Forecasting the spatial transmission of influenza in the United States.

ABSTRACT: Recurrent outbreaks of seasonal and pandemic influenza create a need for forecasts of the geographic spread of this pathogen. Although it is well established that the spatial progression of infection is largely attributable to human mobility, difficulty obtaining real-time information on human movement has limited its incorporation into existing infectious disease forecasting techniques. In this study, we develop and validate an ensemble forecast system for predicting the spatiotemporal spread of influenza that uses readily accessible human mobility data and a metapopulation model. In retrospective state-level forecasts for 35 US states, the system accurately predicts local influenza outbreak onset,-i.e., spatial spread, defined as the week that local incidence increases above a baseline t

SUBMITTER: Pei S 

PROVIDER: S-EPMC5856508 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature