  Flu circulates all over the world. The worldwide infection places a
substantial burden on people's health every year. Regardless of the
characteristic of the worldwide circulation of flu, most previous studies
focused on regional prediction of flu outbreaks. The methodology of considering
the spatio-temporal correlation could help forecast flu outbreaks more
precisely. Furthermore, forecasting a long-term flu outbreak, and understanding
flu infection trends more accurately could help hospitals, clinics, and
pharmaceutical companies to better prepare for annual flu outbreaks. Predicting
a sequence of values in the future, namely, the multi-step prediction of flu
outbreaks should cause concern. Therefore, we highlight the importance of
developing spatio-temporal methodologies to perform multi-step prediction of
worldwide flu outbreaks. We compared the MAPEs of SVM, RF, LSTM models of
predicting flu data of the 1-4 weeks ahead with and without other countries'
flu data. We found the LSTM models achieved the lowest MAPEs in most cases. As
for countries in the Southern hemisphere, the MAPEs of predicting flu data with
other countries are higher than those of predicting without other countries.
For countries in the Northern hemisphere, the MAPEs of predicting flu data of
the 2-4 weeks ahead with other countries are lower than those of predicting
without other countries; and the MAPEs of predicting flu data of the 1-weeks
ahead with other countries are higher than those of predicting without other
countries, except for the UK. In this study, we performed the spatio-temporal
multi-step prediction of influenza outbreaks. The methodology considering the
spatio-temporal features improves the multi-step prediction of flu outbreaks.
