Kukuseva, Maja and Stojkovic, Natasa and Martinovska Bande, Cveta and Koceva Lazarova, Limonka (2024) Mathematical Analysis and Simulation of Measles Infection Spread with SEIRV+D Model. ICT Innovations 2023 Web proceedings. pp. 39-47.
![[thumbnail of mathematical-analysis-and-simulation-of-measles-infection-spread-with-seirvd-model WEB.pdf]](/style/images/fileicons/text.png) |
Text
mathematical-analysis-and-simulation-of-measles-infection-spread-with-seirvd-model WEB.pdf Download (399kB) |
Abstract
Measles is a highly contagious infectious disease caused by a virus in
the paramyxovirus family. Mathematical models for epidemics are useful tools
for understanding and predicting epidemic spread and its dynamics. These mathematical models allow researchers to simulate the spread of measles in the population by presenting the interactions between susceptible, exposed, infected, recovered, and vaccinated individuals. In this paper, an improved SEIRV+D model
is developed based on known epidemiology models such as the SIR and SEIR
models. The interactions between different compartments are given as a system
of six Ordinary Differential Equations (ODEs) that represent the dynamics of the
model. Additionally, the natural death rate and natural birth rate are considered.
One of the key issues in epidemiology models is the reproduction number, which
indicates the average number of secondary infections caused by a single infected
individual. Another key issue is the disease-free equilibrium point (steady state)
that can help predict disease outbreaks. Both the basic reproduction number and
the disease-free equilibrium point have been obtained for the developed model.
A case study for North Macedonia is analyzed for two different vaccination rates
to demonstrate the impact of vaccination.
| Item Type: | Article |
|---|---|
| Subjects: | Natural sciences > Computer and information sciences Natural sciences > Matematics |
| Divisions: | Faculty of Computer Science |
| Depositing User: | Maja Kukuseva |
| Date Deposited: | 07 Aug 2024 07:05 |
| Last Modified: | 10 Aug 2024 17:50 |
| URI: | https://eprints.ugd.edu.mk/id/eprint/34497 |
Actions (login required)
 |
View Item |