Association between the new COVID-19 cases and air pollution with meteorological elements in nine counties of New York state
Association between the new COVID-19 cases and air pollution with meteorological elements in nine counties of New York state
The principal objective of this article is to assess the possible association between the number of COVID-19 infected cases and the concentrations of fine particulate matter (PM2.5) and ozone (O3), atmospheric pollutants related to people’s mobility in urban areas, taking also into account the effect of meteorological conditions. We fit a generalized linear mixed model which includes spatial and temporal terms in order to detect the effect of the meteorological elements and COVID-19 infected cases on the pollutant concentrations. We consider nine counties of the state of New York which registered the highest number of COVID-19 infected cases. We implemented a Bayesian method using integrated nested Laplace approximation (INLA) with a stochastic partial differential equation (SPDE). The results emphasize that all the components used in designing the model contribute to improving the predicted values and can be included in designing similar real-world data (RWD) models. We found only a weak association between PM2.5 and ozone concentrations with COVID-19 infected cases. Records of COVID-19 infected cases and other covariates data from March to May 2020 were collected from electronic health records (EHRs) and standard RWD sources.
Diaz-Avalos, Carlos
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Juan, Pablo
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Chaudhuri, Somnath
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Saez, Marc
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Serra, Laura
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4 December 2020
Diaz-Avalos, Carlos
c3d206d1-9a32-4e8c-88ab-0b7f2cb18e30
Juan, Pablo
f3648398-5752-4dd0-9410-835566b659f4
Chaudhuri, Somnath
ae0507e0-f920-4438-bc9f-ecdd5ac8967a
Saez, Marc
8e1a1aa0-d45d-4a7a-8de8-1ac0f8561c51
Serra, Laura
95c48bad-cb5f-4364-b70c-9d27c5aeb6e5
Diaz-Avalos, Carlos, Juan, Pablo, Chaudhuri, Somnath, Saez, Marc and Serra, Laura
(2020)
Association between the new COVID-19 cases and air pollution with meteorological elements in nine counties of New York state.
International Journal of Environmental Research and Public Health, 17 (23), [9055].
(doi:10.3390/ijerph17239055).
Abstract
The principal objective of this article is to assess the possible association between the number of COVID-19 infected cases and the concentrations of fine particulate matter (PM2.5) and ozone (O3), atmospheric pollutants related to people’s mobility in urban areas, taking also into account the effect of meteorological conditions. We fit a generalized linear mixed model which includes spatial and temporal terms in order to detect the effect of the meteorological elements and COVID-19 infected cases on the pollutant concentrations. We consider nine counties of the state of New York which registered the highest number of COVID-19 infected cases. We implemented a Bayesian method using integrated nested Laplace approximation (INLA) with a stochastic partial differential equation (SPDE). The results emphasize that all the components used in designing the model contribute to improving the predicted values and can be included in designing similar real-world data (RWD) models. We found only a weak association between PM2.5 and ozone concentrations with COVID-19 infected cases. Records of COVID-19 infected cases and other covariates data from March to May 2020 were collected from electronic health records (EHRs) and standard RWD sources.
Text
ijerph-17-09055-v3
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Accepted/In Press date: 2 December 2020
Published date: 4 December 2020
Identifiers
Local EPrints ID: 502534
URI: http://eprints.soton.ac.uk/id/eprint/502534
ISSN: 1660-4601
PURE UUID: 4c657106-2b6a-4136-8ad8-6c1abd54ca53
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Date deposited: 30 Jun 2025 17:41
Last modified: 22 Aug 2025 02:43
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Author:
Carlos Diaz-Avalos
Author:
Pablo Juan
Author:
Somnath Chaudhuri
Author:
Marc Saez
Author:
Laura Serra
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