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The impact of meteorological factors and PM2.5 on COVID-19 transmission
In this study, we analysed the relationship between meteorological factors and the number of patients with coronavirus disease 2019 (COVID-19). The study period was from 12 April 2020 to 13 October 2020, and daily meteorological data and the daily number of patients with COVID-19 in each state of th...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8886088/ https://www.ncbi.nlm.nih.gov/pubmed/35057873 http://dx.doi.org/10.1017/S0950268821002570 |
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author | Zhou, Nan Dai, HaoYun Zha, WenTing Lv, Yuan |
author_facet | Zhou, Nan Dai, HaoYun Zha, WenTing Lv, Yuan |
author_sort | Zhou, Nan |
collection | PubMed |
description | In this study, we analysed the relationship between meteorological factors and the number of patients with coronavirus disease 2019 (COVID-19). The study period was from 12 April 2020 to 13 October 2020, and daily meteorological data and the daily number of patients with COVID-19 in each state of the United States were collected. Based on the number of COVID-19 patients in each state of the United States, we selected four states (California, Florida, New York, Texas) for analysis. One-way analysis of variance ( ANOVA), scatter plot analysis, correlation analysis and distributed lag nonlinear model (DLNM) analysis were used to analyse the relationship between meteorological factors and the number of patients with COVID-19. We found that the significant influencing factors of the number of COVID-19 cases differed among the four states. Specifically, the number of COVID-19 confirmed cases in California and New York was negatively correlated with AWMD (P < 0.01) and positively correlated with AQI, PM2.5 and TAVG (P < 0.01) but not significantly correlated with other factors. Florida was significantly correlated with TAVG (positive) (P < 0.01) but not significantly correlated with other factors. The number of COVID-19 cases in Texas was only significantly negatively associated with AWND (P < 0.01). The influence of temperature and PM2.5 on the spread of COVID-19 is not obvious. This study shows that when the wind speed was 2 m/s, it had a significant positive correlation with COVID-19 cases. The impact of meteorological factors on COVID-19 may be very complicated. It is necessary to further explore the relationship between meteorological factors and COVID-19. By exploring the influence of meteorological factors on COVID-19, we can help people to establish a more accurate early warning system. |
format | Online Article Text |
id | pubmed-8886088 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-88860882022-03-01 The impact of meteorological factors and PM2.5 on COVID-19 transmission Zhou, Nan Dai, HaoYun Zha, WenTing Lv, Yuan Epidemiol Infect Original Paper In this study, we analysed the relationship between meteorological factors and the number of patients with coronavirus disease 2019 (COVID-19). The study period was from 12 April 2020 to 13 October 2020, and daily meteorological data and the daily number of patients with COVID-19 in each state of the United States were collected. Based on the number of COVID-19 patients in each state of the United States, we selected four states (California, Florida, New York, Texas) for analysis. One-way analysis of variance ( ANOVA), scatter plot analysis, correlation analysis and distributed lag nonlinear model (DLNM) analysis were used to analyse the relationship between meteorological factors and the number of patients with COVID-19. We found that the significant influencing factors of the number of COVID-19 cases differed among the four states. Specifically, the number of COVID-19 confirmed cases in California and New York was negatively correlated with AWMD (P < 0.01) and positively correlated with AQI, PM2.5 and TAVG (P < 0.01) but not significantly correlated with other factors. Florida was significantly correlated with TAVG (positive) (P < 0.01) but not significantly correlated with other factors. The number of COVID-19 cases in Texas was only significantly negatively associated with AWND (P < 0.01). The influence of temperature and PM2.5 on the spread of COVID-19 is not obvious. This study shows that when the wind speed was 2 m/s, it had a significant positive correlation with COVID-19 cases. The impact of meteorological factors on COVID-19 may be very complicated. It is necessary to further explore the relationship between meteorological factors and COVID-19. By exploring the influence of meteorological factors on COVID-19, we can help people to establish a more accurate early warning system. Cambridge University Press 2022-01-21 /pmc/articles/PMC8886088/ /pubmed/35057873 http://dx.doi.org/10.1017/S0950268821002570 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. |
spellingShingle | Original Paper Zhou, Nan Dai, HaoYun Zha, WenTing Lv, Yuan The impact of meteorological factors and PM2.5 on COVID-19 transmission |
title | The impact of meteorological factors and PM2.5 on COVID-19 transmission |
title_full | The impact of meteorological factors and PM2.5 on COVID-19 transmission |
title_fullStr | The impact of meteorological factors and PM2.5 on COVID-19 transmission |
title_full_unstemmed | The impact of meteorological factors and PM2.5 on COVID-19 transmission |
title_short | The impact of meteorological factors and PM2.5 on COVID-19 transmission |
title_sort | impact of meteorological factors and pm2.5 on covid-19 transmission |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8886088/ https://www.ncbi.nlm.nih.gov/pubmed/35057873 http://dx.doi.org/10.1017/S0950268821002570 |
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