title: Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks.
abstract: BACKGROUND
  Time series models can play an important role in disease prediction. Incidence data can be used to predict the future occurrence of disease events. Developments in modeling approaches provide an opportunity to compare different time series models for predictive power.
  
  
  RESULTS
  We applied ARIMA and Random Forest time series models to incidence data of outbreaks of highly pathogenic avian influenza (H5N1) in Egypt, available through the online EMPRES-I system. We found that the Random Forest model outperformed the ARIMA model in predictive ability. Furthermore, we found that the Random Forest model is effective for predicting outbreaks of H5N1 in Egypt.
  
  
  CONCLUSIONS
  Random Forest time series modeling provides enhanced predictive ability over existing time series models for the prediction of infectious disease outbreaks. This result, along with those showing the concordance between bird and human outbreaks (Rabinowitz et al. 2012), provides a new approach to predicting these dangerous outbreaks in bird populations based on existing, freely available data. Our analysis uncovers the time-series structure of outbreak severity for highly pathogenic avain influenza (H5N1) in Egypt.
authors.affiliation: Yale Center for Analytical Sciences, Yale University, New Haven, CT, USA. michael.kane@yale.edu.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Kane
authors.forename: Natalie
authors.initials: N
authors.lastname: Price
authors.forename: Matthew
authors.initials: M
authors.lastname: Scotch
authors.forename: Peter
authors.initials: P
authors.lastname: Rabinowitz
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D001717:Birds / Q000821:virology; D019295:Computational Biology / Q000379:methods*; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D004534:Egypt / Q000453:epidemiology; D006801:Humans; D053124:Influenza A Virus, H5N1 Subtype / Q000502:physiology*; D005585:Influenza in Birds / Q000453:epidemiology*; D007251:Influenza, Human / Q000453:epidemiology*; D015233:Models, Statistical