Conference proceeding
Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19
Proceedings of the AAAI Conference on Artificial Intelligence, Vol.35(6), pp.4855-4863
05/18/2021
Abstract
Forecasting influenza in a timely manner aids health organizations and policymakers in adequate preparation and decision making. However, effective influenza forecasting still remains a challenge despite increasing research interest. It is even more challenging amidst the COVID pandemic, when the influenza-like illness (ILI) counts are affected by various factors such as symptomatic similarities with COVID-19 and shift in healthcare seeking patterns of the general population. Under the current pandemic, historical influenza models carry valuable expertise about the disease dynamics but face difficulties adapting. Therefore, we propose CALI-Net, a neural transfer learning architecture which allows us to 'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist. Our framework enables this adaptation by automatically learning when it should emphasize learning from COVID-related signals and when it should learn from the historical model. Thus, we exploit representations learned from historical ILI data as well as the limited COVID-related signals. Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic. In addition, we show that success in our primary goal, adaptation, does not sacrifice overall performance as compared with state-of-the-art influenza forecasting approaches.
Details
- Title: Subtitle
- Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19
- Creators
- Alexander RodríguezNikhil MuralidharBijaya AdhikariAnika TabassumNaren RamakrishnanB. Aditya Prakash
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the AAAI Conference on Artificial Intelligence, Vol.35(6), pp.4855-4863
- Language
- English
- Date published
- 05/18/2021
- Academic Unit
- Computer Science
- Record Identifier
- 9984259473502771
Metrics
8 Record Views