LSTM Recurrent Neural Networks for Influenza Trends Prediction

• Published in: Bioinformatics Research and Applications Vol. 10847; pp. 259 - 264
• Main Authors: Liu, Liyuan, Han, Meng, Zhou, Yiyun, Wang, Yan
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Air pollution; Climate change; Google trends; Influenza trends; Influenza-like illness; Long short term memory
• ISBN: 9783319949673; 3319949675
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-94968-0_25

Influenza-like illness (ILI) is an acute respiratory infection causes substantial mortality and morbidity. Predict Influenza trends and response to a health disease rapidly is crucial to diminish the loss of life. In this paper, we employ the long short term memory (LSTM) recurrent neural networks to forecast the influenza trends. We are the first one to use multiple and novel data sources including virologic surveillance, influenza geographic spread, Google trends, climate and air pollution to predict influenza trends. Moreover, We find there are several environmental and climatic factors have the significant correlation with ILI rate.