Optimal multi-source forecasting of seasonal influenza.

ABSTRACT: Forecasting the emergence and spread of influenza viruses is an important public health challenge. Timely and accurate estimates of influenza prevalence, particularly of severe cases requiring hospitalization, can improve control measures to reduce transmission and mortality. Here, we extend a previously published machine learning method for influenza forecasting to integrate multiple diverse data sources, including traditional surveillance data, electronic health records, internet search traffic, and social media activity. Our hierarchical framework uses multi-linear regression to combine forecasts from multiple data sources and greedy optimization with forward selection to sequentially choose the most predictive combinations of data sources. We show that the systematic integration of com

SUBMITTER: Ertem Z 

PROVIDER: S-EPMC6138397 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature