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The influence of weather conditions on the COVID-19 epidemic: Evidence from 279 prefecture-level panel data in China
Studying the influence of weather conditions on the COVID-19 epidemic is an emerging field. However, existing studies in this area tend to utilize time-series data, which have certain limitations and fail to consider individual, social, and economic factors. Therefore, this study aimed to fill this...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Inc.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8536487/ https://www.ncbi.nlm.nih.gov/pubmed/34695427 http://dx.doi.org/10.1016/j.envres.2021.112272 |
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author | Lin, Ruofei Wang, Xiaoli Huang, Junpei |
author_facet | Lin, Ruofei Wang, Xiaoli Huang, Junpei |
author_sort | Lin, Ruofei |
collection | PubMed |
description | Studying the influence of weather conditions on the COVID-19 epidemic is an emerging field. However, existing studies in this area tend to utilize time-series data, which have certain limitations and fail to consider individual, social, and economic factors. Therefore, this study aimed to fill this gap. In this paper, we explored the influence of weather conditions on the COVID-19 epidemic using COVID-19-related prefecture-daily panel data collected in mainland China between January 1, 2020, and February 19, 2020. A two-way fixed effect model was applied taking into account factors including public health measures, effective distance to Wuhan, population density, economic development level, health, and medical conditions. We also used a piecewise linear regression to determine the relationship in detail. We found that there is a conditional negative relationship between weather conditions and the epidemic. Each 1 °C rise in mean temperature led to a 0.49% increase in the confirmed cases growth rate when mean temperature was above −7 °C. Similarly, when the relative humidity was greater than 46%, it was negatively correlated with the epidemic, where a 1% increase in relative humidity decreased the rate of confirmed cases by 0.19%. Furthermore, prefecture-level administrative regions, such as Chifeng (included as “warning cities”) have more days of “dangerous weather”, which is favorable for outbreaks. In addition, we found that the impact of mean temperature is greatest in the east, the influence of relative humidity is most pronounced in the central region, and the significance of weather conditions is more important in the coastal region. Finally, we found that rising diurnal temperatures decreased the negative impact of weather conditions on the spread of COVID-19. We also observed that strict public health measures and high social concern can mitigate the adverse effects of cold and dry weather on the spread of the epidemic. To the best of our knowledge, this is the first study which applies the two-way fixed effect model to investigate the influence of weather conditions on the COVID-19 epidemic, takes into account socio-economic factors and draws new conclusions. |
format | Online Article Text |
id | pubmed-8536487 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85364872021-10-25 The influence of weather conditions on the COVID-19 epidemic: Evidence from 279 prefecture-level panel data in China Lin, Ruofei Wang, Xiaoli Huang, Junpei Environ Res Article Studying the influence of weather conditions on the COVID-19 epidemic is an emerging field. However, existing studies in this area tend to utilize time-series data, which have certain limitations and fail to consider individual, social, and economic factors. Therefore, this study aimed to fill this gap. In this paper, we explored the influence of weather conditions on the COVID-19 epidemic using COVID-19-related prefecture-daily panel data collected in mainland China between January 1, 2020, and February 19, 2020. A two-way fixed effect model was applied taking into account factors including public health measures, effective distance to Wuhan, population density, economic development level, health, and medical conditions. We also used a piecewise linear regression to determine the relationship in detail. We found that there is a conditional negative relationship between weather conditions and the epidemic. Each 1 °C rise in mean temperature led to a 0.49% increase in the confirmed cases growth rate when mean temperature was above −7 °C. Similarly, when the relative humidity was greater than 46%, it was negatively correlated with the epidemic, where a 1% increase in relative humidity decreased the rate of confirmed cases by 0.19%. Furthermore, prefecture-level administrative regions, such as Chifeng (included as “warning cities”) have more days of “dangerous weather”, which is favorable for outbreaks. In addition, we found that the impact of mean temperature is greatest in the east, the influence of relative humidity is most pronounced in the central region, and the significance of weather conditions is more important in the coastal region. Finally, we found that rising diurnal temperatures decreased the negative impact of weather conditions on the spread of COVID-19. We also observed that strict public health measures and high social concern can mitigate the adverse effects of cold and dry weather on the spread of the epidemic. To the best of our knowledge, this is the first study which applies the two-way fixed effect model to investigate the influence of weather conditions on the COVID-19 epidemic, takes into account socio-economic factors and draws new conclusions. Elsevier Inc. 2022-04-15 2021-10-23 /pmc/articles/PMC8536487/ /pubmed/34695427 http://dx.doi.org/10.1016/j.envres.2021.112272 Text en © 2021 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Lin, Ruofei Wang, Xiaoli Huang, Junpei The influence of weather conditions on the COVID-19 epidemic: Evidence from 279 prefecture-level panel data in China |
title | The influence of weather conditions on the COVID-19 epidemic: Evidence from 279 prefecture-level panel data in China |
title_full | The influence of weather conditions on the COVID-19 epidemic: Evidence from 279 prefecture-level panel data in China |
title_fullStr | The influence of weather conditions on the COVID-19 epidemic: Evidence from 279 prefecture-level panel data in China |
title_full_unstemmed | The influence of weather conditions on the COVID-19 epidemic: Evidence from 279 prefecture-level panel data in China |
title_short | The influence of weather conditions on the COVID-19 epidemic: Evidence from 279 prefecture-level panel data in China |
title_sort | influence of weather conditions on the covid-19 epidemic: evidence from 279 prefecture-level panel data in china |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8536487/ https://www.ncbi.nlm.nih.gov/pubmed/34695427 http://dx.doi.org/10.1016/j.envres.2021.112272 |
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