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

ABSTRACT: Accurate and reliable forecasts of seasonal epidemics of infectious disease can assist in the design of countermeasures and increase public awareness and preparedness. This article describes two main contributions we made recently toward this goal: a novel approach to probabilistic modeling of surveillance 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 available 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

SUBMITTER: Brooks LC 

PROVIDER: S-EPMC6034894 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature