Covid-19 Analysis Using Tensor Methods

TL;DR

This paper employs tensor decomposition techniques to analyze, predict, and identify Covid-19 hotspots using spatiotemporal data, demonstrating high accuracy and interpretability in real-world applications.

Contribution

It introduces a tensor-based framework for Covid-19 data analysis, including novel tensor completion and sampling methods, with practical regularization and application to US data.

Findings

High accuracy in weekly and quarterly Covid-19 spread predictions

Effective identification of Covid-19 hotspots in US data

Enhanced interpretability and visualization of tensor models

Abstract

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.