Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks
1 Yale Center for Analytical Sciences, Yale University, New Haven, CT, USA
2 Biostatistics Department, Yale University, New Haven, CT, USA
3 School of Public Health, Harvard University, Cambridge, MA, USA
4 Department of Biomedical Informatics, College of Health Solutions, Arizona State University, Tempe, AZ, USA
5 Center for Environmental Security, Biodesign Institute and Security and Defense Systems Initiative, Arizona State University, Tempe, AZ, USA
6 Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
BMC Bioinformatics 2014, 15:276 doi:10.1186/1471-2105-15-276Published: 13 August 2014
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.
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.
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.