Mathematical modeling and simulation for COVID-19 with mutant and quarantined strategy

Zhenhua Yu, Jingmeng Zhang, Yun Zhang, Xuya Cong, Xiaobo Li and Almetwally M. Mostafa

Chaos, Solitons & Fractals, 2024, vol. 181, issue C

Abstract: A new nonlinear dynamics model named SEIMQR (Susceptible-Exposed-Infected-Mutant-Quarantined-Recovered) is proposed to study the transmission mechanism of COVID-19 and predict its development tendency. We calculate equilibria and basic reproduction number, and prove the local asymptotic stability of the proposed model. The transcritical bifurcation of the equilibria and sensitivity of important parameters are analyzed. The least square method is employed to estimate model parameters, and then COVID-19 transmission tendency is predicted. The validity of the proposed model is verified by real data in Britain. Simulation results show that it can well simulate the spread of COVID-19 in three different periods, whose mean relative errors are 2.24 %, 2.20 %, and 6.54 %, respectively. Theoretical proofs and numerical simulations indicate that the proposed model is more adaptable to complex epidemics modeling like COVID-19, which can provide theoretical support for scientific epidemics prevention.

Keywords: COVID-19; SEIMQR model; Equilibria; Parameter estimation; Epidemic prediction (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077924002078
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:181:y:2024:i:c:s0960077924002078

DOI: 10.1016/j.chaos.2024.114656

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().