Spatio-Temporal Multi-step Prediction of Influenza Outbreaks

• URL: http://arxiv.org/abs/2102.08137v1
• Date: Tue, 16 Feb 2021 13:17:11 GMT
• Title: Spatio-Temporal Multi-step Prediction of Influenza Outbreaks
• Authors: Jie Zhang, Kazumitsu Nawata, Hongyan Wu
• Abstract summary: The worldwide infection places a substantial burden on people's health every year. The methodology of considering the multi-step prediction of flu outbreaks could help forecast flu outbreaks more precisely. Forecasting flu infection trends more accurately could help hospitals, clinics, and pharmaceutical companies to better prepare for annual flu outbreaks.
• Score: 4.578493011818268
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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