Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions

This article describes two main contributions we made recently toward this goal: a novel approach to probabilistic modeling of sur veillance time series based on “delta densities”, and an optimization scheme for combining output from multiple forecasting methods into an adaptively weighted ensemble. Delta densities describe the probability distribution of the change between one observation and the next, conditioned on avail able data; chaining together nonparametric estimates of these distributions yields a model for an entire trajectory. Corresponding distributional forecasts cover more observed events than alternatives that treat the whole season as a unit, and improve upon multiple evaluation metrics when extracting key targets of interest to public health officials. Adaptively weighted ensembles integrate the results of multiple forecasting methods, such as delta density, using weights that can change from situation to situation. We treat selection of optimal weightings across forecasting methods as a separat e estimation task, and describe an estimation procedure based on optimizing cross-validation performance. We consider some details of the data generation process, including data revisions and holiday effects, both in the construction of these forecasting methods and when performing retrospective eva luation. The delta density method and an adaptively weighted ensemble of other forecasting methods each improve significantly on the next best ensemble component when...

Source: PLoS Computational Biology - June 15, 2018 Category: Biology Authors: Logan C. Brooks Source Type: research