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Mathematical Modeling of Population Growth Using Differential Equations
Subject area: Science,Engineering and Technology · Area of research: Mathematical Population Modeling
DOI: https://doi.org/10.64388/IREV10I1-1722138
Abstract
Population growth is a central topic in mathematical biology and ecological modeling. This paper analyzes population dynamics using exponential, logistic, and extended nonlinear models. The study formulates first-order differential equations to describe how populations evolve over time under different environmental constraints. Analytical solutions are derived for classical models, while advanced extensions such as harvesting effects, Allee thresholds, time-dependent growth rates, delayed feedback systems, and predator–prey interactions are also introduced. Results show that exponential models describe ideal conditions, while logistic and nonlinear models provide realistic and stable long-term population behavior.
Keywords
Logistic Growth, Exponential Growth, Carrying Capacity, Stability Analysis, Ecological Systems, Population Biology.
How to cite this paper
@article{1722138,
author = {Lavkush Pandey},
title = {Mathematical Modeling of Population Growth Using Differential Equations},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {1},
pages = {3958-3963},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722138.pdf},
abstract = {Population growth is a central topic in mathematical biology and ecological modeling. This paper analyzes population dynamics using exponential, logistic, and extended nonlinear models. The study formulates first-order differential equations to describe how populations evolve over time under different environmental constraints. Analytical solutions are derived for classical models, while advanced extensions such as harvesting effects, Allee thresholds, time-dependent growth rates, delayed feedback systems, and predator–prey interactions are also introduced. Results show that exponential models describe ideal conditions, while logistic and nonlinear models provide realistic and stable long-term population behavior.},
keywords = {Logistic Growth, Exponential Growth, Carrying Capacity, Stability Analysis, Ecological Systems, Population Biology.},
month = {July},
doi = {https://doi.org/10.64388/IREV10I1-1722138}
}