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Forecasting influenza in a timely manner aids health organizations andpolicymakers in adequate preparation and decision making. However, effectiveinfluenza forecasting still remains a challenge despite increasing researchinterest. It is even more challenging amidst the COVID pandemic, when theinfluenza-like illness (ILI) counts are affected by various factors such assymptomatic similarities with COVID-19 and shift in healthcare seeking patternsof the general population. Under the current pandemic, historical influenzamodels carry valuable expertise about the disease dynamics but facedifficulties adapting. Therefore, we propose CALI-Net, a neural transferlearning architecture which allows us to 'steer' a historical diseaseforecasting model to new scenarios where flu and COVID co-exist. Our frameworkenables this adaptation by automatically learning when it should emphasizelearning from COVID-related signals and when it should learn from thehistorical model. Thus, we exploit representations learned from historical ILIdata as well as the limited COVID-related signals. Our experiments demonstratethat our approach is successful in adapting a historical forecasting model tothe current pandemic. In addition, we show that success in our primary goal,adaptation, does not sacrifice overall performance as compared withstate-of-the-art influenza forecasting approaches.

Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19