Nowcasting cases and trends during the measles 2023/24 outbreak in England

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Abstract Background From 2023 to 2024, England had the largest measles outbreak in over a decade. Lags from suspected cases’ symptom onset to the availability of test results mean laboratory-confirmed case data are inherently retrospective rather than real-time. Reporting lags vary by measles prevalence and whether testing was for diagnostic or surveillance purposes. Nowcasting models predict future backfilling of reported cases and can estimate recent trends. Methods We developed a generalised additive model framework accounting for reporting delays, location, and day-of-week effects in line-list data by onset date. The model was re-fit weekly providing real-time nowcasts and directional trends for national and regional users. Retrospectively, we tested alternative specifications to optimise structure and confirm predictive performance, evaluating with log weighted interval score (WIS) and ranked probability score (RPS). Results For case count estimates, the operational and retrospective models outperformed the baseline model, with a lower average daily national log WIS by 42% and 41%, respectively, and similar regional improvements. For four-week trend direction, the operational and retrospective models provided better national estimates than the baseline with an average RPS lower by 69% and 6% respectively. Regionally, they also outperformed the baseline model in London, but the baseline model offered better performance for the smoother single-peak West Midlands epidemic. An alternative model indexed by report date instead sometimes outperformed the other nowcasting models for trend direction but also lagged changes in trend. Interpretation Our work highlights the utility for real-time nowcasting models during outbreaks to inform fast-evolving trends, and the need for early access of accurate reporting delay data to facilitate effective modelling.

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