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.

In this paper, the stationary waiting time distributions FLI (x) and F (x) are explicitly formu}ated for the GItEklrn queue under the first-come first-served discipline. The transition probability matrix and the imbedded probabilities play the important roles in this study. Some numerieal results are presented for vaTious systernr, as E2/Eklm, U2/Ektrn, DXEklm, etc. Further the properties ofF(x) are eonsidered.

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