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.

翻译：在本文中,我们使用强光模型来分析Covid-19大流行数据。 首先,我们使用强光模型、卡通多球体和更高级的塔克分解模型来提取多种模式的图案。 其次,我们使用高光多球多球多球分解法来预测多空间源的时空数据并识别Covid-19热点。 我们使用常规的迭代高光谱完成技术以及实用的规范参数测量器来预测Covid-19案例的传播并发现和识别热点。 我们的方法可以高精确地预测周和季度的Covid-19扩散。 第三,我们用新的抽样方法来分析美国的Covid-19数据,用于交替最小区域。 此外,我们将这些算法与标准的高光分解器的可解释性、可视化和成本分析进行比较。 最后,我们通过将这些技术应用于新泽西的Covid-19数据来展示这些方法的功效。