  In this paper, we use tensor models to analyze Covid-19 pandemic data. First,
we use tensor models, canonical polyadic and higher-order Tucker
decompositions, to extract patterns over multiple modes. Second, we implement a
tensor completion algorithm using canonical polyadic tensor decomposition to
predict spatiotemporal data from multiple spatial sources and to identify
Covid-19 hotspots. We apply a regularized iterative tensor completion technique
with a practical regularization parameter estimator to predict the spread of
Covid-19 cases and to find and identify hotspots. Our method can predict weekly
and quarterly Covid-19 spreads with high accuracy. Third, we analyze Covid-19
data in the US using a novel sampling method for alternating least-squares.
Moreover, we compare the algorithms with standard tensor decompositions in
terms of their interpretability, visualization and cost analysis. Finally, we
demonstrate the efficacy of the methods by applying the techniques to New
Jersey's Covid-19 data.
