About: Trends of SARS-Cov-2 infection in 67 countries: Role of climate zone, temperature, humidity and curve behavior of cumulative frequency on duplication time

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About: Trends of SARS-Cov-2 infection in 67 countries: Role of climate zone, temperature, humidity and curve behavior of cumulative frequency on duplication time Goto Sponge NotDistinct Permalink

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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Trends of SARS-Cov-2 infection in 67 countries: Role of climate zone, temperature, humidity and curve behavior of cumulative frequency on duplication time
Creator	Tapia-Conyer, Roberto María, Rosa Schmulson, Max Berumen, Jaime Guerrero, Guadalupe Larriva-Sahd, Jorge Liceaga, Eduardo Olaiz, Gustavo Betancourt-Cravioto, Miguel Barrera, Elizabeth Chew, Wong Fajardo-Dolci, Germán Gallardo, Héctor Garcia-Leyva, Rebeca
Source	MedRxiv
abstract	Summary Objective. To analyze the role of temperature, humidity, date of first case diagnosed (DFC) and the behavior of the growth-curve of cumulative frequency (CF) [number of days to rise (DCS) and reach the first 100 cases (D100), and the difference between them (ΔDD)] with the doubling time (Td) of Covid-19 cases in 67 countries grouped by climate zone. Design. Retrospective incident case study. Setting. WHO based register of cumulative incidence of Covid-19 cases. Participants. 1,706,914 subjects diagnosed between 12-29-2019 and 4-15-2020. Exposures. SARS-Cov-2 virus, ambient humidity, temperature and climate areas (temperate, tropical/subtropical). Main outcome measures. Comparison of DCS, D100, ΔDD, DFC, humidity, temperature, Td for the first (Td10) and second (Td20) ten days of the CF growth-curve between countries according to climate zone, and identification of factors involved in Td, as well as predictors of CF using lineal regression models. Results. Td10 and Td20 were ≥3 days longer in tropical/subtropical vs. temperate areas (2.8[plusmn]1.2 vs. 5.7[plusmn]3.4; p=1.41E-05 and 4.6[plusmn]1.8 vs. 8.6[plusmn]4.2; p=9.7E-05, respectively). The factors involved in Td10 (DFC and ΔDD) were different than those in Td20 (Td10 and climate areas). After D100, the fastest growth-curves during the first 10 days, were associated with Td10<2 and Td10<3 in temperate and tropical/subtropical countries, respectively. The fold change Td20/Td10 >2 was associated with earlier flattening of the growth-curve. In multivariate models, Td10, DFC and ambient temperature were negatively related with CF and explained 44.7% (r2 = 0.447) of CF variability at day 20 of the growth-curve, while Td20 and DFC were negatively related with CF and explained 63.8% (r2 = 0.638) of CF variability towards day 30 of the growth-curve. Conclusions. The larger Td in tropical/subtropical countries is positively related to DFC and temperature. Td and environmental factors explain 64% of CF variability in the best of cases. Therefore, other factors, such as pandemic containment measures, would explain the remaining variability.
has issue date	2020-04-23(xsd:dateTime)
bibo:doi	10.1101/2020.04.18.20070920
has license	medrxiv
sha1sum (hex)	31a8c61158e3d1c3c2dc4c0f1a16ca29deee4383
schema:url	https://doi.org/10.1101/2020.04.18.20070920
resource representing a document's title	Trends of SARS-Cov-2 infection in 67 countries: Role of climate zone, temperature, humidity and curve behavior of cumulative frequency on duplication time
resource representing a document's body	covid:31a8c61158e3d1c3c2dc4c0f1a16ca29deee4383#body_text
is schema:about of	named entity 'Covid-19' named entity 'identification' named entity 'case' named entity 'DCS' named entity 'Setting' named entity 'Main' named entity 'models' named entity 'cases' named entity 'D100' named entity 'reach' named entity 'cumulative incidence' named entity 'doubling time' named entity 'temperate' named entity 'subtropical' named entity 'Covid-19 cases' named entity 'infection' named entity 'cumulative frequency' named entity 'humidity' named entity 'cases' named entity 'medRxiv' named entity 'preprint' named entity 'nominal data' named entity 'preprint' named entity 'Covid' named entity 'preprint' named entity 'preprint' named entity 'doi' named entity 'medRxiv' named entity 'evolution' named entity 'temperate' named entity 'viruses' named entity 'Sweden' named entity 'peer review' named entity 'medRxiv' named entity 'epidemic' named entity 'linear regression' named entity 'Czech Republic' named entity 'subtropical areas' named entity 'COVID-19' named entity 'Covid' named entity 'ULR' named entity 'Adenovirus' named entity 'linear regression' named entity 'preprint' named entity 'TH1' named entity 'North America' named entity 'Covid' named entity 'temperate' named entity 'North America' named entity 'medRxiv' named entity 'hemostasis' named entity 'Covid-19' named entity 'power analysis' named entity 'relative humidity' named entity 'Norway' named entity 'preprint' named entity 'evolution' named entity 'peer review' named entity 'preprint' named entity 'SPSS' named entity 'medRxiv' named entity 'Covid' named entity 'correlation' named entity 'time frame' named entity 'population density' named entity 'temperate' named entity 'peer review' named entity 'temperate' named entity 'preprint' named entity 'temperate countries' named entity 'univariate analysis' named entity 'medRxiv'

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