Titre :

Nonsmooth convex optimization to estimate the Covid-19 reproduction number space-time evolution with robustness against low quality data

Auteur(s) :

Pascal, Barbara [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Abry, Patrice [Auteur]
Laboratoire de Physique de l'ENS Lyon [Phys-ENS]
Pustelnik, Nelly [Auteur]
Laboratoire de Physique de l'ENS Lyon [Phys-ENS]
Roux, Stéphane G. [Auteur]
Laboratoire de Physique de l'ENS Lyon [Phys-ENS]
Gribonval, Rémi [Auteur]
Dynamic Networks : Temporal and Structural Capture Approach [DANTE]
Flandrin, Patrick [Auteur]
Laboratoire de Physique de l'ENS Lyon [Phys-ENS]

Titre de la revue :

IEEE Transactions on Signal Processing

Pagination :

2859-2868

Éditeur :

Institute of Electrical and Electronics Engineers

Date de publication :

2022-06-19

ISSN :

1053-587X

Mot(s)-clé(s) en anglais :

IEEE Covid-19
reproduction number
space-time evolution
nonsmooth convex optimization
outlier robustness

Discipline(s) HAL :

Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Sciences du Vivant [q-bio]/Santé publique et épidémiologie
Mathématiques [math]/Optimisation et contrôle [math.OC]
Statistiques [stat]/Applications [stat.AP]

Résumé en anglais : [en]

Daily pandemic surveillance, often achieved through the estimation of the reproduction number, constitutes a critical challenge for national health authorities to design countermeasures. In an earlier work, we proposed to ...
Lire la suite >Daily pandemic surveillance, often achieved through the estimation of the reproduction number, constitutes a critical challenge for national health authorities to design countermeasures. In an earlier work, we proposed to formulate the estimation of the reproduction number as an optimization problem, combining data-model fidelity and space-time regularity constraints, solved by nonsmooth convex proximal minimizations. Though promising, that first formulation significantly lacks robustness against the Covid-19 data low quality (irrelevant or missing counts, pseudo-seasonalities,.. .) stemming from the emergency and crisis context, which significantly impairs accurate pandemic evolution assessments. The present work aims to overcome these limitations by carefully crafting a functional permitting to estimate jointly, in a single step, the reproduction number and outliers defined to model low quality data. This functional also enforces epidemiology-driven regularity properties for the reproduction number estimates, while preserving convexity, thus permitting the design of efficient minimization algorithms, based on proximity operators that are derived analytically. The explicit convergence of the proposed algorithm is proven theoretically. Its relevance is quantified on real Covid-19 data, consisting of daily new infection counts for 200+ countries and for the 96 metropolitan France counties, publicly available at Johns Hopkins University and Santé-Publique-France. The procedure permits automated daily updates of these estimates, reported via animated and interactive maps. Open-source estimation procedures will be made publicly available.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL