About: Spatial and temporal regularization to estimate COVID-19 Reproduction Number R(t): Promoting piecewise smoothness via convex optimization

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About: Spatial and temporal regularization to estimate COVID-19 Reproduction Number R(t): Promoting piecewise smoothness via convex optimization 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	Spatial and temporal regularization to estimate COVID-19 Reproduction Number R(t): Promoting piecewise smoothness via convex optimization
Creator	Abry, Patrice Audit, Benjamin Borgnat, Pierre Flandrin, Patrick Garnier, Nicolas Gribonval, Rémi Guichard, Eric Jensen, Pablo Lucas, Charles-Gérard Pustelnik, Nelly Roux, Stéphane
Source	MedRxiv
abstract	Among the different indicators that quantify the spread of an epidemic, such as the on-going COVID-19, stands first the reproduction number which measures how many people can be contaminated by an infected person. In order to permit the mon- itoring of the evolution of this number, a new estimation procedure is proposed here, assuming a well-accepted model for current incidence data, based on past observa- tions. The novelty of the proposed approach is twofold: 1) the estimation of the repro- duction number is achieved by convex optimization within a proximal-based inverse problem formulation, with constraints aimed at promoting piecewise smoothness; 2) the approach is developed in a multivariate setting, allowing for the simultaneous handling of multiple time series attached to different geographical regions, together with a spatial (graph-based) regularization of their evolutions in time. The effectiveness of the approach is first supported by simulations, and two main applications to real COVID-19 data are then discussed. The first one refers to the comparative evolution of the reproduction number for a number of countries, while the second one focuses on French counties and their joint analysis, leading to dynamic maps revealing the temporal co-evolution of their reproduction numbers.
has issue date	2020-06-12(xsd:dateTime)
bibo:doi	10.1101/2020.06.10.20127365
has license	medrxiv
sha1sum (hex)	d7654417288363a343f32de99e2af98a3d3c9266
schema:url	https://doi.org/10.1101/2020.06.10.20127365
resource representing a document's title	Spatial and temporal regularization to estimate COVID-19 Reproduction Number R(t): Promoting piecewise smoothness via convex optimization
resource representing a document's body	covid:d7654417288363a343f32de99e2af98a3d3c9266#body_text
is schema:about of	named entity 'preprint' named entity 'number' named entity 'dedicated' named entity 'novelty' named entity 'inverse problem' named entity 'period' named entity 'spatial heterogeneity' named entity 'global' named entity 'Infectious' named entity 'refer' named entity 'These' named entity 'simultaneous' named entity 'procedure' named entity 'Reproduction' named entity 'Number' named entity 'smoothness' named entity 'EPIDEMIC' named entity 'DEDICATED' named entity 'TEMPORAL' named entity 'AVAILABLE' named entity 'NEW' named entity 'PROXIMAL' named entity 'COUNTRIES' named entity 'INFECTIOUS' named entity 'POPULATION' named entity 'INTERNATIONAL' named entity 'NUMBERS' named entity 'INFECTED' named entity 'OPTIMIZATION' named entity 'COVID-19' named entity 'COUNTIES' named entity 'GLOBAL' named entity 'PEOPLE' named entity 'BEING' named entity 'CO-EVOLUTION' named entity 'PAST' named entity 'CURRENT' named entity 'DIFFERENT' named entity 'CONTACTS' named entity 'REPRODUCTION' named entity 'AUTHOR' named entity 'ORDER' named entity 'EXPOSED' named entity 'CONTACT' named entity 'parametric' named entity 'expense' named entity 'multivariate' named entity 'CC-BY-NC' named entity 'monitoring' named entity 'multiple' named entity 'SIR' named entity 'procedures' named entity 'co-evolution' named entity 'contact' named entity 'patterns' named entity 'quantify' named entity 'case' named entity 'International' named entity 'supported' named entity 'contaminated' named entity 'promoting' named entity 'COVID-19' named entity 'peer review' named entity 'CC-BY-NC 4.0' named entity 'evolution' named entity 'convex optimization' named entity 'measures' named entity 'smoothness' named entity 'Johns Hopkins University' named entity 'time series' named entity 'collected data' named entity 'serial interval' named entity 'départments' named entity 'heterogeneity' named entity 'time series'

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