  Early prediction of the prevalence of influenza reduces its impact. Various
studies have been conducted to predict the number of influenza-infected people.
However, these studies are not highly accurate especially in the distant future
such as over one month. To deal with this problem, we investigate the sequence
to sequence (Seq2Seq) with attention model using Google Trends data to assess
and predict the number of influenza-infected people over the course of multiple
weeks. Google Trends data help to compensate the dark figures including the
statistics and improve the prediction accuracy. We demonstrate that the
attention mechanism is highly effective to improve prediction accuracy and
achieves state-of-the art results, with a Pearson correlation and
root-mean-square error of 0.996 and 0.67, respectively. However, the prediction
accuracy of the peak of influenza epidemic is not sufficient, and further
investigation is needed to overcome this problem.
