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

Objectives: In 2023/24, England had its largest measles outbreak in a decade. Lags from symptom onset to test results made laboratory-confirmed case data inherently retrospective rather than real-time. Reporting lags varied by measles prevalence and testing purpose. Nowcasting models can predict future backfilling of reported cases and estimate recent trends.

Methods: We developed a generalised additive model accounting for reporting delays, location, and day-of-week effects in line-list data by symptom 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 national case estimates, the operational and retrospective models outperformed the baseline model, reducing daily log WIS by 42% and 41%, respectively. For four-week trends, the operational and retrospective models provided better national estimates than the baseline, reducing RPS by 69% and 6%, respectively. An alternative model indexed by report date sometimes outperformed others for trend direction but lagged trend changes.

Conclusions: Our work highlights the value of real-time nowcasting during outbreaks to inform fast-evolving trends, and early access to accurate reporting delay data for effective modelling.