Nonsmooth Convex Optimization to Estimate the Covid-19 Reproduction Number Space-Time Evolution With Robustness Against Low Quality Data
Author:
Affiliation:
1. University of Lille, Lille, France
2. CNRS, ENS de Lyon, Laboratoire de Physique, Lyon, France
3. University of Lyon, Lyon Cedex 07, France
Funder
CNRS
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/78/9675017/09792465.pdf?arnumber=9792465
Reference29 articles.
1. Impact of lockdown on COVID-19 epidemic in Île-de-France and possible exit strategies
2. Estimating the burden of SARS-CoV-2 in France;salje;Science,2020
3. Improved inference of time-varying reproduction numbers during infectious disease outbreaks
4. Spatial and temporal regularization to estimate COVID-19 reproduction number R(t): Promoting piecewise smoothness via convex optimization
5. Convex Analysis and Monotone Operator Theory in Hilbert Spaces
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3. Pandemic Intensity Estimation from Stochastic Approximation-Based Algorithms;2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP);2023-12-10
4. Proximal-Langevin Samplers for Nonsmooth Composite Posteriors: Application to the Estimation of Covid19 Reproduction Number;2023 31st European Signal Processing Conference (EUSIPCO);2023-09-04
5. Distributed Reproduction Numbers of Networked Epidemics;2023 American Control Conference (ACC);2023-05-31
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