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

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 article, 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.

The impact and cost-effectiveness of pneumococcal immunisation strategies for the elderly in England.

Pneumococcal disease, presenting as invasive pneumococcal disease (IPD) or community-acquired pneumonia (CAP) is an important cause of illness and hospitalisation in the elderly. To reduce pneumococcal burden, since 2003, 65-year-olds in England have been offered a 23-valent pneumococcal polysaccharide vaccine (PPV23). This study compares the impact and cost-effectiveness (CE) of vaccination with the existing PPV23 vaccine to the new 15-and 20-valent pneumococcal conjugate vaccines (PCV15 and PCV20), targeting adults aged 65 or 75 years old. We developed a static Markov model for immunisation against pneumococcal disease, capturing different vaccine effectiveness and immunity waning assumptions, projecting the number of IPD/CAP cases averted over the thirty years following vaccination. Using an economic model and probabilistic sensitivity analysis we evaluated the CE of the different immunisation strategies at current vaccine list prices and the willingness-to-pay at a median threshold of £20,000/QALY and an uncertainty threshold of 90% of simulations below £30,000/QALY. PCV20 averted more IPD and CAP cases than PCV15 or PPV23 over the thirty years following vaccination: 353(360), 145(159) and 150(174) IPD and 581(673), 259(485) and 212(235) CAP cases at a vaccination age of 65(75) under base vaccine effectiveness assumptions. At the listed prices of PCV20 and PPV23 vaccines as of May 2023, both vaccines were cost-effective when vaccinating 65- or 75-year-olds with an ICER threshold of £20,000 per QALY. To achieve the same cost-effectiveness as PPV23, the additional cost of PCV20 should be less than £44(£91) at an ICER threshold of £20,000/QALY (£30,000/QALY) if vaccination age is 65 (or £54(£103) if vaccination age is increased to 75). We showed that both PPV23 and PCV20 were likely to be cost-effective. PCV20 was likely to avert more cases of pneumococcal disease in elderly adults in England than the current PPV23 vaccine, given input assumptions of a higher vaccine effectiveness and slower waning for PCV20.

Inferring community transmission of SARS-CoV-2 in the United Kingdom using the ONS COVID-19 Infection Survey.

Key epidemiological parameters, including the effective reproduction number, R(t), and the instantaneous growth rate, r(t), generated from an ensemble of models, have been informing public health policy throughout the COVID-19 pandemic in the four nations of the United Kingdom of Great Britain and Northern Ireland (UK). However, estimation of these quantities became challenging with the scaling down of surveillance systems as part of the transition from the "emergency" to "endemic" phase of the pandemic. The Office for National Statistics (ONS) COVID-19 Infection Survey (CIS) provided an opportunity to continue estimating these parameters in the absence of other data streams. We used a penalised spline model fitted to the publicly-available ONS CIS test positivity estimates to produce a smoothed estimate of the prevalence of SARS-CoV-2 positivity over time. The resulting fitted curve was used to estimate the "ONS-based" R(t) and r(t) across the four nations of the UK. Estimates produced under this model are compared to government-published estimates with particular consideration given to the contribution that this single data stream can offer in the estimation of these parameters. Depending on the nation and parameter, we found that up to 77% of the variance in the government-published estimates can be explained by the ONS-based estimates, demonstrating the value of this singular data stream to track the epidemic in each of the four nations. We additionally find that the ONS-based estimates uncover epidemic trends earlier than the corresponding government-published estimates. Our work shows that the ONS CIS can be used to generate key COVID-19 epidemiological parameters across the four UK nations, further underlining the enormous value of such population-level studies of infection. This is not intended as an alternative to ensemble modelling, rather it is intended as a potential solution to the aforementioned challenge faced by public health officials in the UK in early 2022.