Tweet distribution across geolocations.

<p>The number of tweets collected within a 25-mile radius of military installations for 31 geolocations.</p

Tweet distribution across geolocations.

<p>The number of tweets collected within a 25-mile radius of military installations for 31 geolocations.</p

The ICD-9 codes used to describe ILI symptoms (NOS = Not otherwise specified).

<p>The ICD-9 codes used to describe ILI symptoms (NOS = Not otherwise specified).</p

ILI nowcasting results (current week) for six geolocations estimated using cross-validation over four years (2011–2014).

<p>Models: AdaBoost, SVM with a linear kernel, and LSTM. Metrics: Pearson correlation (CORR), RMSPE (%), MAPE (%), and RMSE. The highest performance results within each datatype are highlighted in bold.</p

ILI prediction results for 31 geolocations estimated using MAPE for nowcasting (this week) and forecasting (one and two weeks).

<p>Locations with min and max MAPE scores are underlined.</p

True vs. predicted ILI dynamics one week in advance as a function of time in 2014 for 31 geolocations.

<p>We plot true ILI values (True), one week forecasts obtained using social media features only (SM), ILI historical data (ILI), and combined ILI + SM data (ILISM).</p

True vs. predicted ILI proportions (real-time current week estimates) as a function of time (2011–2014) for six geolocations.

<p>We plot true ILI proportions (True), predictions from social media (tweet and network) features (SM), and predictions from ILI historical data (ILI) obtained using LSTM model.</p

ILI forecasting (one week) results for six geolocations.

<p>Models: AdaBoost, SVM with a linear kernel, and LSTM. Metrics: RMSPE (%), MAPE (%), and RMSE (% ILI). The best performing models within each data type are highlighted in bold.</p

Model performance measured as Pearson correlation between true and predicted ILI dynamics as a function of the number of tweets per location.

<p>Predictions are made for the current week, one and two weeks in advance using SMOnly and ILI + SM models. Outlier locations are marked with labels. Trends are shown as dotted lines. International locations are shown as triangles.</p

Diagram of a two-branch neural network model for ILI dynamics prediction.

<p>The model combines ILI historical estimates encoded using one LSTM layer on the left and social media predictors (ILI + SM) encoded using another LSTM layer on the right to forecast ILI dynamics in weeks.</p