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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.
References
[1] Allen, L. J. S. (2007). An introduction to mathematical biology. Pearson.
[2] Bacaër, N. (2011). A short history of mathematical population dynamics. Springer.
[3] Boyce, W. E., & DiPrima, R. C. (2017). Elementary differential equations and boundary value problems. Wiley.
[4] Brauer, F., Castillo-Chavez, C., & Feng, Z. (2019). Mathematical models in epidemiology and ecology. Springer.
[5] Edelstein-Keshet, L. (1988). Mathematical models in biology. McGraw-Hill.
[6] Edelstein-Keshet, L. (2005). Mathematical models in biology. SIAM.
[7] Edelstein-Keshet, L., & Edelstein-Keshet, L. (2013). Mathematical models in biology. SIAM.
[8] Freedman, H. I. (1980). Deterministic mathematical models in population ecology. Marcel Dekker.
[9] Hirsch, M. W., Smale, S., & Devaney, R. L. (2013). Differential equations, dynamical systems, and an introduction to chaos. Academic Press.
[10] Kaplan, D., & Glass, L. (1995). Understanding nonlinear dynamics. Springer.
[11] Kot, M. (2001). Elements of mathematical ecology. Cambridge University Press.
[12] Logan, J. D. (2015). Applied mathematics. Wiley.
[13] May, R. M. (1976). Simple mathematical models with very complicated dynamics. Nature.
[14] Murray, J. D. (1989). Mathematical biology. Springer.
[15] Murray, J. D. (2002). Mathematical biology I: An introduction. Springer.
[16] Murray, J. D. (2003). Mathematical biology II: Spatial models and biomedical applications. Springer.
[17] Perko, L. (2013). Differential equations and dynamical systems. Springer.
[18] Smith, H. L. (2011). An introduction to delay differential equations with applications to the life sciences. Springer.
[19] Strogatz, S. H. (2018). Nonlinear dynamics and chaos. CRC Press.
[20] Thieme, H. R. (2003). Mathematics in population biology. Princeton University Press.
[21] Verma, A.K. (2017). A Handbook of Zoology. Shri Balaji Publications, Muzaffarnagar. 1- 648. pp.
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}
}