Title :

Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal Monte Carlo Samplers

Author(s) :

Fort, Gersende [Auteur]
Institut de Mathématiques de Toulouse UMR5219 [IMT]
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]

Journal title :

IEEE Transactions on Signal Processing

Pages :

888-900

Publisher :

Institute of Electrical and Electronics Engineers

Publication date :

2023-02-22

ISSN :

1053-587X

English keyword(s) :

Markov Chain Monte Carlo sampling
nonsmooth convex optimization
Bayesian inverse problems
credibility intervals
Covid19
reproduction number

HAL domain(s) :

Statistiques [stat]/Méthodologie [stat.ME]
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Statistiques [stat]/Applications [stat.AP]
Statistiques [stat]/Machine Learning [stat.ML]

English abstract : [en]

Monitoring the Covid19 pandemic constitutes a critical societal stake that received considerable research efforts. The intensity of the pandemic on a given territory is efficiently measured by the reproduction number, ...
Show more >Monitoring the Covid19 pandemic constitutes a critical societal stake that received considerable research efforts. The intensity of the pandemic on a given territory is efficiently measured by the reproduction number, quantifying the rate of growth of daily new infections. Recently, estimates for the time evolution of the reproduction number were produced using an inverse problem formulation with a nonsmooth functional minimization. While it was designed to be robust to the limited quality of the Covid19 data (outliers, missing counts), the procedure lacks the ability to output credibility interval based estimates. This remains a severe limitation for practical use in actual pandemic monitoring by epidemiologists that the present work aims to overcome by use of Monte Carlo sampling. After interpretation of the nonsmooth functional into a Bayesian framework, several sampling schemes are tailored to adjust the nonsmooth nature of the resulting posterior distribution. The originality of the devised algorithms stems from combining a Langevin Monte Carlo sampling scheme with Proximal operators. Performance of the new algorithms in producing relevant credibility intervals for the reproduction number estimates and denoised counts are compared. Assessment is conducted on real daily new infection counts made available by the Johns Hopkins University. The interest of the devised monitoring tools are illustrated on Covid19 data from several different countries.Show less >

Language :

Anglais

Popular science :

Non

Collections :

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

Source :

Harvested from HAL