayesian P-spline model were broadly consistent with estimates from a simple exponential model, with overlapping credible intervals. However, there were sometimes substantial differences in point estimates. The Bayesian P-spline model was further able to infer changes in over shorter periods tracking a temporary increase above one during late-May 2020, a gradual increase in over the summer of 2020 as restrictions were eased, and a reduction in during England’s second national lockdown followed by an increase as the Alpha variant surged. The model is robust against both under-fitting and over-fitting and is able to interpolate between periods of available data; it is a particularly versatile model when growth rate can change over small timescales, as in the current SARS-CoV-2 pandemic. This work highlights the importance of pairing robust methods with representative samples to track pandemics

Bias of influenza vaccine effectiveness estimates from test-negative studies conducted during an influenza pandemic

Test-negative (TN) studies have become the most widely used study design for the estimation of influenza vaccine effectiveness (VE) and are easily incorporated into existing influenza surveillance networks. We seek to determine the bias of TN-based VE estimates during a pandemic using a dynamic probability model. The model is used to evaluate and compare the bias of VE estimates under various sources of bias when vaccination occurs after the beginning of an outbreak, such as during a pandemic. The model includes two covariates (health status and health awareness), which may affect the probabilities of vaccination, developing influenza and non-influenza acute respiratory illness (ARI), and seeking medical care. Specifically, we evaluate the bias of VE estimates when (1) vaccination affects the probability of developing a non-influenza ARI; (2) vaccination affects the probability of seeking medical care; (3) a covariate (e.g. health status) is related to both the probabilities of vaccination and developing an ARI; and (4) a covariate (e.g. health awareness) is related to both the probabilities of vaccination and of seeking medical care. We considered two outcomes against which the vaccine is supposed to protect: symptomatic influenza and medically-attended influenza. When vaccination begins during an outbreak, we found that the effect of delayed onset of vaccination is unpredictable. VE estimates from TN studies were biased regardless of the source of bias present. However, if the core assumption of the TN design is satisfied, that is, if vaccination does not affect the probability of non-influenza ARI, then TN-based VE estimates against medically-attended influenza will only suffer from small (<0.05) to moderate bias (≥0.05 and <0.10). These results suggest that if sources of bias listed above are ruled out, TN studies are a valid study design for the estimation of VE during a pandemic

A dynamic model for evaluation of the bias of influenza vaccine effectiveness estimates from observational studies

As influenza vaccination is now widely recommended in the United States, observational studies based on patients with acute respiratory illness (ARI) remain the only option to estimate influenza vaccine effectiveness (VE). We developed a dynamic probability model to evaluate bias of VE estimates from passive surveillance cohort, test-negative, and traditional case-control studies. The model includes two covariates (health status and health awareness), which may affect the probabilities of vaccination, developing ARI, and seeking medical care. Our results suggest that test-negative studies produce unbiased estimates of VE against medically-attended influenza when (1) vaccination does not affect the probability of non-influenza ARI and (2) health status has the same effect on the probability of influenza and non-influenza ARIs. The same estimate may be severely biased (i.e., estimated VE - true VE ≥ 0.20) for estimating VE against symptomatic influenza if the vaccine affects the probability of seeking care against influenza ARI. VE estimates from test-negative studies may also be severely biased for both outcomes of interest when vaccination affects the probability of non-influenza ARI, but estimates from passive surveillance cohort studies are unbiased in this case. Finally, VE estimates from traditional case-control studies suffer from bias regardless of the source of bias

A comparison of the test-negative and the traditional case-control study designs for estimation of influenza vaccine effectiveness under nonrandom vaccination

