Cargando…

Air transportation, population density and temperature predict the spread of COVID-19 in Brazil

There is evidence that COVID-19, the disease caused by the betacoronavirus SARS-CoV-2, is sensitive to environmental conditions. However, such conditions often correlate with demographic and socioeconomic factors at larger spatial extents, which could confound this inference. We evaluated the effect...

Descripción completa

Detalles Bibliográficos
Autores principales: Pequeno, Pedro, Mendel, Bruna, Rosa, Clarissa, Bosholn, Mariane, Souza, Jorge Luiz, Baccaro, Fabricio, Barbosa, Reinaldo, Magnusson, William
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7275681/
https://www.ncbi.nlm.nih.gov/pubmed/32547889
http://dx.doi.org/10.7717/peerj.9322
_version_ 1783542836328660992
author Pequeno, Pedro
Mendel, Bruna
Rosa, Clarissa
Bosholn, Mariane
Souza, Jorge Luiz
Baccaro, Fabricio
Barbosa, Reinaldo
Magnusson, William
author_facet Pequeno, Pedro
Mendel, Bruna
Rosa, Clarissa
Bosholn, Mariane
Souza, Jorge Luiz
Baccaro, Fabricio
Barbosa, Reinaldo
Magnusson, William
author_sort Pequeno, Pedro
collection PubMed
description There is evidence that COVID-19, the disease caused by the betacoronavirus SARS-CoV-2, is sensitive to environmental conditions. However, such conditions often correlate with demographic and socioeconomic factors at larger spatial extents, which could confound this inference. We evaluated the effect of meteorological conditions (temperature, solar radiation, air humidity and precipitation) on 292 daily records of cumulative number of confirmed COVID-19 cases across the 27 Brazilian capital cities during the 1st month of the outbreak, while controlling for an indicator of the number of tests, the number of arriving flights, population density, proportion of elderly people and average income. Apart from increasing with time, the number of confirmed cases was mainly related to the number of arriving flights and population density, increasing with both factors. However, after accounting for these effects, the disease was shown to be temperature sensitive: there were more cases in colder cities and days, and cases accumulated faster at lower temperatures. Our best estimate indicates that a 1 °C increase in temperature has been associated with a decrease in confirmed cases of 8%. The quality of the data and unknowns limit the analysis, but the study reveals an urgent need to understand more about the environmental sensitivity of the disease to predict demands on health services in different regions and seasons.
format Online
Article
Text
id pubmed-7275681
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher PeerJ Inc.
record_format MEDLINE/PubMed
spelling pubmed-72756812020-06-15 Air transportation, population density and temperature predict the spread of COVID-19 in Brazil Pequeno, Pedro Mendel, Bruna Rosa, Clarissa Bosholn, Mariane Souza, Jorge Luiz Baccaro, Fabricio Barbosa, Reinaldo Magnusson, William PeerJ Biogeography There is evidence that COVID-19, the disease caused by the betacoronavirus SARS-CoV-2, is sensitive to environmental conditions. However, such conditions often correlate with demographic and socioeconomic factors at larger spatial extents, which could confound this inference. We evaluated the effect of meteorological conditions (temperature, solar radiation, air humidity and precipitation) on 292 daily records of cumulative number of confirmed COVID-19 cases across the 27 Brazilian capital cities during the 1st month of the outbreak, while controlling for an indicator of the number of tests, the number of arriving flights, population density, proportion of elderly people and average income. Apart from increasing with time, the number of confirmed cases was mainly related to the number of arriving flights and population density, increasing with both factors. However, after accounting for these effects, the disease was shown to be temperature sensitive: there were more cases in colder cities and days, and cases accumulated faster at lower temperatures. Our best estimate indicates that a 1 °C increase in temperature has been associated with a decrease in confirmed cases of 8%. The quality of the data and unknowns limit the analysis, but the study reveals an urgent need to understand more about the environmental sensitivity of the disease to predict demands on health services in different regions and seasons. PeerJ Inc. 2020-06-03 /pmc/articles/PMC7275681/ /pubmed/32547889 http://dx.doi.org/10.7717/peerj.9322 Text en © 2020 Pequeno et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Biogeography
Pequeno, Pedro
Mendel, Bruna
Rosa, Clarissa
Bosholn, Mariane
Souza, Jorge Luiz
Baccaro, Fabricio
Barbosa, Reinaldo
Magnusson, William
Air transportation, population density and temperature predict the spread of COVID-19 in Brazil
title Air transportation, population density and temperature predict the spread of COVID-19 in Brazil
title_full Air transportation, population density and temperature predict the spread of COVID-19 in Brazil
title_fullStr Air transportation, population density and temperature predict the spread of COVID-19 in Brazil
title_full_unstemmed Air transportation, population density and temperature predict the spread of COVID-19 in Brazil
title_short Air transportation, population density and temperature predict the spread of COVID-19 in Brazil
title_sort air transportation, population density and temperature predict the spread of covid-19 in brazil
topic Biogeography
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7275681/
https://www.ncbi.nlm.nih.gov/pubmed/32547889
http://dx.doi.org/10.7717/peerj.9322
work_keys_str_mv AT pequenopedro airtransportationpopulationdensityandtemperaturepredictthespreadofcovid19inbrazil
AT mendelbruna airtransportationpopulationdensityandtemperaturepredictthespreadofcovid19inbrazil
AT rosaclarissa airtransportationpopulationdensityandtemperaturepredictthespreadofcovid19inbrazil
AT bosholnmariane airtransportationpopulationdensityandtemperaturepredictthespreadofcovid19inbrazil
AT souzajorgeluiz airtransportationpopulationdensityandtemperaturepredictthespreadofcovid19inbrazil
AT baccarofabricio airtransportationpopulationdensityandtemperaturepredictthespreadofcovid19inbrazil
AT barbosareinaldo airtransportationpopulationdensityandtemperaturepredictthespreadofcovid19inbrazil
AT magnussonwilliam airtransportationpopulationdensityandtemperaturepredictthespreadofcovid19inbrazil