17To identify countries that have seasonal patterns similar to the time series of influenza 18 surveillance data in the United States and other countries, and to forecast the 2018-2019 19 seasonal influenza outbreak in the U.S. using linear regression, auto regressive integrated 20 moving average, and deep learning. We collected the surveillance data of 164 countries from 21 2010 to 2018 using the FluNet database. Data for influenza-like illness (ILI) in the U.S. were 22 collected from the Fluview database. This cross-correlation study identified the time lag 23 between the two time-series. Deep learning was performed to forecast ILI, total influenza, A, 24 and B viruses after 26 weeks in the U.S. The seasonal influenza patterns in Australia and Chile 25showed a high correlation with those of the U.S. 22 weeks and 28 weeks earlier, respectively.
26The R 2 score of DNN models for ILI for validation set in 2015-2019 was 0.722 despite how 27 hard it is to forecast 26 weeks ahead. Our prediction models forecast that the ILI for the U.S.
28in 2018-2019 may be later and less severe than those in 2017-2018, judging from the influenza 29 activity for Australia and Chile in 2018. It allows to estimate peak timing, peak intensity, and 30 type-specific influenza activities for next season at 40 th week. The correlation for seasonal 31 influenza among Australia, Chile, and the U.S. could be used to decide on influenza vaccine 32 strategy six months ahead in the U.S. 33 34
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