An Application of Nowcasting Methods: Cases of Norovirus during the Winter 2023/2024 in England

Abstract

AbstractBackgroundNorovirus 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.MethodsTo nowcast the real-time case burden of norovirus a generalised additive model, semi-mechanistic Bayesian joint process and delay model, and Bayesian structural time series model including syndromic surveillance data were developed. These models were evaluated over weekly nowcasts using a probabilistic scoring framework.ResultsModelling current cases clearly outperforms a simple heuristic approach. Models that harnessed a time delay correction had higher skill, overall, relative to forecasting techniques. However, forecasting approaches were found to be more reliable in the event of temporally changeable reporting patterns. The incorporation of norovirus syndromic surveillance data was not shown to improve model skill in this nowcasting task, which may be indicative poor correlation between the indicator and norovirus incidence.InterpretationAnalysis of surveillance data enhanced by nowcasting reporting delays improves understanding over simple model assumptions, which is important for real-time decision making. The structure of 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.