  Severe acute respiratory disease SARS-CoV-2 has had a found impact on public
health systems and healthcare emergency response especially with respect to
making decisions on the most effective measures to be taken at any given time.
As demonstrated throughout the last three years with COVID-19, the prediction
of the number of positive cases can be an effective way to facilitate
decision-making. However, the limited availability of data and the highly
dynamic and uncertain nature of the virus transmissibility makes this task very
challenging. Aiming at investigating these challenges and in order to address
this problem, this work studies data-driven (learning, statistical) methods for
incrementally training models to adapt to these nonstationary conditions. An
extensive empirical study is conducted to examine various characteristics, such
as, performance analysis on a per virus wave basis, feature extraction,
"lookback" window size, memory size, all for next-, 7-, and 14-day forecasting
tasks. We demonstrate that the incremental learning framework can successfully
address the aforementioned challenges and perform well during outbreaks,
providing accurate predictions.
