Mellor, Jonathon (J);Tang, Maria L (ML);Finch, Emilie (E);Christie, Rachel (R);Polhill, Oliver (O);Overton, Christopher E (CE);Hoban, Ann (A);Douglas, Amy (A);Deeny, Sarah R (SR);Ward, Thomas (T);

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PLoS Comput Biol.2025 Feb 21;21(2):e1012849.doi:10.1371/journal.pcbi.1012849

Abstract

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.