Model-based epidemiological assessment is useful to support decision-making at the beginning of an emerging Aedes-transmitted outbreak.However, early forecasts are generally unreliable as little information is available in click here the first few incidence data points.Here, we show how past Aedes-transmitted epidemics help improve these predictions.The approach was applied to the 2015-2017 Zika virus epidemics in three islands of the French West Indies, with historical data including other Aedes-transmitted diseases (chikungunya and Zika) in the same and other locations.Hierarchical models were used to build informative a priori distributions on the reproduction ratio and the reporting rates.
The accuracy and sharpness of forecasts improved substantially when these a priori distributions were used in models for prediction.For example, early forecasts of final epidemic size obtained without historical information were 3.3 times too high on average (range: 0.2 to 5.8) with respect to the eventual size, but were far closer (1.
1 times the real value on average, range: 0.4 to 1.5) using information on past CHIKV epidemics in the same places.Likewise, the 97.5% upper bound for maximal incidence was 15.
3 times (range: 2.0 to 63.1) the actual peak incidence, and became much sharper at 2.4 times (range: 1.3 to 3.
9) the actual peak incidence with informative a priori distributions.Improvements were more limited for the date of peak incidence and the total duration of the epidemic.The framework can adapt to all forecasting models at the early stages 6-0 igora vibrance of emerging Aedes-transmitted outbreaks.