  Public health surveillance systems often fail to detect emerging infectious
diseases, particularly in resource limited settings. By integrating relevant
clinical and internet-source data, we can close critical gaps in coverage and
accelerate outbreak detection. Here, we present a multivariate algorithm that
uses freely available online data to provide early warning of emerging
influenza epidemics in the US. We evaluated 240 candidate predictors and found
that the most predictive combination does \textit{not} include surveillance or
electronic health records data, but instead consists of eight Google search and
Wikipedia pageview time series reflecting changing levels of interest in
influenza-related topics. In cross validation on 2010-2016 data, this algorithm
sounds alarms an average of 16.4 weeks prior to influenza activity reaching the
Center for Disease Control and Prevention (CDC) threshold for declaring the
start of the season. In an out-of-sample test on data from the rapidly-emerging
fall wave of the 2009 H1N1 pandemic, it recognized the threat five weeks in
advance of this surveillance threshold. Simpler algorithms, including fixed
week-of-the-year triggers, lag the optimized alarms by only a few weeks when
detecting seasonal influenza, but fail to provide early warning in the 2009
pandemic scenario. This demonstrates a robust method for designing next
generation outbreak detection algorithms. By combining scan statistics with
machine learning, it identifies tractable combinations of data sources (from
among thousands of candidates) that can provide early warning of emerging
infectious disease threats worldwide.
