Figure 1.

Ensemble forecast workflow. (a) To predict next year's epidemic status, we extract features from a daily time series of temperature (K) and precipitation (mm) over a defined (t₀, p) time interval and for each year in the training period. (b) We produce an array of features corresponding to the mean value of temperature and precipitation over the (t₀, p) interval and (c) train an SVM to classify next year's epidemic status. (d) This process is repeated for all 432 (t₀, p) intervals, and the top 11 models are automatically selected to (e) contribute to a majority voting system based on historical out-of-sample accuracy.