Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks

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 For...

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Bibliographic Details
Published in:BMC bioinformatics 2014-08, Vol.15 (1), p.276-276, Article 276
Main Authors: Kane, Michael J, Price, Natalie, Scotch, Matthew, Rabinowitz, Peter
Format: Article
Language:English
Subjects:
Animals
Artificial Intelligence
Behavior
Bird populations
Birds
Birds - virology
Care and treatment
Communicable diseases
Computational Biology - methods
Diagnosis
Disease
Disease Outbreaks - statistics & numerical data
Economic models
Egypt - epidemiology
Epidemics
Humans
Influenza A Virus, H5N1 Subtype - physiology
Influenza in Birds - epidemiology
Influenza, Human - epidemiology
Medical research
Medicine, Experimental
Methodology
Models, Statistical
Mortality
Noise
Public health
Rain
Risk factors
Stochastic models
Studies
Time series
Tropical diseases
Variables
Zoonoses
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Summary: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.
ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-15-276