  Throughout the Covid-19 pandemic, a significant amount of effort had been put
into developing techniques that predict the number of infections under various
assumptions about the public policy and non-pharmaceutical interventions. While
both the available data and the sophistication of the AI models and available
computing power exceed what was available in previous years, the overall
success of prediction approaches was very limited. In this paper, we start from
prediction algorithms proposed for XPrize Pandemic Response Challenge and
consider several directions that might allow their improvement. Then, we
investigate their performance over medium-term predictions extending over
several months. We find that augmenting the algorithms with additional
information about the culture of the modeled region, incorporating traditional
compartmental models and up-to-date deep learning architectures can improve the
performance for short term predictions, the accuracy of medium-term predictions
is still very low and a significant amount of future research is needed to make
such models a reliable component of a public policy toolbox.
