Exploring Bifurcation in the Compartmental Mathematical Model of COVID-19 Transmission-Reference-Cited by-同舟云学术

Exploring Bifurcation in the Compartmental Mathematical Model of COVID-19 Transmission

Published:2024-09-11 Issue:9 Volume:12 Page:186
ISSN:2079-3197
Container-title:Computation
language:en
Short-container-title:Computation

Author:

Kiseleva Olena¹,Yakovlev Sergiy²^ORCID,Chumachenko Dmytro³⁴^ORCID,Kuzenkov Oleksandr¹

Affiliation:

1. Faculty of Applied Mathematics, Oles Honchar Dnipro National University, 49000 Dnipro, Ukraine

2. Institute of Mathematics, Lodz University of Technology, 90-924 Lodz, Poland

3. Department of Mathematical Modeling and Artificial Intelligence, National Aerospace University “Kharkiv Aviation Institute”, 61070 Kharkiv, Ukraine

4. Ubiquitous Health Technology Lab, University of Waterloo, Waterloo, ON N2L 3G5, Canada

Abstract

This study proposes and theoretically substantiates a unique mathematical model for predicting the spread of infectious diseases using the example of COVID-19. The model is described by a special system of autonomous differential equations, which has scientific novelty for cases of complex dynamics of disease transmission. The adequacy of the model is confirmed by testing on the example of the spread of COVID-19 in one of the largest regions of Ukraine, both in terms of population and area. The practical novelty emerges through its versatile application in real-world contexts, guiding organizational decisions and public health responses. The model’s capacity to facilitate system functioning evaluation and identify significant parameters underlines its potential for proactive management and effective response in the evolving landscape of infectious diseases.

Funder

National Science Center under the IMPRESS-U program

Publisher

MDPI AG

Link

https://www.mdpi.com/2079-3197/12/9/186/pdf

Reference28 articles.

1. WHO Declares COVID-19 a Pandemic;Cucinotta;Acta Bio-Medica Atenei Parm.,2020

2. Nonpharmaceutical Public Health Interventions to Curb the COVID-19 Pandemic: A Narrative Review;Bestetti;J. Infect. Dev. Ctries.,2022

3. Saleem, F., AL-Ghamdi, A.S.A.-M., Alassafi, M.O., and AlGhamdi, S.A. (2022). Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review. Int. J. Environ. Res. Public Health, 19.

4. Spinella, C., and Mio, A.M. (2021). Simulation of the Impact of People Mobility, Vaccination Rate, and Virus Variants on the Evolution of COVID-19 Outbreak in Italy. Sci. Rep., 11.

5. Wang, P., Zheng, X., and Liu, H. (2022). Simulation and Forecasting Models of COVID-19 Taking into Account Spatio-Temporal Dynamic Characteristics: A Review. Front. Public Health, 10.