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Guo Y, Li B, Duan T, Yao N, Wang H, Yang Y, Yan S, Sun M, Wang L, Yao Y, Sun Y, Jia J, Liu S. A panel regression analysis for the COVID-19 epidemic in the United States. PLoS One 2022; 17:e0273344. [PMID: 35984832 PMCID: PMC9390909 DOI: 10.1371/journal.pone.0273344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 08/05/2022] [Indexed: 11/19/2022] Open
Abstract
This study explored the roles of epidemic-spread-related behaviors, vaccination status and weather factors during the COVID-19 epidemic in 50 U.S. states since March 2020. Data from March 1, 2020 to February 5, 2022 were incorporated into panel model. The states were clustered by the k-means method. In addition to discussing the whole time period, we also took multiple events nodes into account and analyzed the data in different time periods respectively by panel linear regression method. In addition, influence of cluster grouping and different incubation periods were been discussed. Non-segmented analysis showed the rate of people staying at home and the vaccination dose per capita were significantly negatively correlated with the daily incidence rate, while the number of long-distance trips was positively correlated. Weather indicators also had a negative effect to a certain extent. Most segmental results support the above view. The vaccination dose per capita was unsurprisingly proved to be the most significant factor especially for epidemic dominated by Omicron strains. 7-day was a more robust incubation period with the best model fit while weather had different effects on the epidemic spread in different time period. The implementation of prevention behaviors and the promotion of vaccination may have a successful control effect on COVID-19, including variants’ epidemic such as Omicron. The spread of COVID-19 also might be associated with weather, albeit to a lesser extent.
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Affiliation(s)
- Yinpei Guo
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Bo Li
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Tonghua Duan
- Department of Computational Mathematics, School of Mathematics, Jilin University, Changchun, China
| | - Nan Yao
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Han Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Yixue Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Shoumeng Yan
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Mengzi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Ling Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Yan Yao
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Yuchen Sun
- Jilin National Applied Mathematical Center, Jilin University, Changchun, China
| | - Jiwei Jia
- Department of Computational Mathematics, School of Mathematics, Jilin University, Changchun, China
- Jilin National Applied Mathematical Center, Jilin University, Changchun, China
- * E-mail: (SL); (JJ)
| | - Siyu Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
- * E-mail: (SL); (JJ)
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