Understanding the Influence of Multiple Factors on the Spread of Omicron Variant Strains via the Multivariate Regression Method

Zhenkai Xu; Shaofu Lin; Zhisheng Huang; Yu Fu

doi:10.1007/978-981-99-7108-4_14

Understanding the Influence of Multiple Factors on the Spread of Omicron Variant Strains via the Multivariate Regression Method

Zhenkai Xu, Shaofu Lin^*, Zhisheng Huang, Yu Fu

^*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

The Omicron variant of SARS-CoV-2, emerging in November 2021, has rapidly spread worldwide due to its high transmissibility and ability to evade vaccines. It is still not fully under control, and there is a need to enhance our scientific understanding of the Omicron variant. Investigating the influencing factors and the correlated characteristics of the transmission of the Omicron variant remains an important issue in COVID-19 prevention and control. This study utilized data from various sources to investigate Omicron’s transmission factors. Focusing on populous countries like China, France, and the US, a multiple regression model was optimized through the Gauss-Newton method to reveal links between daily Omicron cases and variables like climate, population, healthcare, and vaccination and etc. Results showed vaccination rates, healthcare facility numbers, and population density as pivotal factors influencing transmission. Higher vaccination rates and more healthcare facilities correlated with lower Omicron transmission, while dense population areas experienced higher spread. These findings hold significance for guiding public health decisions and shaping vaccination strategies amidst the Omicron variant’s ongoing impact.

Original language	English
Title of host publication	Health Information Science
Subtitle of host publication	12th International Conference, HIS 2023, Melbourne, VIC, Australia, October 23–24, 2023, Proceedings
Editors	Yan Li, Zhisheng Huang, Manik Sharma, Lu Chen, Rui Zhou
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	161-174
Number of pages	14
ISBN (Electronic)	9789819971084
ISBN (Print)	9789819971077
DOIs	https://doi.org/10.1007/978-981-99-7108-4_14
Publication status	Published - 2023
Event	12th International Conference on Health Information Science, HIS 2023 - Melbourne, Australia Duration: 23 Oct 2023 → 24 Oct 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14305 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Health Information Science, HIS 2023
Country/Territory	Australia
City	Melbourne
Period	23/10/23 → 24/10/23

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

Keywords

COVID-19 Pandemic
Multiple Regression
Omicron
Quantitative Analysis

Access to Document

10.1007/978-981-99-7108-4_14

Understanding the Influence of Multiple Factors on the Spread of Omicron Variant Strains via the Multivariate Regression MethodFinal published version, 348 KBLicence: Article 25fa Dutch Copyright Act

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Xu, Z., Lin, S., Huang, Z., & Fu, Y. (2023). Understanding the Influence of Multiple Factors on the Spread of Omicron Variant Strains via the Multivariate Regression Method. In Y. Li, Z. Huang, M. Sharma, L. Chen, & R. Zhou (Eds.), Health Information Science: 12th International Conference, HIS 2023, Melbourne, VIC, Australia, October 23–24, 2023, Proceedings (pp. 161-174). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14305 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-7108-4_14

Xu, Zhenkai ; Lin, Shaofu ; Huang, Zhisheng et al. / Understanding the Influence of Multiple Factors on the Spread of Omicron Variant Strains via the Multivariate Regression Method. Health Information Science: 12th International Conference, HIS 2023, Melbourne, VIC, Australia, October 23–24, 2023, Proceedings. editor / Yan Li ; Zhisheng Huang ; Manik Sharma ; Lu Chen ; Rui Zhou. Springer Science and Business Media Deutschland GmbH, 2023. pp. 161-174 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Understanding the Influence of Multiple Factors on the Spread of Omicron Variant Strains via the Multivariate Regression Method",

abstract = "The Omicron variant of SARS-CoV-2, emerging in November 2021, has rapidly spread worldwide due to its high transmissibility and ability to evade vaccines. It is still not fully under control, and there is a need to enhance our scientific understanding of the Omicron variant. Investigating the influencing factors and the correlated characteristics of the transmission of the Omicron variant remains an important issue in COVID-19 prevention and control. This study utilized data from various sources to investigate Omicron{\textquoteright}s transmission factors. Focusing on populous countries like China, France, and the US, a multiple regression model was optimized through the Gauss-Newton method to reveal links between daily Omicron cases and variables like climate, population, healthcare, and vaccination and etc. Results showed vaccination rates, healthcare facility numbers, and population density as pivotal factors influencing transmission. Higher vaccination rates and more healthcare facilities correlated with lower Omicron transmission, while dense population areas experienced higher spread. These findings hold significance for guiding public health decisions and shaping vaccination strategies amidst the Omicron variant{\textquoteright}s ongoing impact.",

