流感暴发的时空多步预测
Spatio-Temporal Multi-step Prediction of Influenza Outbreaks
摘要：
流感在全世界流行。这种全球性的感染每年都给人们的健康带来巨大负担。无论流感的全球传播的特点，大多数以前的研究集中在流感爆发的区域预测。考虑时空相关性的方法有助于更准确地预测流感爆发。此外，更准确地预测长期流感爆发和了解流感感染趋势可以帮助医院，诊所和制药公司更好地为年度流感爆发做好准备。预测未来的一系列值，即流感爆发的多步预测应该引起关注。因此，我们强调了发展时空方法对全球流感爆发进行多步预测的重要性。我们比较了SVM、RF、LSTM模型在有和没有其他国家流感数据的情况下预测未来1-4周流感数据的MAPE。我们发现LSTM模型在大多数情况下实现了最低的MAPE。至于南半球国家，与其他国家一起预测流感数据的MAPE高于没有其他国家的预测。对于北半球国家，预测有其他国家的2-4周流感数据的MAPE低于预测没有其他国家的MAPE;预测有其他国家的1-周流感数据的MAPE高于预测没有其他国家的MAPE，英国除外。在这项研究中，我们进行了时空多步预测流感爆发。考虑时空特征的方法改进了流感暴发的多步预测。
英文摘要：
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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