About: Time Dynamics of COVID-19

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Time Dynamics of COVID-19
Creator	Bhattacharjee, Satarupa Carroll, Cody Chen, Yaqing Dubey, Paromita Fan, Jianing Gajardo, Álvaro Müller, Hans-Georg Wang, Jane-Ling Zhou, Xiner
Source	MedRxiv
abstract	We apply tools from functional data analysis to model cumulative trajectories of COVID-19 cases across countries, establishing a framework for quantifying and comparing cases and deaths across countries longitudinally. It emerges that a country's trajectory during an initial first month %22priming period%22 largely determines how the situation unfolds subsequently. We also propose a method for forecasting case counts, which takes advantage of the common, latent information in the entire sample of curves, instead of just the history of a single country. Our framework facilitates to quantify the effects of demographic covariates and social mobility on doubling rates and case fatality rates through a time-varying regression model. Decreased workplace mobility is associated with lower doubling rates with a roughly two week delay, and case fatality rates exhibit a positive feedback pattern.
has issue date	2020-05-23(xsd:dateTime)
bibo:doi	10.1101/2020.05.21.20109405
has license	medrxiv
sha1sum (hex)	9369579de3affda0b0fe251184024f824ea9c62c
schema:url	https://doi.org/10.1101/2020.05.21.20109405
resource representing a document's title	Time Dynamics of COVID-19
resource representing a document's body	covid:9369579de3affda0b0fe251184024f824ea9c62c#body_text
is schema:about of	named entity 'COVID-19' named entity 'demographic' named entity 'case' named entity 'regression model' named entity 'roughly' named entity 'sample' named entity 'regression model' named entity 'COVID-19' named entity 'doubling time' named entity 'eigenfunction' named entity 'peer review' named entity 'peer review' named entity 'stochastic process' named entity 'virus' named entity 'medRxiv' named entity 'Philippines' named entity 'uncorrelated' named entity 'Spain' named entity 'total death' named entity 'Poland' named entity 'medRxiv' named entity 'India' named entity 'population density' named entity 'Norway' named entity 'confounding' named entity 'inflection point' named entity 'virus' named entity 'FPCA' named entity 'medRxiv' named entity 'COVID-19' named entity 'May 23' named entity 'medRxiv' named entity 'infectious disease spread' named entity 'Japan' named entity 'Functional data analysis' named entity 'positively correlated' named entity 'FPCA' named entity 'medRxiv' named entity 'Slovenia' named entity 'South America' named entity 'functional data analysis' named entity 'epidemiological modeling' named entity 'peer review' named entity 'medRxiv' named entity 'Netherlands' named entity 'principal component analysis' named entity 'Singapore' named entity 'standard deviations' named entity 'Norway' named entity 'FPCA' named entity 'COVID-19' named entity 'regression model' named entity 'FDA' named entity 'covariance function' named entity '95% confidence' named entity 'mixed model' named entity 'virus' named entity 'COVID-19' named entity 'COVID-19' named entity 'Switzerland' named entity 'France' named entity 'Slovenia' named entity 'self-isolation' named entity 'population density' named entity 'FDA' named entity 'outlier' named entity 'Principal Components Analysis' named entity 'Poisson' named entity 'peer review' named entity 'medRxiv' named entity 'Thailand' named entity 'case fatality rate' named entity 'conditional expectation' named entity 'social mobility' named entity 'Spain' named entity 'Western European' named entity 'exponential growth'

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