Covid-19 pandemic data analysis using tensor methods
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...
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| Format: | Article |
| Language: | English |
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REA Press
2024-03-01
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| Series: | Computational Algorithms and Numerical Dimensions |
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| Online Access: | https://www.journal-cand.com/article_193189_c8e4e9f17168ba351a67f4e38d885225.pdf |
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| author | Dipak Dulal Ramin Goudarzi Karim Carmeliza Navasca |
| author_facet | Dipak Dulal Ramin Goudarzi Karim Carmeliza Navasca |
| author_sort | Dipak Dulal |
| collection | DOAJ |
| description | 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 identifyCovid-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. |
| format | Article |
| id | doaj-art-e7849df6dfe84be19ebe10060f07a39c |
| institution | Kabale University |
| issn | 2980-7646 2980-9320 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | REA Press |
| record_format | Article |
| series | Computational Algorithms and Numerical Dimensions |
| spelling | doaj-art-e7849df6dfe84be19ebe10060f07a39c2025-01-30T11:22:59ZengREA PressComputational Algorithms and Numerical Dimensions2980-76462980-93202024-03-0131174410.22105/cand.2024.450017.1092193189Covid-19 pandemic data analysis using tensor methodsDipak Dulal0Ramin Goudarzi Karim1Carmeliza Navasca2Department of Mathematics, University of Alabama at Birmingham, Birmingham, AL 35294Department of Computational and Information Sciences, Stillman College, Tuscaloosa, AL 35401Department of Mathematics, University of Alabama at Birmingham, Birmingham, AL 35294In 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 identifyCovid-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.https://www.journal-cand.com/article_193189_c8e4e9f17168ba351a67f4e38d885225.pdftensortensor completiontensor decompositioncovid-19spatiotemporal data |
| spellingShingle | Dipak Dulal Ramin Goudarzi Karim Carmeliza Navasca Covid-19 pandemic data analysis using tensor methods Computational Algorithms and Numerical Dimensions tensor tensor completion tensor decomposition covid-19 spatiotemporal data |
| title | Covid-19 pandemic data analysis using tensor methods |
| title_full | Covid-19 pandemic data analysis using tensor methods |
| title_fullStr | Covid-19 pandemic data analysis using tensor methods |
| title_full_unstemmed | Covid-19 pandemic data analysis using tensor methods |
| title_short | Covid-19 pandemic data analysis using tensor methods |
| title_sort | covid 19 pandemic data analysis using tensor methods |
| topic | tensor tensor completion tensor decomposition covid-19 spatiotemporal data |
| url | https://www.journal-cand.com/article_193189_c8e4e9f17168ba351a67f4e38d885225.pdf |
| work_keys_str_mv | AT dipakdulal covid19pandemicdataanalysisusingtensormethods AT ramingoudarzikarim covid19pandemicdataanalysisusingtensormethods AT carmelizanavasca covid19pandemicdataanalysisusingtensormethods |