We present a machine learning-based methodology capable of providing
real-time ("nowcast") and forecast estimates of influenza activity in the US by
leveraging data from multiple data sources including: Google searches, Twitter
microblogs, nearly real-time hospital visit records, and data from a
participatory surveillance system. Our main contribution consists of combining
multiple influenza-like illnesses (ILI) activity estimates, generated
independently with each data source, into a single prediction of ILI utilizing
machine learning ensemble approaches. Our methodology exploits the information
in each data source and produces accurate weekly ILI predictions for up to four
weeks ahead of the release of CDC's ILI reports. We evaluate the predictive
ability of our ensemble approach during the 2013-2014 (retrospective) and
2014-2015 (live) flu seasons for each of the four weekly time horizons. Our
ensemble approach demonstrates several advantages: (1) our ensemble method's
predictions outperform every prediction using each data source independently,
(2) our methodology can produce predictions one week ahead of GFT's real-time
estimates with comparable accuracy, and (3) our two and three week forecast
estimates have comparable accuracy to real-time predictions using an
autoregressive model. Moreover, our results show that considerable insight is
gained from incorporating disparate data streams, in the form of social media
and crowd sourced data, into influenza predictions in all time horizons

本研究使用机器学习方法，结合多种数据源包括谷歌搜索、社交媒体数据和卫生部门数据等，提供实时和预测性的美国流感活动估计。通过将每个数据源产生的流感样病例（ILI）活动情况独立生成的多个估计值结合起来，使用机器学习集成方法生成准确的每周 ILI 预测，为 CDC ILI 报告发布前的四周提供预测。结果证明，将不同数据源（包括社交媒体和众包数据）纳入流感预测可以获得极大的洞见。