Parameter Identification Problem in the discrete-time SIR Model

We investigate the problem of determining time dependent parameters for discrete-time epidemiological compartmental models such as the Susceptible-Infected-Recovered (SIR). We show how to determine parameters based on minimal error type iterative schemes. Such methods involve the computation of the adjoint of the derivative operator of a nonlinear function. This is a nontrivial task that we accomplish by carefully crafting auxiliary problems. To show the efficiency of the method, we consider examples involving real COVID-19 data.

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Publication Details

Journal
Trends in Computational and Applied Mathematics
Published
2024-11-06
DOI
https://doi.org/10.5540/tcam.2024.025.e01805
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Parameter Identification Problem in the discrete-time SIR Model

Jemy A. Mandujano Valle, Alexandre L. Madureira
Trends in Computational and Applied Mathematics
Fault Detection and Control Systems
article

Parameter Identification Problem in the discrete-time SIR Model

Jemy A. Mandujano Valle, Alexandre L. Madureira
article en

Abstract

We investigate the problem of determining time dependent parameters for discrete-time epidemiological compartmental models such as the Susceptible-Infected-Recovered (SIR). We show how to determine parameters based on minimal error type iterative schemes. Such methods involve the computation of the adjoint of the derivative operator of a nonlinear function. This is a nontrivial task that we accomplish by carefully crafting auxiliary problems. To show the efficiency of the method, we consider examples involving real COVID-19 data.

Trends in Computational and Applied MathematicsVol. 25(1)
Laboratório Nacional de Computação Científica (BR)
Governo Brasil, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Ministério da Saúde
Openalex Percentile: Top 68%
Fault Detection and Control Systems
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