About: Predictive mathematical models for the number of individuals infected with COVID-19

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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
title	Predictive mathematical models for the number of individuals infected with COVID-19
Creator	Dikaios, N Fokas, A Kastis, G
source	MedRxiv
abstract	We model the time-evolution of the number N(t) of individuals reported to be infected in a given country with a specific virus, in terms of a Riccati equation. Although this equation is nonlinear and it contains time-dependent coefficients, it can be solved in closed form, yielding an expression for N(t) that depends on a function (t). For the particular case that (t) is constant, this expression reduces to the well-known logistic formula, giving rise to a sigmoidal curve suitable for modelling usual epidemics. However, for the case of the COVID-19 pandemic, the long series of available data shows that the use of this simple formula for predictions underestimates N(t); thus, the logistic formula only provides a lower bound of N(t). After experimenting with more than 50 different forms of (t), we introduce two novel models that will be referred to as 'rational' and 'birational'. The parameters specifying these models (as well as those of the logistic model), are determined from the available data using an error-minimizing algorithm. The analysis of the applicability of the above models to the cases of China and South Korea suggest that they yield more accurate predictions, and importantly that they may provide an upper bound of the actual N(t). Results are presented for Italy, Spain, and France.
has issue date	2020-05-06(xsd:dateTime)
bibo:doi	10.1101/2020.05.02.20088591
has license	medrxiv
sha1sum (hex)	7baebb8b6e2d2bae85316e6080b4ed427e5dcd5a
schema:url	https://doi.org/10.1101/2020.05.02.20088591
resource representing a document's title	Predictive mathematical models for the number of individuals infected with COVID-19
resource representing a document's body	covid:7baebb8b6e2d2bae85316e6080b4ed427e5dcd5a#body_text
is schema:about of	named entity 'COVID-19' named entity 'solved' named entity 'equation' named entity 'series' named entity 'constant' »more»

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