Document Type : Original Article

Authors

• Dipak Dulal ¹
• Ramin Goudarzi Karim ²
• Carmeliza Navasca ¹

¹ Department of Mathematics, University of Alabama at Birmingham, Birmingham, AL 35294

² Department of Computational and Information Sciences, Stillman College, Tuscaloosa, AL 35401

Abstract

In this paper, we use tensor models to analyze the 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 leastsquares. Moreover, we compare the algorithms with standard tensor decompositions concerning their interpretability, visualization, and cost analysis. Finally, we demonstrate the efficacy of the methods by applying the techniques to the New Jersey Covid-19 case tensor data.

Keywords

• tensor
• tensor completion
• tensor decomposition
• Covid-19
• spatiotemporal data