  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.
