Optimal multi-source forecasting of seasonal influenza

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 complementary data sources can substantially improve forecast accuracy over single data sources. When forecasting the Center for Disease Control and Prevention (CDC) influenza-like-illness reports (ILINet) from week 48 through week 20, the optimal combination of predictors includes public health surveillance data and commercially available electronic medical records, but neither search engine nor social media data.Author summary: In the United States, seasonal influenza causes thousands of deaths and hundreds of thousands of hospitalizations. The annual timing and burden of the flu season vary considerably with the severity of the circulating viruses. Epidemic forecasting can inform early and effective countermeasures to limit the human toll of severe seasonal and pandemic influenza. With a growing toolkit of sophisticated statistical methods and the recent explosion of influenza-related data, we can now systematically match models to data to achieve timely and accurate warning as flu epidemics emerge, peak and subside. Here, we introduce a framework for identifying optimal combinations of data sources, and show that public health surveillance data and electronic health records collectively forecast seasonal influenza better than any single data source alone and better than influenza-related search engine and social media data.