Parameters’ estimation, sensitivity analysis and model uncertainty for an influenza a mathematical model: case of Morocco
Abstract
In this paper, we consider a SEIS model type compartmental model to explore the evolution of the Influenza epidemic in Morocco. To consider a more realistic model we consider the seasonality of parameters, by considering time-dependent infection rate, time-dependent recovery rate, and time-dependent intervention rate. The next-generation matrix method is used to compute the threshold of equilibria’s stability R0. Based on real data of Influenza epidemic A infections in Morocco from the month of December 2018 to the month of March 2019 published by the Influenza Laboratory Surveillance Information system, we estimate the model parameters. In order to identify the most influential parameter in the proposed model, we carry out the local sensitivity analysis. We calculate sensitivity indices based on the estimated parameters which identify the most influential parameters. We perform also the uncertainty analysis to determine the relationship between the different parameters of the model.
- Published
- 24 August 2020
- Volume
- 2020 (2020)
- License
- CC BY 3.0
Copyright © 2020 Bidah Sara, Zakary Omar, Tridane Abdessamad, Rachik Mostafa, Ferjouchia Hanane. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article
B. Sara, Z. Omar, T. Abdessamad, R. Mostafa, F. Hanane, Parameters’ estimation, sensitivity analysis and model uncertainty for an influenza a mathematical model: case of Morocco, Commun. Math. Biol. Neurosci. 2020 (2020), 57. https://doi.org/10.28919/cmbn/4753
BibTeX
@article{Sara2020,
author = {Sara, Bidah and Omar, Zakary and Abdessamad, Tridane and Mostafa, Rachik and Hanane, Ferjouchia},
title = {Parameters’ estimation, sensitivity analysis and model uncertainty for an influenza a mathematical model: case of Morocco},
journal = {Commun. Math. Biol. Neurosci.},
volume = {2020},
year = {2020},
pages = {57},
doi = {10.28919/cmbn/4753}
}