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Spatiotemporal analysis of the morbidity of global Omicron from November 2021 to February 2022
The Omicron variant was first reported to the World Health Organization (WHO) from South Africa on November 24, 2021; this variant is spreading rapidly worldwide. No study has conducted a spatiotemporal analysis of the morbidity of Omicron infection at the country level; hence, to explore the spatia...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9544667/ https://www.ncbi.nlm.nih.gov/pubmed/35864556 http://dx.doi.org/10.1002/jmv.28013 |
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author | Liang, Yuelang Gong, Zijun Guo, Jiajia Cheng, Qi Yao, Zhenjiang |
author_facet | Liang, Yuelang Gong, Zijun Guo, Jiajia Cheng, Qi Yao, Zhenjiang |
author_sort | Liang, Yuelang |
collection | PubMed |
description | The Omicron variant was first reported to the World Health Organization (WHO) from South Africa on November 24, 2021; this variant is spreading rapidly worldwide. No study has conducted a spatiotemporal analysis of the morbidity of Omicron infection at the country level; hence, to explore the spatial transmission of the Omicron variant among the 220 countries worldwide, we aimed to the analyze its spatial autocorrelation and to conduct a multiple linear regression to investigate the underlying factors associated with the pandemic. This study was an ecological study. Data on the number of confirmed cases were extracted from the WHO website. The spatiotemporal characteristic was described in a thematic map. The Global Moran Index (Moran's I) was used to detect the spatial autocorrelation, while the local indicators of spatial association (LISA) were used to analyze the local spatial correlation characteristics. The joinpoint regression model was used to explore the change in the trend of the Omicron incidence over time. The association between the morbidity of Omicron and influencing factors were analyzed using multiple linear regression. This study was an ecological study. Data on the number of confirmed cases were extracted from the WHO website. The spatiotemporal characteristic was described in a thematic map. The Global Moran Index (Moran's I) was used to detect the spatial autocorrelation, while the LISA were used to analyze the local spatial correlation characteristics. The joinpoint regression model was used to explore the change in the trend of the Omicron incidence over time. The association between the morbidity of Omicron and influencing factors were analyzed using multiple linear regression. The value of Moran's I was positive (Moran's I = 0.061, Z‐score = 3.772, p = 0.007), indicating a spatial correlation of the morbidity of Omicron at the country level. From November 26, 2021 to February 26, 2022; the morbidity showed obvious spatial clustering. Hotspot clustering was observed mostly in Europe (locations in High–High category: 24). Coldspot clustering was observed mostly in Africa and Asia (locations in Low−Low category: 32). The result of joinpoint regression showed an increasing trend from December 21, 2021 to January 26, 2022. Results of the multiple linear regression analysis demonstrated that the morbidity of Omicron was strongly positively correlated with income support (coefficient = 1.905, 95% confidence interval [CI]: 1.354–2.456, p < 0.001) and strongly negatively correlated with close public transport (coefficient = −1.591, 95% CI: −2.461 to −0.721, p = 0.001). Omicron outbreaks exhibited spatial clustering at the country level worldwide; the countries with higher disease morbidity could impact the other countries that are surrounded by and close to it. The locations with High–High clustering category, which referred to the countries with higher disease morbidity, were mainly observed in Europe, and its adjoining country also showed high spatial clustering. The morbidity of Omicron increased from December 21, 2021 to January 26, 2022. The higher morbidity of Omicron was associated with the economic and policy interventions implemented; hence, to deal with the epidemic, the prevention and control measures should be strengthened in all aspects. |
format | Online Article Text |
