Model validation and predictions
Using in-sample data for model training and out-of-sample data for model testing, we used the following four validations: 1) randomly sampled 80% of weeks without replacement; 2) leave out 52 non-contiguous randomly sampled weeks; 3) leave out 20% of randomly sampled schools, and 4) leave one influenza season out (i.e., model training used all but one season and the out-of-sample season was used for model testing) to account for influenzas’ seasonal variation. Estimated R2 used linear regressions of out-of-sample observed influenza cases (outcome) and predicted cases (independent variable). Prediction metrics used mean absolute error (MAE) and relative mean absolute error (relMAE). Mean absolute error was defined as the mean of the absolute value of model prediction errors(18). Relative MAE is the ratio comparing a model’s MAE to a reference MAE (i.e., from a model including calendar week, and average weekly temperature, and relative humidity), where relMAE of 1.0 indicated the same prediction error for two models. We visually compared observed and predicted cumulative distributions and time-series of influenza cases.