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.

Figure 1

The retrospective predictions. The retrospective predictions for the ARIMA model and the Random Forest model along with the actual outbreak counts.

Figure 2

The retrospective residuals. The retrospective residuals for the ARIMA and Random Forest model.

Figure 3

The simulated prospective predictions. The simulated prospective predictions for the ARIMA model and the Random Forest model along with the actual outbreak counts.

Figure 4

The simulated prospective residuals. The simulated prospective residuals for the ARIMA and Random Forest model.

Figure 5

The actual and predicted changes for the Random Forest model. The actual and predicted changes in outbreak for each day of the simulated prospective study using the Random Forest model.

Figure 6

Quantile plot of the residuals for the simulated prospective analysis using Random Forests. The normal quantile plot of the differences between the predicted changes and the actual changes using the Random Forest model.