Background As annual influenza vaccination is recommended for all U.S. persons aged 6 months or older, it is unethical to conduct randomized clinical trials to estimate influenza vaccine effectiveness (VE). Observational studies are being increasingly used to estimate VE. We developed a probability model for comparing the bias and the precision of VE estimates from two case-control designs: the traditional case-control (TCC) design and the test-negative (TN) design. In both study designs, acute respiratory illness (ARI) patients seeking medical care testing positive for influenza infection are considered cases. In the TN design, ARI patients seeking medical care who test negative serve as controls, while in the TCC design, controls are randomly selected individuals from the community who did not contract an ARI. Methods Our model assigns each study participant a covariate corresponding to the person’s health status. The probabilities of vaccination and of contracting influenza and non-influenza ARI depend on health status. Hence, our model allows non-random vaccination and confounding. In addition, the probability of seeking care for ARI may depend on vaccination and health status. We consider two outcomes of interest: symptomatic influenza (SI) and medically-attended influenza (MAI). Results If vaccination does not affect the probability of non-influenza ARI, then VE estimates from TN studies usually have smaller bias than estimates from TCC studies. We also found that if vaccinated influenza ARI patients are less likely to seek medical care than unvaccinated patients because the vaccine reduces symptoms’ severity, then estimates of VE from both types of studies may be severely biased when the outcome of interest is SI. The bias is not present when the outcome of interest is MAI. Conclusions The TN design produces valid estimates of VE if (a) vaccination does not affect the probabilities of non-influenza ARI and of seeking care against influenza ARI, and (b) the confounding effects resulting from non-random vaccination are similar for influenza and non-influenza ARI. Since the bias of VE estimates depends on the outcome against which the vaccine is supposed to protect, it is important to specify the outcome of interest when evaluating the bias

High prevalence of SARS-CoV-2 swab positivity and increasing R number in England during October 2020: REACT-1 round 6 interim report

Background REACT-1 measures prevalence of SARS-CoV-2 infection in representative samples of the population in England using PCR testing from self-administered nose and throat swabs. Here we report interim results for round 6 of observations for swabs collected from the 16th to 25th October 2020 inclusive. Methods REACT-1 round 6 aims to collect data and swab results from 160,000 people aged 5 and above. Here we report results from the first 86,000 individuals. We estimate prevalence of PCR-confirmed SARS-CoV-2 infection, reproduction numbers (R) and temporal trends using exponential growth or decay models. Prevalence estimates are presented both unweighted and weighted to be representative of the population of England, accounting for response rate, region, deprivation and ethnicity. We compare these interim results with data from round 5, based on swabs collected from 18th September to 5th October 2020 inclusive. Results Overall prevalence of infection in the community in England was 1.28% or 128 people per 10,000, up from 60 per 10,000 in the previous round. Infections were doubling every 9.0 (6.1, 18) days with a national reproduction number (R) estimated at 1.56 (1.27, 1.88) compared to 1.16 (1.05, 1.27) in the previous round. Prevalence of infection was highest in Yorkshire and The Humber at 2.72% (2.12%, 3.50%), up from 0.84% (0.60%, 1.17%), and the North West at 2.27% (1.90%, 2.72%), up from 1.21% (1.01%, 1.46%), and lowest in South East at 0.55% (0.45%, 0.68%), up from 0.29% (0.23%, 0.37%). Clustering of cases was more prevalent in Lancashire, Manchester, Liverpool and West Yorkshire, West Midlands and East Midlands. Interim estimates of R were above 2 in the South East, East of England, London and South West, but with wide confidence intervals. Nationally, prevalence increased across all age groups with the greatest increase in those aged 55-64 at 1.20% (0.99%, 1.46%), up 3-fold from 0.37% (0.30%, 0.46%). In those aged over 65, prevalence was 0.81% (0.58%, 0.96%) up 2-fold from 0.35% (0.28%, 0.43%). Prevalence remained highest in 18 to 24-year olds at 2.25% (1.47%, 3.42%). Conclusion The co-occurrence of high prevalence and rapid growth means that the second wave of the epidemic in England has now reached a critical stage. Whether via regional or national measures, it is now time-critical to control the virus and turn R below one if further hospital admissions and deaths from COVID-19 are to be avoided

SARS-CoV-2 antibody prevalence in England following the first peak of the pandemic.

