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

Area of Science:

• Epidemiology
• Public Health Surveillance
• Mathematical Modeling

Background:

• England experienced its most significant measles outbreak in a decade during 2023/24.
• Laboratory-confirmed case data was retrospective due to delays from symptom onset to test results.
• Reporting lags varied based on measles prevalence and testing objectives.

Purpose of the Study:

• To develop and evaluate nowcasting models for real-time estimation of measles trends.
• To predict future case data backfilling and assess recent epidemiological trends.
• To provide timely national and regional data for outbreak management.

Main Methods:

• A generalized additive model was developed, incorporating reporting delays, location, and day-of-week effects.
• The model was re-fit weekly to provide real-time nowcasts and trend directions.
• Model performance was evaluated retrospectively using log weighted interval score (WIS) and ranked probability score (RPS).

Main Results:

• Operational and retrospective models significantly outperformed the baseline model for national case estimates (42% and 41% reduction in log WIS).
• These models also provided superior national estimates for four-week trends (69% and 6% reduction in RPS).
• An alternative report-date indexed model showed potential for trend direction but lagged behind trend changes.

Conclusions:

• Real-time nowcasting is valuable for informing fast-evolving trends during disease outbreaks.
• Accurate reporting delay data is essential for effective epidemiological modeling and surveillance.
• The developed models offer improved accuracy for measles outbreak surveillance.