COVID-19 and Influenza Joint Forecasts Using Internet Search Information in the United States

TL;DR

This paper introduces ARGOX-Joint-Ensemble, a novel ensemble framework that combines influenza and COVID-19 forecasting models to improve joint disease prediction during the pandemic, aiding public health decision-making.

Contribution

The paper presents a new ensemble method that effectively integrates influenza and COVID-19 forecasts, addressing challenges of co-circulation and similar symptoms in disease modeling.

Findings

Outperforms existing benchmark models in joint forecasting accuracy.

Adapts influenza models to pandemic conditions, enhancing COVID-19 predictions.

Remains competitive with publicly available forecasting models.

Abstract

As COVID-19 pandemic progresses, severe flu seasons may happen alongside an increase in cases in cases and death of COVID-19, causing severe burdens on health care resources and public safety. A consequence of a twindemic may be a mixture of two different infections in the same person at the same time, "flurona". Admist the raising trend of "flurona", forecasting both influenza outbreaks and COVID-19 waves in a timely manner is more urgent than ever, as accurate joint real-time tracking of the twindemic aids health organizations and policymakers in adequate preparation and decision making. Under the current pandemic, state-of-art influenza and COVID-19 forecasting models carry valuable domain information but face shortcomings under current complex disease dynamics, such as similarities in symptoms and public healthcare seeking patterns of the two diseases. Inspired by the inner-connection between influenza and COVID-19 activities, we propose ARGOX-Joint-Ensemble which allows us to combine historical influenza and COVID-19 disease forecasting models to a new ensemble framework that handles scenarios where flu and COVID co-exist. Our framework is able to emphasize learning from COVID-related or influenza signals, through a winner-takes-all ensemble fashion. Moreover, our experiments demonstrate that our approach is successful in adapting past influenza forecasting models to the current pandemic, while improving upon previous COVID-19 forecasting models, by steadily outperforming alternative benchmark methods, and remaining competitive with publicly available models.