Cargando…

Evaluation of the effects of meteorological factors on COVID-19 prevalence by the distributed lag nonlinear model

BACKGROUND: Although numerous studies have explored the impact of meteorological factors on the epidemic of COVID-19, their relationship remains controversial and needs to be clarified. METHODS: We assessed the risk effect of various meteorological factors on COVID-19 infection using the distributed...

Descripción completa

Detalles Bibliográficos
Autores principales: Ai, Hongjing, Nie, Rongfang, Wang, Xiaosheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8995909/
https://www.ncbi.nlm.nih.gov/pubmed/35410263
http://dx.doi.org/10.1186/s12967-022-03371-1
_version_ 1784684385564360704
author Ai, Hongjing
Nie, Rongfang
Wang, Xiaosheng
author_facet Ai, Hongjing
Nie, Rongfang
Wang, Xiaosheng
author_sort Ai, Hongjing
collection PubMed
description BACKGROUND: Although numerous studies have explored the impact of meteorological factors on the epidemic of COVID-19, their relationship remains controversial and needs to be clarified. METHODS: We assessed the risk effect of various meteorological factors on COVID-19 infection using the distributed lag nonlinear model, based on related data from July 1, 2020, to June 30, 2021, in eight countries, including Portugal, Greece, Egypt, South Africa, Paraguay, Uruguay, South Korea, and Japan, which are in Europe, Africa, South America, and Asia, respectively. We also explored associations between COVID-19 prevalence and individual meteorological factors by the Spearman’s rank correlation test. RESULTS: There were significant non-linear relationships between both temperature and relative humidity and COVID-19 prevalence. In the countries located in the Northern Hemisphere with similar latitudes, the risk of COVID-19 infection was the highest at temperature below 5 ℃. In the countries located in the Southern Hemisphere with similar latitudes, their highest infection risk occurred at around 15 ℃. Nevertheless, in most countries, high temperature showed no significant association with reduced risk of COVID-19 infection. The effect pattern of relative humidity on COVID-19 depended on the range of its variation in countries. Overall, low relative humidity was correlated with increased risk of COVID-19 infection, while the high risk of infection at extremely high relative humidity could occur in some countries. In addition, relative humidity had a longer lag effect on COVID-19 than temperature. CONCLUSIONS: The effects of meteorological factors on COVID-19 prevalence are nonlinear and hysteretic. Although low temperature and relative humidity may lower the risk of COVID-19, high temperature or relative humidity could also be associated with a high prevalence of COVID-19 in some regions.
format Online
Article
Text
id pubmed-8995909
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-89959092022-04-11 Evaluation of the effects of meteorological factors on COVID-19 prevalence by the distributed lag nonlinear model Ai, Hongjing Nie, Rongfang Wang, Xiaosheng J Transl Med Research BACKGROUND: Although numerous studies have explored the impact of meteorological factors on the epidemic of COVID-19, their relationship remains controversial and needs to be clarified. METHODS: We assessed the risk effect of various meteorological factors on COVID-19 infection using the distributed lag nonlinear model, based on related data from July 1, 2020, to June 30, 2021, in eight countries, including Portugal, Greece, Egypt, South Africa, Paraguay, Uruguay, South Korea, and Japan, which are in Europe, Africa, South America, and Asia, respectively. We also explored associations between COVID-19 prevalence and individual meteorological factors by the Spearman’s rank correlation test. RESULTS: There were significant non-linear relationships between both temperature and relative humidity and COVID-19 prevalence. In the countries located in the Northern Hemisphere with similar latitudes, the risk of COVID-19 infection was the highest at temperature below 5 ℃. In the countries located in the Southern Hemisphere with similar latitudes, their highest infection risk occurred at around 15 ℃. Nevertheless, in most countries, high temperature showed no significant association with reduced risk of COVID-19 infection. The effect pattern of relative humidity on COVID-19 depended on the range of its variation in countries. Overall, low relative humidity was correlated with increased risk of COVID-19 infection, while the high risk of infection at extremely high relative humidity could occur in some countries. In addition, relative humidity had a longer lag effect on COVID-19 than temperature. CONCLUSIONS: The effects of meteorological factors on COVID-19 prevalence are nonlinear and hysteretic. Although low temperature and relative humidity may lower the risk of COVID-19, high temperature or relative humidity could also be associated with a high prevalence of COVID-19 in some regions. BioMed Central 2022-04-11 /pmc/articles/PMC8995909/ /pubmed/35410263 http://dx.doi.org/10.1186/s12967-022-03371-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Ai, Hongjing
Nie, Rongfang
Wang, Xiaosheng
Evaluation of the effects of meteorological factors on COVID-19 prevalence by the distributed lag nonlinear model
title Evaluation of the effects of meteorological factors on COVID-19 prevalence by the distributed lag nonlinear model
title_full Evaluation of the effects of meteorological factors on COVID-19 prevalence by the distributed lag nonlinear model
title_fullStr Evaluation of the effects of meteorological factors on COVID-19 prevalence by the distributed lag nonlinear model
title_full_unstemmed Evaluation of the effects of meteorological factors on COVID-19 prevalence by the distributed lag nonlinear model
title_short Evaluation of the effects of meteorological factors on COVID-19 prevalence by the distributed lag nonlinear model
title_sort evaluation of the effects of meteorological factors on covid-19 prevalence by the distributed lag nonlinear model
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8995909/
https://www.ncbi.nlm.nih.gov/pubmed/35410263
http://dx.doi.org/10.1186/s12967-022-03371-1
work_keys_str_mv AT aihongjing evaluationoftheeffectsofmeteorologicalfactorsoncovid19prevalencebythedistributedlagnonlinearmodel
AT nierongfang evaluationoftheeffectsofmeteorologicalfactorsoncovid19prevalencebythedistributedlagnonlinearmodel
AT wangxiaosheng evaluationoftheeffectsofmeteorologicalfactorsoncovid19prevalencebythedistributedlagnonlinearmodel