Assessing the Impacts of Meteorological Factors on COVID-19 Pandemic Using Generalized Estimating Equations
BackgroundMeteorological factors have been proven to affect pathogens; both the transmission routes and other intermediate. Many studies have worked on assessing how those meteorological factors would influence the transmissibility of COVID-19. In this study, we used generalized estimating equations...
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.920312/full |
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| author | Shengnan Lin Jia Rui Jia Rui Fang Xie Meirong Zhan Qiuping Chen Qiuping Chen Bin Zhao Yuanzhao Zhu Zhuoyang Li Bin Deng Shanshan Yu An Li Yanshu Ke Wenwen Zeng Yanhua Su Yi-Chen Chiang Tianmu Chen |
| author_facet | Shengnan Lin Jia Rui Jia Rui Fang Xie Meirong Zhan Qiuping Chen Qiuping Chen Bin Zhao Yuanzhao Zhu Zhuoyang Li Bin Deng Shanshan Yu An Li Yanshu Ke Wenwen Zeng Yanhua Su Yi-Chen Chiang Tianmu Chen |
| author_sort | Shengnan Lin |
| collection | DOAJ |
| description | BackgroundMeteorological factors have been proven to affect pathogens; both the transmission routes and other intermediate. Many studies have worked on assessing how those meteorological factors would influence the transmissibility of COVID-19. In this study, we used generalized estimating equations to evaluate the impact of meteorological factors on Coronavirus disease 2019 (COVID-19) by using three outcome variables, which are transmissibility, incidence rate, and the number of reported cases.MethodsIn this study, the data on the daily number of new cases and deaths of COVID-19 in 30 provinces and cities nationwide were obtained from the provincial and municipal health committees, while the data from 682 conventional weather stations in the selected provinces and cities were obtained from the website of the China Meteorological Administration. We built a Susceptible-Exposed-Symptomatic-Asymptomatic-Recovered/Removed (SEIAR) model to fit the data, then we calculated the transmissibility of COVID-19 using an indicator of the effective reproduction number (Reff). To quantify the different impacts of meteorological factors on several outcome variables including transmissibility, incidence rate, and the number of reported cases of COVID-19, we collected panel data and used generalized estimating equations. We also explored whether there is a lag effect and the different times of meteorological factors on the three outcome variables.ResultsPrecipitation and wind speed had a negative effect on transmissibility, incidence rate, and the number of reported cases, while humidity had a positive effect on them. The higher the temperature, the lower the transmissibility. The temperature had a lag effect on the incidence rate, while the remaining five meteorological factors had immediate and lag effects on the incidence rate and the number of reported cases.ConclusionMeteorological factors had similar effects on incidence rate and number of reported cases, but different effects on transmissibility. Temperature, relative humidity, precipitation, sunshine hours, and wind speed had immediate and lag effects on transmissibility, but with different lag times. An increase in temperature may first cause a decrease in virus transmissibility and then lead to a decrease in incidence rate. Also, the mechanism of the role of meteorological factors in the process of transmissibility to incidence rate needs to be further explored. |
| first_indexed | 2024-04-13T12:30:32Z |
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| language | English |
| last_indexed | 2024-04-13T12:30:32Z |
| publishDate | 2022-07-01 |
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| series | Frontiers in Public Health |
| spelling | doaj.art-3e74b885e2184a6eb37a962d189f6ce72022-12-22T02:46:51ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-07-011010.3389/fpubh.2022.920312920312Assessing the Impacts of Meteorological Factors on COVID-19 Pandemic Using Generalized Estimating EquationsShengnan Lin0Jia Rui1Jia Rui2Fang Xie3Meirong Zhan4Qiuping Chen5Qiuping Chen6Bin Zhao7Yuanzhao Zhu8Zhuoyang Li9Bin Deng10Shanshan Yu11An Li12Yanshu Ke13Wenwen Zeng14Yanhua Su15Yi-Chen Chiang16Tianmu Chen17School of Public Health, Xiamen University, Xiamen, ChinaSchool of Public Health, Xiamen University, Xiamen, ChinaCirad, UMR 17, Intertryp, Université de Montpellier, Montpellier, FranceSchool of Public Health, Xiamen University, Xiamen, ChinaFujian Provincial Center for Disease Control and Prevention, Fuzhou, ChinaSchool of Public Health, Xiamen University, Xiamen, ChinaCirad, UMR 17, Intertryp, Université de Montpellier, Montpellier, FranceClinical