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

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About: Air transportation, population density and temperature predict the spread of COVID-19 in Brazil Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Air transportation, population density and temperature predict the spread of COVID-19 in Brazil
Creator	Baccaro, Fabricio Barbosa, Reinaldo Bosholn, Mariane Magnusson, William Mendel, Bruna Pequeno, Pedro Rosa, Clarissa Souza, Jorge
Source	Medline; PMC
abstract	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.
has issue date	2020-06-03(xsd:dateTime)
bibo:doi	10.7717/peerj.9322
bibo:pmid	32547889
has license	cc-by
sha1sum (hex)	84326066b4c76692e13233d35d9fbbb2f8fbce6b
schema:url	https://doi.org/10.7717/peerj.9322
resource representing a document's title	Air transportation, population density and temperature predict the spread of COVID-19 in Brazil
has PubMed Central identifier	PMC7275681
has PubMed identifier	32547889
schema:publication	PeerJ
resource representing a document's body	covid:84326066b4c76692e13233d35d9fbbb2f8fbce6b#body_text
is schema:about of	named entity 'disease' named entity 'number' named entity 'temperature' named entity 'confirmed' named entity 'population density' named entity 'temperatures' named entity 'demographic' named entity 'temperature' named entity 'confirmed' named entity 'health' named entity 'CAUSED BY' named entity 'DATA' named entity 'BUT' named entity 'EVIDENCE' named entity 'TESTS' named entity 'SOCIOECONOMIC FACTORS' named entity 'LOWER' named entity 'EXTENTS' named entity 'APART' named entity 'CAPITAL' named entity 'LIMIT' named entity 'ABOUT' named entity 'REGIONS' named entity 'TEMPERATURES' named entity 'UNKNOWNS' named entity 'cumulative' named entity 'However' named entity 'environmental' named entity 'proportion' named entity 'cities' named entity 'temperature' named entity 'betacoronavirus' named entity 'inference' named entity 'outbreak' named entity 'factors' named entity 'time' named entity 'effects' named entity 'conditions' named entity 'COVID-19' named entity 'cases' named entity 'precipitation' named entity 'predict' named entity 'COVID-19' named entity 'solar radiation' named entity 'air humidity' named entity 'socioeconomic' named entity 'health services' named entity 'Air transportation' named entity 'COVID-19' named entity 'Brazil' named entity 'COVID-19 disease' named entity 'Brazil' named entity 'Brazil' named entity 'infection' named entity 'virus' named entity 'solar radiation' named entity 'COVID-19' named entity 'viruses' named entity 'health care' named entity 'Cuiabá' named entity 'Secretarias' named entity 'polymorphisms' named entity 'solar radiation' named entity 'COVID-19' named entity 'COVID-19' named entity 'humidity' named entity 'SARS-CoV-2' named entity 'COVID-19' named entity 'standard deviation' named entity 'population density' named entity 'SARS-CoV-2' named entity 'time series' named entity 'socioeconomic' named entity 'COVID-19'

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