  Predicting the spread and containment of COVID-19 is a challenge of utmost
importance that the broader scientific community is currently facing. One of
the main sources of difficulty is that a very limited amount of daily COVID-19
case data is available, and with few exceptions, the majority of countries are
currently in the "exponential spread stage," and thus there is scarce
information available which would enable one to predict the phase transition
between spread and containment.
  In this paper, we propose a novel approach to predicting the spread of
COVID-19 based on dictionary learning and online nonnegative matrix
factorization (online NMF). The key idea is to learn dictionary patterns of
short evolution instances of the new daily cases in multiple countries at the
same time, so that their latent correlation structures are captured in the
dictionary patterns. We first learn such patterns by minibatch learning from
the entire time-series and then further adapt them to the time-series by online
NMF. As we progressively adapt and improve the learned dictionary patterns to
the more recent observations, we also use them to make one-step predictions by
the partial fitting. Lastly, by recursively applying the one-step predictions,
we can extrapolate our predictions into the near future. Our prediction results
can be directly attributed to the learned dictionary patterns due to their
interpretability.
