Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks
By Michael J. Kane, Natalie Price, Matthew Scotch, Peter Rabinowitz. 2014
353 citations recorded by the source corpus.
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.
Subfields: Epidemiology, Public Health, Environmental and Occupational Health
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Sima Siami‐Namini
- Neda Tavakoli
- Akbar Siami Namin
- Ali Aït-Bachir
- Éric Gaussier
- Parantapa Goswami
- Yagmur Gizem Cinar
- Vadim V. Strijov
Citations in the corpus
- Position-Based Content Attention for Time Series Forecasting with Sequence-to-Sequence RNNs (2017, methodology): “These methods, discussed in Section 2, have also been shown to provide state-of-the-art results on various forecasting problems ( e.g. [16]).”
- A Comparison of ARIMA and LSTM in Forecasting Time Series (2018, background): “On the other hand, the optimization of an GARCH model and its parameters might be challenging and problematic [13].”