England has experienced a large outbreak of SARS-CoV-2, disproportionately affecting people from disadvantaged and ethnic minority communities. It is unclear how much of this excess is due to differences in exposure associated with structural inequalities. Here we report from the REal-time Assessment of Community Transmission-2 (REACT-2) national study of over 100,000 people. After adjusting for test characteristics and re-weighting to the population, overall antibody prevalence is 6.0% (95% CI: 5.8-6.1). An estimated 3.4 million people had developed antibodies to SARS-CoV-2 by mid-July 2020. Prevalence is two- to three-fold higher among health and care workers compared with non-essential workers, and in people of Black or South Asian than white ethnicity, while age- and sex-specific infection fatality ratios are similar across ethnicities. Our results indicate that higher hospitalisation and mortality from COVID-19 in minority ethnic groups may reflect higher rates of infection rather than differential experience of disease or care

Resurgence of SARS-CoV-2: detection by community viral surveillance

Surveillance of the SARS-CoV-2 epidemic has mainly relied on case reporting which is biased by health service performance, test availability and test-seeking behaviors. We report a community-wide national representative surveillance program in England involving self-administered swab results from 594,000 individuals tested for SARS-CoV-2, regardless of symptoms, from May to beginning of September 2020. The epidemic declined between May and July 2020 but then increased gradually from mid-August, accelerating into early September 2020 at the start of the second wave. When compared to cases detected through routine surveillance, we report here a longer period of decline and a younger age distribution. Representative community sampling for SARS-CoV-2 can substantially improve situational awareness and feed into the public health response even at low prevalence

High prevalence of SARS-CoV-2 swab positivity in England during September 2020: interim report of round 5 of REACT-1 study

Background REACT-1 is a community survey of PCR confirmed swab-positivity for SARS-CoV-2 among random samples of the population in England. This interim report includes data from the fifth round of data collection currently underway for swabs sampled from the 18th to 26th September 2020.Methods Repeated cross-sectional surveys of random samples of the population aged 5 years and over in England with sample size ranging from 120,000 to 160,000 people in each round of data collection. Collection of self-administered nose and throat swab for PCR and questionnaire data. Prevalence of swab-positivity by round and by demographic variables including age, sex, region, ethnicity. Estimation of reproduction number (R) between and within rounds, and time trends using exponential growth or decay model. Assessment of geographical clustering based on boundary-free spatial model.Results Over the 9 days for which data are available, we find 363 positives from 84,610 samples giving a weighted prevalence to date of 0.55% (0.47%, 0.64%) in round 5. This implies that 411,000 (351,000, 478,000) people in England are virus-positive under the assumption that the swab assay is 75% sensitive. Using data from the most recent two rounds, we estimate a doubling time of 10.6 (9.4, 12.0) days covering the period 20th August to 26th September, corresponding to a reproduction number R of 1.47 (1.40, 1.53). Using data only from round 5 we estimate a reproduction number of 1.06 (0.74, 1.46) with probability of 63% that R is greater than 1. Between rounds 4 and 5 there was a marked increase in unweighted prevalence at all ages. In the most recent data, prevalence was highest in the 18 to 24 yrs age group at 0.96% (0.68%, 1.36%). At 65+ yrs prevalence increased 7-fold between rounds 4 and 5 from 0.04% (0.03%, 0.07%) to 0.29% (0.23%, 0.37%). Prevalence increased in all regions between rounds 4 and 5, giving the highest unweighted prevalence in round 5 in the North West at 0.86% (0.69%, 1.06%). In London, prevalence increased 5-fold from 0.10% (0.06%, 0.17%) to 0.49% (0.36%, 0.68%). Regional R values ranged from 1.32 (1.16,1.50) in Yorkshire and the Humber to 1.63 (1.42, 1.88) in the East Midlands over the same period. In the most recent data, there was extensive clustering in the North West, Midlands and in and around London with pockets of clustering in other regions including the South West, North East and East of England. Odds of swab-positivity were 2-fold higher in people of Asian and Black ethnicity compared with white participants.Conclusion Rapid growth has led to high prevalence of SARS-CoV-2 virus in England among all regions and age groups, including those age groups at highest risk. Although there is evidence of a recent deceleration in the epidemic, current levels of prevalence will inevitably result in additional hospitalisations and mortality in coming weeks. A re-doubling of public health efforts is needed to return to a declining phase of the epidemic.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThe study was funded by the Department of Health and Social Care in England.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:Research ethics approval was obtained from the South Central-Berkshire B Research Ethics Committee (IRAS ID: 283787).All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe original datasets generated or analysed, or both, during this study are not publicly available because of governance restrictions and the identifiable nature of the data