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GLOBAL SENSITIVITY ANALYSIS IN THE SIHR EPIDEMIOLOGICAL MODEL WITH APPLICATION TO COVID19

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Bibliographic Details
Title: GLOBAL SENSITIVITY ANALYSIS IN THE SIHR EPIDEMIOLOGICAL MODEL WITH APPLICATION TO COVID19
Authors: Djellout, Hacène, Chauvière, Cédric, Ismail, Liban
Contributors: Laboratoire de Mathématiques Blaise Pascal (LMBP), Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)
Source: ISSN: 2169-0014 ; Journal of Statistics & Management Systems ; https://uca.hal.science/hal-04027720 ; Journal of Statistics & Management Systems, In press.
Publisher Information: CCSD
Taylor and Francis
Publication Year: 2023
Collection: HAL Clermont Auvergne (Université Blaise Pascal Clermont-Ferrand / Université d'Auvergne)
Subject Terms: SIHR model, Lagrange polynomial, Stochastic collocation method, Sobol indices, Simulations, COVID19, [STAT]Statistics [stat], [MATH]Mathematics [math]
Description: International audience ; In this paper, we develop a numerical approach based on chaos expansions to analyze the sensitivity of input parameters (basic reproduction number, cure rate, hospitalization rate) on the evolution of the SIHR epidemiological model. Lagrange polynomials affords a natural framework for computing Sobol indices. Such indices give reliable information on the relative importance of each uncertain entry parameters. We use the COVID19 disease as a numerical application to illustrate the study for two different basic reproduction numbers R0. Further- more, we consider a sinusoidal and logistic infection rate and we give the Sobol indices when the parameters follow the uniform law.
Document Type: article in journal/newspaper
Language: English
Availability: https://uca.hal.science/hal-04027720
https://uca.hal.science/hal-04027720v1/document
https://uca.hal.science/hal-04027720v1/file/ArticleGlobalSA.pdf
Rights: http://creativecommons.org/licenses/by-nc-nd/ ; info:eu-repo/semantics/OpenAccess
Accession Number: edsbas.21BA045D
Database: BASE
Description
Abstract:International audience ; In this paper, we develop a numerical approach based on chaos expansions to analyze the sensitivity of input parameters (basic reproduction number, cure rate, hospitalization rate) on the evolution of the SIHR epidemiological model. Lagrange polynomials affords a natural framework for computing Sobol indices. Such indices give reliable information on the relative importance of each uncertain entry parameters. We use the COVID19 disease as a numerical application to illustrate the study for two different basic reproduction numbers R0. Further- more, we consider a sinusoidal and logistic infection rate and we give the Sobol indices when the parameters follow the uniform law.