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LSTM Recurrent Neural Networks for Influenza Trends Prediction

  • Liyuan Liu
  • Meng Han
  • Yiyun Zhou
  • Yan Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10847)

Abstract

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.

Keywords

Influenza-like illness Influenza trends Google trends Climate change Air pollution Long short term memory 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Kennesaw State UniversityKennesawUSA

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