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Association between the New COVID-19 Cases and Air Pollution with Meteorological Elements in Nine Counties of New York State

Author

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  • Carlos Díaz-Avalos

    (Department of Probability and Statistics, IIMAS, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico)

  • Pablo Juan

    (Department of Mathematics and IMAC, Universitat Jaume I, Castellón, 12006 Castellón, Spain)

  • Somnath Chaudhuri

    (Department of Mathematics, Universitat Jaume I, 12006 Castellón, Spain)

  • Marc Sáez

    (Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, 17003 Girona, Spain
    CIBER of Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain)

  • Laura Serra

    (Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, 17003 Girona, Spain
    CIBER of Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain)

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 (PM 2.5 ) and ozone (O 3 ), 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 PM 2.5 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.

Suggested Citation

  • Carlos Díaz-Avalos & Pablo Juan & Somnath Chaudhuri & Marc Sáez & Laura Serra, 2020. "Association between the New COVID-19 Cases and Air Pollution with Meteorological Elements in Nine Counties of New York State," IJERPH, MDPI, vol. 17(23), pages 1-18, December.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:23:p:9055-:d:456875
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    References listed on IDEAS

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    1. Lindgren, Finn & Rue, Håvard, 2015. "Bayesian Spatial Modelling with R-INLA," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i19).
    2. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    3. Atanu Sengupta & Sanjoy De, 2020. "Review of Literature," India Studies in Business and Economics, in: Assessing Performance of Banks in India Fifty Years After Nationalization, chapter 0, pages 15-30, Springer.
    4. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    5. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
    6. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
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    Keywords

    COVID-19; INLA; RWD; PM 2.5 ; O 3 ; New York;
    All these keywords.

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