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原文連結
論文資訊
- 類型：已發表論文
- 日期：2018
摘要
Forecasting the 湧現 and spread of influenza 病毒es 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 傳播 and mortality. Here, we extend a previously published 機器學習 method for influenza forecasting to integrate multiple diverse data sources, including traditional surveillance data, electronic health records, internet search traffic, and 社會 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 substant
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