An application of nowcasting methods: Cases of norovirus during the winter 2023/2024 in England

Background: Norovirus is a leading cause of acute gastroenteritis, adding to strain on healthcare systems. Diagnostic test reporting of norovirus is often delayed, resulting in incomplete data for real-time surveillance. Methods: To nowcast the real-time case burden of norovirus a generalised additive model (GAM), semi-mechanistic Bayesian joint process and delay model “epinowcast”, and Bayesian structural time series (BSTS) model including syndromic surveillance data were developed. These models were evaluated over weekly nowcasts using a probabilistic scoring framework. Results: Using the weighted interval score (WIS) we show a heuristic approach is outperformed by models harnessing time delay corrections, with daily mean WIS = 7.73, 3.03, 2.29 for the baseline, “epinowcast”, and GAM, respectively. Forecasting approaches were reliable in the event of temporally changing reporting values, with WIS = 4.57 for the BSTS model. However, the syndromic surveillance (111 online pathways) did not improve the BSTS model, WIS = 10.28, potentially indicating poor correspondence between surveillance indicators. Interpretation: Analysis of surveillance data enhanced by nowcasting delayed reporting improves understanding over simple model assumptions, important for real-time decision making. The modelling approach needs to be informed by the patterns of the reporting delay and can have large impacts on operational performance and insights produced. Author summary: Norovirus is an important pathogen for infectious disease surveillance as it causes hospital strain. However, reporting delays, the time from taking a test to the data being reported to national surveillance, for norovirus cases make it challenging to understand trends in real-time This is because data in the most recent week is partially missing. The most recent cases can be estimated using methods called “nowcasting”. In this work we explore a range of different methods for nowcasting norovirus cases in England over winter 2023/24. We show that while there are differences between each model, the best performing models are those that use the partially reported data. The research shows that norovirus cases could be predicted well in real-time if an appropriate method is chosen. Additionally, we explored whether using other data, namely an online health-guidance surveillance system (NHS 111 online), can help improve model performance, but for this use case there did not appear to be a benefit.