Medical Laboratory, Xiang'an Hospital of Xiamen University, Xiamen, ChinaSchool of Public Health, Xiamen University, Xiamen, ChinaSchool of Public Health, Xiamen University, Xiamen, ChinaSchool of Public Health, Xiamen University, Xiamen, ChinaSchool of Public Health, Xiamen University, Xiamen, ChinaSchool of Public Health, Xiamen University, Xiamen, ChinaSchool of Public Health, Xiamen University, Xiamen, ChinaSchool of Public Health, Xiamen University, Xiamen, ChinaSchool of Public Health, Xiamen University, Xiamen, ChinaSchool of Public Health, Xiamen University, Xiamen, ChinaSchool of Public Health, Xiamen University, Xiamen, ChinaBackgroundMeteorological factors have been proven to affect pathogens; both the transmission routes and other intermediate. Many studies have worked on assessing how those meteorological factors would influence the transmissibility of COVID-19. In this study, we used generalized estimating equations to evaluate the impact of meteorological factors on Coronavirus disease 2019 (COVID-19) by using three outcome variables, which are transmissibility, incidence rate, and the number of reported cases.MethodsIn this study, the data on the daily number of new cases and deaths of COVID-19 in 30 provinces and cities nationwide were obtained from the provincial and municipal health committees, while the data from 682 conventional weather stations in the selected provinces and cities were obtained from the website of the China Meteorological Administration. We built a Susceptible-Exposed-Symptomatic-Asymptomatic-Recovered/Removed (SEIAR) model to fit the data, then we calculated the transmissibility of COVID-19 using an indicator of the effective reproduction number (Reff). To quantify the different impacts of meteorological factors on several outcome variables including transmissibility, incidence rate, and the number of reported cases of COVID-19, we collected panel data and used generalized estimating equations. We also explored whether there is a lag effect and the different times of meteorological factors on the three outcome variables.ResultsPrecipitation and wind speed had a negative effect on transmissibility, incidence rate, and the number of reported cases, while humidity had a positive effect on them. The higher the temperature, the lower the transmissibility. The temperature had a lag effect on the incidence rate, while the remaining five meteorological factors had immediate and lag effects on the incidence rate and the number of reported cases.ConclusionMeteorological factors had similar effects on incidence rate and number of reported cases, but different effects on transmissibility. Temperature, relative humidity, precipitation, sunshine hours, and wind speed had immediate and lag effects on transmissibility, but with different lag times. An increase in temperature may first cause a decrease in virus transmissibility and then lead to a decrease in incidence rate. Also, the mechanism of the role of meteorological factors in the process of transmissibility to incidence rate needs to be further explored.https://www.frontiersin.org/articles/10.3389/fpubh.2022.920312/fullCOVID-19meteorological factorstransmissibilitygeneralized estimating equationslagged effect |
| spellingShingle | Shengnan Lin Jia Rui Jia Rui Fang Xie Meirong Zhan Qiuping Chen Qiuping Chen Bin Zhao Yuanzhao Zhu Zhuoyang Li Bin Deng Shanshan Yu An Li Yanshu Ke Wenwen Zeng Yanhua Su Yi-Chen Chiang Tianmu Chen Assessing the Impacts of Meteorological Factors on COVID-19 Pandemic Using Generalized Estimating Equations Frontiers in Public Health COVID-19 meteorological factors transmissibility generalized estimating equations lagged effect |
| title | Assessing the Impacts of Meteorological Factors on COVID-19 Pandemic Using Generalized Estimating Equations |
| title_full | Assessing the Impacts of Meteorological Factors on COVID-19 Pandemic Using Generalized Estimating Equations |
| title_fullStr | Assessing the Impacts of Meteorological Factors on COVID-19 Pandemic Using Generalized Estimating Equations |
| title_full_unstemmed | Assessing the Impacts of Meteorological Factors on COVID-19 Pandemic Using Generalized Estimating Equations |
| title_short | Assessing the Impacts of Meteorological Factors on COVID-19 Pandemic Using Generalized Estimating Equations |
| title_sort | assessing the impacts of meteorological factors on covid 19 pandemic using generalized estimating equations |
| topic | COVID-19 meteorological factors transmissibility generalized estimating equations lagged effect |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.920312/full |
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