  The accurate forecasting of infectious epidemic diseases such as influenza is
a crucial task undertaken by medical institutions. Although numerous flu
forecasting methods and models based mainly on historical flu activity data and
online user-generated contents have been proposed in previous studies, no flu
forecasting model targeting multiple countries using two types of data exists
at present. Our paper leverages multi-task learning to tackle the challenge of
building one flu forecasting model targeting multiple countries; each country
as each task. Also, to develop the flu prediction model with higher
performance, we solved two issues; finding suitable search queries, which are
part of the user-generated contents, and how to leverage search queries
efficiently in the model creation. For the first issue, we propose the transfer
approaches from English to other languages. For the second issue, we propose a
novel flu forecasting model that takes advantage of search queries using an
attention mechanism and extend the model to a multi-task model for multiple
countries' flu forecasts. Experiments on forecasting flu epidemics in five
countries demonstrate that our model significantly improved the performance by
leveraging the search queries and multi-task learning compared to the
baselines.
