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.

Through a computer monitor darkly: artificial intelligence in absorption, distribution, metabolism and excretion science.

Artificial Intelligence (AI) is poised or has already begun to influence highly absorption, distribution, metabolism and excretion (ADME) science. It is not in the area expected, that of superior modelling of ADME data to increase its predictive power. It is influencing traditional exhaustive and careful literature research by providing almost perfect summaries of existing information. This will highly influence how people study, graduate and progress in the ADME sciences. The literature contains many flaws, protein binding influence on unbound drug concentration, is one of the examples cited, and without direction AI may help to popularise them.ADME science has a relatively small number of key assays and values but these are produced under widely varying conditions so large data sets, the best substrate for artificial intelligence, are not readily available to produce new more predictive systems. The use of AI to enrich the data bases may be a near term goal.AI is already contributing in other areas such as technical skill assimilation, maintenance of complex instruments (combined with virtual reality) and the processing of pharmacovigilance.

Risk of paediatric multisystem inflammatory syndrome (PIMS-TS) during the SARS-CoV-2 alpha and delta variant waves: National observational and modelling study, 2020-21, England.

Objectives: Paediatric Multisystem Inflammatory Syndrome (PIMS-TS) is a rare life-threatening complication that typically occurs several weeks after SARS-CoV-2 infection in children and young people (CYP). We used national and regional-level data from the COVID-19 pandemic waves in England to develop a model to predict PIMS-TS cases. Methods: SARS-CoV-2 infections in CYP aged 0-15 years in England were estimated using the PHE-Cambridge real-time model. PIMS-TS cases were identified through the British Paediatric Surveillance Unit during (March-June 2020) and through Secondary Uses Services (SUS) from November 2020. A predictive model was developed to estimate PIMS-TS risk and lag times after SARS-CoV-2 infections. Results: During the Alpha wave, the model accurately predicted PIMS-TS cases (506 vs. 502 observed cases), with a median estimated risk of 0.038% (IQR, 0.037-0.041%) of paediatric SARS-CoV-2 infections. For the Delta wave, the median risk of PIMS-TS was significantly lower at 0.026% (IQR, 0.025-0.029%), with 212 observed PIMS-TS cases compared to 450 predicted by the model. Conclusions: The model accurately predicted national and regional PIMS-TS cases in CYP during the Alpha wave. PIMS-TS cases were 53% lower than predicted during the Delta wave. Further studies are needed to understand the mechanisms of the observed lower risk with the Delta variant.

Screening strategy for the rapid detection of in vitro generated glutathione conjugates using high-performance liquid chromatography and low-resolution mass spectrometry in combination with LightSight software for data processing.

The knowledge of drug metabolism in the early phases of the drug discovery process is vital for minimising compound failure at later stages. As chemically reactive metabolites may cause adverse drug reactions, it is generally accepted that avoiding formation of reactive metabolites increases the chances of success of a molecule. In order to generate this important information, a screening strategy for the rapid detection of in vitro generated reactive metabolites trapped by glutathione has been developed. The bioassay incorporated the use of native glutathione and its close analogue the glutathione ethyl ester. The generic conditions for detecting glutathione conjugates that undergo constant neutral loss of 129 Da were optimised using a glutathione-based test mix of four compounds. The final liquid chromatography/tandem mass spectrometry constant neutral loss method used low-resolution settings and a scanning window of 200 amu. Data mining was rapidly and efficiently performed using LightSight software. Unambiguous identification of the glutathione conjugates was significantly facilitated by the analytical characteristics of the conjugate pairs formed with glutathione and glutathione ethyl ester, i.e. by chromatographic retention time and mass differences. The reliability and robustness of the screening strategy was tested using a number of compounds known to form reactive metabolites. Overall, the developed screening strategy provided comprehensive and reliable identification of glutathione conjugates and is well suited for rapid routine detection of trapped reactive metabolites. This new approach allowed the identification of a previously unreported diclofenac glutathione conjugate.