How effective is the BNT162b2 mRNA vaccine against SARS-CoV-2 transmission and infection? A national programme analysis in Monaco, July 2021 to September 2022.

BACKGROUND: We quantified SARS-CoV-2 dynamics in different community settings and the direct and indirect effect of the BNT162b2 mRNA vaccine in Monaco for different variants of concern (VOC). METHODS: Between July 2021 and September 2022, we prospectively investigated 20,443 contacts from 6320 index cases using data from the Monaco COVID-19 Public Health Programme. We calculated secondary attack rates (SARs) in households (n = 13,877), schools (n = 2508) and occupational (n = 6499) settings. We used binomial regression with a complementary log-log link function to measure adjusted hazard ratios (aHR) and vaccine effectiveness (aVE) for index cases to infect contacts and contacts to be infected in households. RESULTS: In households, the SAR was 55% (95% CI 54-57) and 50% (48-51) among unvaccinated and vaccinated contacts, respectively. The SAR was 32% (28-36) and 12% (10-13) in workplaces, and 7% (6-9) and 6% (3-10) in schools, among unvaccinated and vaccinated contacts respectively. In household, the aHR was lower in contacts than in index cases (aHR 0.68 [0.55-0.83] and 0.93 [0.74-1.1] for delta; aHR 0.73 [0.66-0.81] and 0.89 [0.80-0.99] for omicron BA.1&2, respectively). Vaccination had no significant effect on either direct or indirect aVE for omicron BA.4&5. The direct aVE in contacts was 32% (17, 45) and 27% (19, 34), and for index cases the indirect aVE was 7% (- 17, 26) and 11% (1, 20) for delta and omicron BA.1&2, respectively. The greatest aVE was in contacts with a previous SARS-CoV-2 infection and a single vaccine dose during the omicron BA.1&2 period (45% [27, 59]), while the lowest were found in contacts with either three vaccine doses (aVE - 24% [- 63, 6]) or one single dose and a previous SARS-CoV-2 infection (aVE - 36% [- 198, 38]) during the omicron BA.4&5 period. CONCLUSIONS: Protection conferred by the BNT162b2 mRNA vaccine against transmission and infection was low for delta and omicron BA.1&2, regardless of the number of vaccine doses and previous SARS-CoV-2 infection. There was no significant vaccine effect for omicron BA.4&5. Health authorities carrying out vaccination campaigns should bear in mind that the current generation of COVID-19 vaccines may not represent an effective tool in protecting individuals from either transmitting or acquiring SARS-CoV-2 infection.

Identifying employee, workplace and population characteristics associated with COVID-19 outbreaks in the workplace: a population-based study.

OBJECTIVES: To identify risk factors that contribute to outbreaks of COVID-19 in the workplace and quantify their effect on outbreak risk. METHODS: We identified outbreaks of COVID-19 cases in the workplace and investigated the characteristics of the individuals, the workplaces, the areas they work and the mode of commute to work, through data linkages based on Middle Layer Super Output Areas in England between 20 June 2021 and 20 February 2022. We estimated population-level associations between potential risk factors and workplace outbreaks, adjusting for plausible confounders identified using a directed acyclic graph. RESULTS: For most industries, increased physical proximity in the workplace was associated with increased risk of COVID-19 outbreaks, while increased vaccination was associated with reduced risk. Employee demographic risk factors varied across industry, but for the majority of industries, a higher proportion of black/African/Caribbean ethnicities and living in deprived areas, was associated with increased outbreak risk. A higher proportion of employees in the 60-64 age group was associated with reduced outbreak risk. There were significant associations between gender, work commute modes and staff contract type with outbreak risk, but these were highly variable across industries. CONCLUSIONS: This study has used novel national data linkages to identify potential risk factors of workplace COVID-19 outbreaks, including possible protective effects of vaccination and increased physical distance at work. The same methodological approach can be applied to wider occupational and environmental health research.

Deterministic epidemic models overestimate the basic reproduction number of observed outbreaks.

The basic reproduction number, R0, is a well-known quantifier of epidemic spread. However, a class of existing methods for estimating R0 from incidence data early in the epidemic can lead to an over-estimation of this quantity. In particular, when fitting deterministic models to estimate the rate of spread, we do not account for the stochastic nature of epidemics and that, given the same system, some outbreaks may lead to epidemics and some may not. Typically, an observed epidemic that we wish to control is a major outbreak. This amounts to implicit selection for major outbreaks which leads to the over-estimation problem. We formally characterised the split between major and minor outbreaks by using Otsu's method which provides us with a working definition. We show that by conditioning a 'deterministic' model on major outbreaks, we can more reliably estimate the basic reproduction number from an observed epidemic trajectory.

Understanding the infection severity and epidemiological characteristics of mpox in the UK.