id | pubmed-9544667 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95446672022-10-14 Spatiotemporal analysis of the morbidity of global Omicron from November 2021 to February 2022 Liang, Yuelang Gong, Zijun Guo, Jiajia Cheng, Qi Yao, Zhenjiang J Med Virol Research Articles The Omicron variant was first reported to the World Health Organization (WHO) from South Africa on November 24, 2021; this variant is spreading rapidly worldwide. No study has conducted a spatiotemporal analysis of the morbidity of Omicron infection at the country level; hence, to explore the spatial transmission of the Omicron variant among the 220 countries worldwide, we aimed to the analyze its spatial autocorrelation and to conduct a multiple linear regression to investigate the underlying factors associated with the pandemic. This study was an ecological study. Data on the number of confirmed cases were extracted from the WHO website. The spatiotemporal characteristic was described in a thematic map. The Global Moran Index (Moran's I) was used to detect the spatial autocorrelation, while the local indicators of spatial association (LISA) were used to analyze the local spatial correlation characteristics. The joinpoint regression model was used to explore the change in the trend of the Omicron incidence over time. The association between the morbidity of Omicron and influencing factors were analyzed using multiple linear regression. This study was an ecological study. Data on the number of confirmed cases were extracted from the WHO website. The spatiotemporal characteristic was described in a thematic map. The Global Moran Index (Moran's I) was used to detect the spatial autocorrelation, while the LISA were used to analyze the local spatial correlation characteristics. The joinpoint regression model was used to explore the change in the trend of the Omicron incidence over time. The association between the morbidity of Omicron and influencing factors were analyzed using multiple linear regression. The value of Moran's I was positive (Moran's I = 0.061, Z‐score = 3.772, p = 0.007), indicating a spatial correlation of the morbidity of Omicron at the country level. From November 26, 2021 to February 26, 2022; the morbidity showed obvious spatial clustering. Hotspot clustering was observed mostly in Europe (locations in High–High category: 24). Coldspot clustering was observed mostly in Africa and Asia (locations in Low−Low category: 32). The result of joinpoint regression showed an increasing trend from December 21, 2021 to January 26, 2022. Results of the multiple linear regression analysis demonstrated that the morbidity of Omicron was strongly positively correlated with income support (coefficient = 1.905, 95% confidence interval [CI]: 1.354–2.456, p < 0.001) and strongly negatively correlated with close public transport (coefficient = −1.591, 95% CI: −2.461 to −0.721, p = 0.001). Omicron outbreaks exhibited spatial clustering at the country level worldwide; the countries with higher disease morbidity could impact the other countries that are surrounded by and close to it. The locations with High–High clustering category, which referred to the countries with higher disease morbidity, were mainly observed in Europe, and its adjoining country also showed high spatial clustering. The morbidity of Omicron increased from December 21, 2021 to January 26, 2022. The higher morbidity of Omicron was associated with the economic and policy interventions implemented; hence, to deal with the epidemic, the prevention and control measures should be strengthened in all aspects. John Wiley and Sons Inc. 2022-07-29 2022-11 /pmc/articles/PMC9544667/ /pubmed/35864556 http://dx.doi.org/10.1002/jmv.28013 Text en © 2022 The Authors. Journal of Medical Virology published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Research Articles Liang, Yuelang Gong, Zijun Guo, Jiajia Cheng, Qi Yao, Zhenjiang Spatiotemporal analysis of the morbidity of global Omicron from November 2021 to February 2022 |
title | Spatiotemporal analysis of the morbidity of global Omicron from November 2021 to February 2022 |
title_full | Spatiotemporal analysis of the morbidity of global Omicron from November 2021 to February 2022 |
title_fullStr | Spatiotemporal analysis of the morbidity of global Omicron from November 2021 to February 2022 |
title_full_unstemmed | Spatiotemporal analysis of the morbidity of global Omicron from November 2021 to February 2022 |
title_short | Spatiotemporal analysis of the morbidity of global Omicron from November 2021 to February 2022 |
title_sort | spatiotemporal analysis of the morbidity of global omicron from november 2021 to february 2022 |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9544667/ https://www.ncbi.nlm.nih.gov/pubmed/35864556 http://dx.doi.org/10.1002/jmv.28013 |
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