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Alfred B Amendolara1,2, David Sant3, Horacio G Rotstein4

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Summary

This study used a deep neural network to predict influenza (flu) outbreaks, finding temperature is the strongest predictor of infection rates. The model achieved highly accurate short-term flu forecasting, outperforming other methods.

Keywords:
EpidemiologyInfluenzaLSTMMachine learningModeling

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Machine Learning

Background:

  • Influenza virus causes yearly global epidemics.
  • Predicting seasonal flu variations and infection mechanisms is crucial.
  • Historical Influenza-Like-Illness (ILI), climate, and population data were utilized.

Purpose of the Study:

  • To develop a predictive model for short-term, seasonal flu infection rates.
  • To identify key environmental and demographic factors influencing flu transmission.
  • To leverage deep learning for enhanced flu forecasting.

Main Methods:

  • Trained a Long Short-Term Memory (LSTM)-based deep neural network.
  • Utilized data from CDC, NCEI, and US Census Bureau.
  • Explored roles of temperature, precipitation, wind speed, population, and vaccination rates.
  • Validated model using K-fold and forward chaining cross-validation.

Main Results:

  • Temperature identified as the strongest predictor of ILI rates.
  • Precipitation was found to increase the predictive power of the model.
  • Achieved a +1 week prediction Mean Absolute Error (MAE) of 0.1973, outperforming other algorithms.
  • Model accurately predicted simulation data and demonstrated temperature's impact on accuracy.

Conclusions:

  • LSTM-based deep neural networks are effective for short-term flu forecasting.
  • The model surpasses traditional algorithms in predictive accuracy.
  • Identified key climatic and biotic factors influencing flu dynamics.
  • Findings are vital for flu forecasting, especially considering potential impacts of the SARS-CoV-2 pandemic.