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Xu, Z, Lin, S, Huang, Z & Fu, Y 2023, Understanding the Influence of Multiple Factors on the Spread of Omicron Variant Strains via the Multivariate Regression Method. in Y Li, Z Huang, M Sharma, L Chen & R Zhou (eds), Health Information Science: 12th International Conference, HIS 2023, Melbourne, VIC, Australia, October 23–24, 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14305 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 161-174, 12th International Conference on Health Information Science, HIS 2023, Melbourne, Australia, 23/10/23. https://doi.org/10.1007/978-981-99-7108-4_14

Understanding the Influence of Multiple Factors on the Spread of Omicron Variant Strains via the Multivariate Regression Method. / Xu, Zhenkai; Lin, Shaofu; Huang, Zhisheng et al.
Health Information Science: 12th International Conference, HIS 2023, Melbourne, VIC, Australia, October 23–24, 2023, Proceedings. ed. / Yan Li; Zhisheng Huang; Manik Sharma; Lu Chen; Rui Zhou. Springer Science and Business Media Deutschland GmbH, 2023. p. 161-174 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14305 LNCS).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Understanding the Influence of Multiple Factors on the Spread of Omicron Variant Strains via the Multivariate Regression Method

AU - Xu, Zhenkai

AU - Lin, Shaofu

AU - Huang, Zhisheng

AU - Fu, Yu

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

PY - 2023

Y1 - 2023

N2 - The Omicron variant of SARS-CoV-2, emerging in November 2021, has rapidly spread worldwide due to its high transmissibility and ability to evade vaccines. It is still not fully under control, and there is a need to enhance our scientific understanding of the Omicron variant. Investigating the influencing factors and the correlated characteristics of the transmission of the Omicron variant remains an important issue in COVID-19 prevention and control. This study utilized data from various sources to investigate Omicron’s transmission factors. Focusing on populous countries like China, France, and the US, a multiple regression model was optimized through the Gauss-Newton method to reveal links between daily Omicron cases and variables like climate, population, healthcare, and vaccination and etc. Results showed vaccination rates, healthcare facility numbers, and population density as pivotal factors influencing transmission. Higher vaccination rates and more healthcare facilities correlated with lower Omicron transmission, while dense population areas experienced higher spread. These findings hold significance for guiding public health decisions and shaping vaccination strategies amidst the Omicron variant’s ongoing impact.

AB - The Omicron variant of SARS-CoV-2, emerging in November 2021, has rapidly spread worldwide due to its high transmissibility and ability to evade vaccines. It is still not fully under control, and there is a need to enhance our scientific understanding of the Omicron variant. Investigating the influencing factors and the correlated characteristics of the transmission of the Omicron variant remains an important issue in COVID-19 prevention and control. This study utilized data from various sources to investigate Omicron’s transmission factors. Focusing on populous countries like China, France, and the US, a multiple regression model was optimized through the Gauss-Newton method to reveal links between daily Omicron cases and variables like climate, population, healthcare, and vaccination and etc. Results showed vaccination rates, healthcare facility numbers, and population density as pivotal factors influencing transmission. Higher vaccination rates and more healthcare facilities correlated with lower Omicron transmission, while dense population areas experienced higher spread. These findings hold significance for guiding public health decisions and shaping vaccination strategies amidst the Omicron variant’s ongoing impact.

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Xu Z, Lin S, Huang Z, Fu Y. Understanding the Influence of Multiple Factors on the Spread of Omicron Variant Strains via the Multivariate Regression Method. In Li Y, Huang Z, Sharma M, Chen L, Zhou R, editors, Health Information Science: 12th International Conference, HIS 2023, Melbourne, VIC, Australia, October 23–24, 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 161-174. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2023 Oct 11. doi: 10.1007/978-981-99-7108-4_14

Understanding the Influence of Multiple Factors on the Spread of Omicron Variant Strains via the Multivariate Regression Method

Abstract

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Conference

Bibliographical note

Keywords

Access to Document

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