tetano
Editor, Senior Moderator
Sci Adv
. 2021 Jun 16;7(25):eabb1237.
doi: 10.1126/sciadv.abb1237. Print 2021 Jun.
Toward the use of neural networks for influenza prediction at multiple spatial resolutions
Emily L Aiken[SUP] 1 [/SUP], Andre T Nguyen[SUP] 2 3 [/SUP], Cecile Viboud[SUP] 4 [/SUP], Mauricio Santillana[SUP] 1 5 6 [/SUP]
Affiliations
Abstract
Mitigating the effects of disease outbreaks with timely and effective interventions requires accurate real-time surveillance and forecasting of disease activity, but traditional health care-based surveillance systems are limited by inherent reporting delays. Machine learning methods have the potential to fill this temporal "data gap," but work to date in this area has focused on relatively simple methods and coarse geographic resolutions (state level and above). We evaluate the predictive performance of a gated recurrent unit neural network approach in comparison with baseline machine learning methods for estimating influenza activity in the United States at the state and city levels and experiment with the inclusion of real-time Internet search data. We find that the neural network approach improves upon baseline models for long time horizons of prediction but is not improved by real-time internet search data. We conduct a thorough analysis of feature importances in all considered models for interpretability purposes.
. 2021 Jun 16;7(25):eabb1237.
doi: 10.1126/sciadv.abb1237. Print 2021 Jun.
Toward the use of neural networks for influenza prediction at multiple spatial resolutions
Emily L Aiken[SUP] 1 [/SUP], Andre T Nguyen[SUP] 2 3 [/SUP], Cecile Viboud[SUP] 4 [/SUP], Mauricio Santillana[SUP] 1 5 6 [/SUP]
Affiliations
- PMID: 34134985
- DOI: 10.1126/sciadv.abb1237
Abstract
Mitigating the effects of disease outbreaks with timely and effective interventions requires accurate real-time surveillance and forecasting of disease activity, but traditional health care-based surveillance systems are limited by inherent reporting delays. Machine learning methods have the potential to fill this temporal "data gap," but work to date in this area has focused on relatively simple methods and coarse geographic resolutions (state level and above). We evaluate the predictive performance of a gated recurrent unit neural network approach in comparison with baseline machine learning methods for estimating influenza activity in the United States at the state and city levels and experiment with the inclusion of real-time Internet search data. We find that the neural network approach improves upon baseline models for long time horizons of prediction but is not improved by real-time internet search data. We conduct a thorough analysis of feature importances in all considered models for interpretability purposes.