In May 2022, individuals infected with the monkeypox virus were detected in the UK without clear travel links to endemic areas. Understanding the clinical characteristics and infection severity of mpox is necessary for effective public health policy. The study period of this paper, from the 1st June 2022 to 30th September 2022, included 3,375 individuals that tested positive for the monkeypox virus. The posterior mean times from infection to hospital admission and length of hospital stay were 14.89 days (95% Credible Intervals (CrI): 13.60, 16.32) and 7.07 days (95% CrI: 6.07, 8.23), respectively. We estimated the modelled Infection Hospitalisation Risk to be 4.13% (95% CrI: 3.04, 5.02), compared to the overall sample Case Hospitalisation Risk (CHR) of 5.10% (95% CrI: 4.38, 5.86). The overall sample CHR was estimated to be 17.86% (95% CrI: 6.06, 33.11) for females and 4.99% (95% CrI: 4.27, 5.75) for males. A notable difference was observed between the CHRs that were estimated for each sex, which may be indicative of increased infection severity in females or a considerably lower infection ascertainment rate. It was estimated that 74.65% (95% CrI: 55.78, 86.85) of infections with the monkeypox virus in the UK were captured over the outbreak.

The real-time infection hospitalisation and fatality risk across the COVID-19 pandemic in England.

The COVID-19 pandemic led to 231,841 deaths and 940,243 hospitalisations in England, by the end of March 2023. This paper calculates the real-time infection hospitalisation risk (IHR) and infection fatality risk (IFR) using the Office for National Statistics Coronavirus Infection Survey (ONS CIS) and the Real-time Assessment of Community Transmission Survey between November 2020 to March 2023. The IHR and the IFR in England peaked in January 2021 at 3.39% (95% Credible Intervals (CrI): 2.79, 3.97) and 0.97% (95% CrI: 0.62, 1.36), respectively. After this time, there was a rapid decline in the severity from infection, with the lowest estimated IHR of 0.32% (95% CrI: 0.27, 0.39) in December 2022 and IFR of 0.06% (95% CrI: 0.04, 0.08) in April 2022. We found infection severity to vary more markedly between regions early in the pandemic however, the absolute heterogeneity has since reduced. The risk from infection of SARS-CoV-2 has changed substantially throughout the COVID-19 pandemic with a decline of 86.03% (80.86, 89.35) and 89.67% (80.18, 93.93) in the IHR and IFR, respectively, since early 2021. From April 2022 until March 2023, the end of the ONS CIS study, we found fluctuating patterns in the severity of infection with the resumption of more normative mixing, resurgent epidemic waves, patterns of waning immunity, and emerging variants that have shown signs of convergent evolution.

Forecasting influenza hospital admissions within English sub-regions using hierarchical generalised additive models.

BACKGROUND: Seasonal influenza places a substantial burden annually on healthcare services. Policies during the COVID-19 pandemic limited the transmission of seasonal influenza, making the timing and magnitude of a potential resurgence difficult to ascertain and its impact important to forecast. METHODS: We have developed a hierarchical generalised additive model (GAM) for the short-term forecasting of hospital admissions with a positive test for the influenza virus sub-regionally across England. The model incorporates a multi-level structure of spatio-temporal splines, weekly cycles in admissions, and spatial correlation. Using multiple performance metrics including interval score, coverage, bias, and median absolute error, the predictive performance is evaluated for the 2022-2023 seasonal wave. Performance is measured against autoregressive integrated moving average (ARIMA) and Prophet time series models. RESULTS: Across the epidemic phases the hierarchical GAM shows improved performance, at all geographic scales relative to the ARIMA and Prophet models. Temporally, the hierarchical GAM has overall an improved performance at 7 and 14 day time horizons. The performance of the GAM is most sensitive to the flexibility of the smoothing function that measures the national epidemic trend. CONCLUSIONS: This study introduces an approach to short-term forecasting of hospital admissions for the influenza virus using hierarchical, spatial, and temporal components. The methodology was designed for the real time forecasting of epidemics. This modelling framework was used across the 2022-2023 winter for healthcare operational planning by the UK Health Security Agency and the National Health Service in England.

Seasonal influenza causes a burden for hospitals and therefore it is useful to be able to accurately predict how many patients might be admitted with the disease. We attempted to predict influenza admissions up to 14 days in the future by creating a computational model that incorporates how the disease is reported and how it spreads. We evaluated our optimised model on data acquired during the winter of 2022-2023 data in England and compared it with previously developed models. Our model was better at modelling how influenza spreads and predicting future hospital admissions than the models we compared it to. Improving how influenza admissions are forecast can enable hospitals to prepare better for increased admissions, enabling improved treatment and reduced death for all patients in hospital over winter.