Spatial and temporal regularization to estimate COVID-19 reproduction number R(t): Promoting piecewise smoothness via convex optimization.
PLoS One
; 15(8): e0237901, 2020.
Artículo
en Inglés
| MEDLINE | ID: covidwho-723873
Preprint
Este artículo de revista científica es probablemente basado en un preprint previamente disponible, por medio del reconocimiento de similitud realizado por una máquina. La confirmación humana aún está pendiente.
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Este artículo de revista científica es probablemente basado en un preprint previamente disponible, por medio del reconocimiento de similitud realizado por una máquina. La confirmación humana aún está pendiente.
Ver preprint
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 monitoring 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 observations. The novelty of the proposed approach is twofold 1) the estimation of the reproduction 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 departments and their joint analysis, leading to dynamic maps revealing the temporal co-evolution of their reproduction numbers.
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Neumonía Viral
/
Modelos Estadísticos
/
Infecciones por Coronavirus
/
Análisis Espacio-Temporal
/
Betacoronavirus
Tipo de estudio:
Estudio experimental
/
Estudio observacional
/
Estudio pronóstico
Tópicos:
Variantes
Límite:
Humanos
País/Región como asunto:
Europa
Idioma:
Inglés
Revista:
PLoS One
Asunto de la revista:
Ciencia
/
Medicina
Año:
2020
Tipo del documento:
Artículo
País de afiliación:
Journal.pone.0237901
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