EMS Annual Meeting Abstracts
Vol. 18, EMS2021-110, 2021, updated on 02 Sep 2021
https://doi.org/10.5194/ems2021-110
EMS Annual Meeting 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Association between COVID-19, mobility and environment in Brazilian capitals

Sergio Ibarra and Edmilson Dias de Freitas
Sergio Ibarra and Edmilson Dias de Freitas
  • University of Sao Paulo, Intitute of Astronomy, Geophysics and Atmospheric Sciences, Atmospheric Sciences, São Paulo, Brazil (sergio.ibarra@usp.br)

Brazil is the country with the highest number of COVID-19 cases and deaths in the southern hemisphere, third behind India and U.S globally. Some studies have analyzed the relationship between mobility, meteorology and air pollution, finding that staying out-of-home increases cases about 5 days and deaths about two weeks after the exposure (Ibarra-Espinosa, et al., 2021). In this work we will extend the analyses presented by Ibarra-Espinosa et al., (2021), by including more Brazilian cities. Specifically, the metropolitan region of Rio de Janeiro is considered a Megacity and monitors meteorology and air pollution, necessary to the analyses. The metropolitan regions of Porto Alegre, Belo Horizonte and Curitiba as well. The method consists in applying a semiparametric model (Dominici et al, 2004), but in this case, controllying all the environmental factors and their interactions and the parameter consists in the mobility alone. We will compare local mobility index, as Google Residential Mobility Index (RMI), as done by Ibarra-Espinosa et al., (2021). Due to the high dispersion of the data, COVID-19 will be modeled by quasi-poisson and negative binomial distribution, with generalized additive models (Wood., 2017; Zeileis et al., 2008; R Core Team, 2021). 

Ibarra-Espinosa, S., de Freitas, E.D., Ropkins, K., Dominici, F., Rehbein, A., 2021. Association between COVID-19, mobility and environment in São Paulo, Brazil. medRxiv. https://doi.org/10.1101/2021.02.08.21250113 

Dominici F, McDermott A, Hastie TJ. 2004. Improved semiparametric time series models of air pollution and mortality. J Am Stat Assoc 99: 938–948. R Core Team. 2021. R: A Language and Environment for Statistical Computing. 

Wood S. 2017. Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC. 

Zeileis A, Kleiber C, Jackman S. 2008. Regression Models for Count Data in R. J Stat Software, Artic 27:1–25; doi:10.18637/jss.v027.i08. 

How to cite: Ibarra, S. and Dias de Freitas, E.: Association between COVID-19, mobility and environment in Brazilian capitals, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-110, https://doi.org/10.5194/ems2021-110, 2021.

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