Forecasting norovirus cases on cruise ships to support outbreak management on board

Background: Norovirus outbreaks on cruise ships are a significant threat to the cruising industry. Mathematical models have the potential to leverage routinely collected syndromic surveillance data on board to provide insight into outbreak evolution. Methods: We used historical data from seven norovirus outbreaks occurred in 2011-2013, totalling 359 diagnosed cases, to assess the performance of automated forecasts in real-time. We compared the performance of a set of alternative models on three endpoints (the number of cases by symptom onset time, by diagnosis date, and the total number of cases until the end of the cruise), using the logarithmic score (logS), the ranked probability score (RPS), and the 95% coverage. Results: We found that the best forecasting performance was given by a model that includes both superspreading and the effect of case isolation. This model had in most cases a better score than that of a baseline model assuming constant incidence; this happened in 59-70% of data points when assessed using the logS and 53-57% with the RPS (depending on the considered endpoint). The best model also had the highest coverage over all endpoints. Its added value was especially evident for longer forecasting horizons, with an improvement in performance for up to 78% of data points, both according to the logS and the RPS. Conclusions: Simple mathematical models integrating key mechanisms of norovirus transmission can help predict the number of cases on board. This knowledge can be automatized in syndromic surveillance systems to support decision making for the management of outbreaks.