A Mathematical Model of COVID-19: Analysis and Identification of Parameters for Better Decision Making

ISSN Print: 2152-7385
ISSN Online: 2152-7393
www.scirp.org/journal/am
E-mail: am@scirp.org

Citations

"A Mathematical Model of COVID-19: Analysis and Identification of Parameters for Better Decision Making"
written by Ouaténi Diallo, Yaya Kone, Chata Sanogo, Jérôme Pousin,
published by Applied Mathematics, Vol.13 No.2, 2022

has been cited by the following article(s):

[1]	Evaluation of dynamic growth trend of renewable energy based on mathematical model
	Energy Reports, 2023

[2]	A Review of Mathematical Models of COVID-19 Transmission
	Contemporary Mathematics, 2023

[3]	Development of a probabilistic model for Covid-19 dynamics with consideration of non-severe and severe infections
	Alexandria Engineering Journal, 2023

[4]	Energy Reports
	2019

[1]	On finite-time stability of some COVID-19 models using fractional discrete calculus Computer Methods and Programs in Biomedicine Update, 2025 DOI:10.1016/j.cmpbup.2025.100188

[2]	A method to determine the unknown parameters of mathematical model of epidemic transmission by genetic algorithm Network Modeling Analysis in Health Informatics and Bioinformatics, 2025 DOI:10.1007/s13721-025-00616-z

[3]	Evaluation of dynamic growth trend of renewable energy based on mathematical model Energy Reports, 2023 DOI:10.1016/j.egyr.2022.11.139

[4]	Development of a probabilistic model for Covid-19 dynamics with consideration of non-severe and severe infections Alexandria Engineering Journal, 2023 DOI:10.1016/j.aej.2023.09.067