A non-standard finite-difference-method for a non-autonomous epidemiological model: analysis, parameter identification and applications.

COVID-19 SIR model convergence epidemiology non-negativity non-standard finite-difference-method

Journal

Mathematical biosciences and engineering : MBE
ISSN: 1551-0018
Titre abrégé: Math Biosci Eng
Pays: United States
ID NLM: 101197794

Informations de publication

Date de publication:
05 Jun 2023
Historique:
medline: 28 7 2023
pubmed: 28 7 2023
entrez: 28 7 2023
Statut: ppublish

Résumé

In this work, we propose a new non-standard finite-difference-method for the numerical solution of the time-continuous non-autonomous susceptible-infected-recovered model. For our time-discrete numerical solution algorithm, we prove preservation of non-negativity and show that the unique time-discrete solution converges linearly towards the time-continuous unique solution. In addition to that, we introduce a parameter identification algorithm for the susceptible-infected-recovered model. Finally, we provide two numerical examples to stress our theoretical findings.

Identifiants

pubmed: 37501473
doi: 10.3934/mbe.2023577
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

12923-12954

Auteurs

Benjamin Wacker (B)

Department of Engineering and Natural Sciences, University of Applied Sciences Merseburg, Eberhard-Leibnitz-Str. 2, D-06217 Merseburg, Germany.
Chair of Data Science, Faculty of Management, Social Work and Construction, HAWK, Haarmannplatz 3, D-37603 Holzminden, Germany.

Jan Christian Schlüter (JC)

Chair of Data Science, Faculty of Management, Social Work and Construction, HAWK, Haarmannplatz 3, D-37603 Holzminden, Germany.
Computational Epidemiology and Public Health Research Group, Institute for Medical Epidemiology, Biometrics and Informatics, Interdisciplinary Center for Health Sciences, Martin Luther University Halle-Wittenberg, Magdeburger Str. 8, D-06112 Halle, Germany.

Classifications MeSH