SEIR Mathematical Model of COVID-19 Epidemic Transmission in France

The COVID-19 pandemic, first identified in Wuhan, China, in December 2019, rapidly spread across the globe, necessitating mathematical models to understand its transmission dynamics. This study presents a susceptible-exposed-infected-removed (SEIR) model to analyze the spread of COVID-19 in France. The model incorporates key epidemiological parameters, including the transmission rate (α) and social interaction factor (κ), which influence disease spread. To solve the system of differential equations, we employ the fourth-order Runge-Kutta (RK4) method. A parametric study is conducted to validate the model, and the basic reproduction number (R0) is derived to assess disease stability. The results align with real-world data, confirming the model’s effectiveness in describing the outbreak. Our findings highlight the critical role of social distancing, recovery rates, and transmission reduction measures in mitigating the spread of COVID-19.

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Publication Details

Journal
SciNexuses.
Published
2025-03-05
DOI
https://doi.org/10.61356/j.scin.2025.2504
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
0.00
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article

SEIR Mathematical Model of COVID-19 Epidemic Transmission in France

Midra Tahir
SciNexuses.
COVID-19 epidemiological studies
article

SEIR Mathematical Model of COVID-19 Epidemic Transmission in France

Midra Tahir
article en

Abstract

The COVID-19 pandemic, first identified in Wuhan, China, in December 2019, rapidly spread across the globe, necessitating mathematical models to understand its transmission dynamics. This study presents a susceptible-exposed-infected-removed (SEIR) model to analyze the spread of COVID-19 in France. The model incorporates key epidemiological parameters, including the transmission rate (α) and social interaction factor (κ), which influence disease spread. To solve the system of differential equations, we employ the fourth-order Runge-Kutta (RK4) method. A parametric study is conducted to validate the model, and the basic reproduction number (R0) is derived to assess disease stability. The results align with real-world data, confirming the model’s effectiveness in describing the outbreak. Our findings highlight the critical role of social distancing, recovery rates, and transmission reduction measures in mitigating the spread of COVID-19.

SciNexuses.Vol. 2
Openalex Percentile: Top 95%
COVID-19 epidemiological studies
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