Original Research Article

Extended Stochastic Logistic Growth Model with Population Dynamics: Development and Simulation

ISSN 2979-8582  ·  Article No. 076

Ikegwu, Emmanuel M.✉ Nkemnole, E. Bridget Arowolo, O. Taofik

Publication Details

Publication Date
10/11/2026
Volume / Issue
Vol 1, Issue 6 (2026)
Article No.
076
Journal
British Journal of Contemporary Research
Received
29 Oct 2026
Views
34
Downloads
12
Affiliations

Ikegwu, Emmanuel M.: Department of Statistics, Faculty of Physical and Earth Science, University of Lagos, Akoka, Nigeria; Department of Statistics, School of Science, Yaba College of Technology, Yaba, Lagos, Nigeria

Nkemnole, E. Bridget: Department of Statistics, Faculty of Physical and Earth Science, University of Lagos, Akoka, Nigeria

Arowolo, O. Taofik: Department of Mathematical Sciences, Faculty of Science, Lagos State University of Science and Technology, Ikorodu, Lagos, Nigeria; Department of Statistics, Faculty of Physical and Earth Science, University of Lagos, Akoka, Nigeria

Abstract

Population growth in developing economies presents complex challenges, requiring robust models to inform sustainable policy interventions. This literature review examines the classical Stochastic Logistic Growth Models (SLGMs), and proposes a Population Dynamics Extension to it as an advanced framework integrating stochasticity, carrying capacity and population dynamics to better capture real-world demographic fluctuations. Unlike classical logistic models, the Extended SLGM with population dynamics accounts for environmental stressors, economic constraints, and policy-driven population control measures, making it particularly relevant for developing nations grappling with rapid urbanisation and resource scarcity. This review synthesises key studies applying the SLGM and creates scenarios of extensions with births, deaths and migration rates, and resource constraints, highlighting its superiority over deterministic approaches in predicting growth trajectories under uncertainty and over the classical SLGM. Special emphasis is placed on how the model informs policies balancing economic development with demographic stability. Critically, we identify gaps in current research, such as the need for localised parameter calibration and interdisciplinary integration with socio-economic data. By evaluating the extended SLGM with Population Dynamics, its theoretical and empirical contributions will be explored, with its potential as a tool for policymakers seeking evidence-based strategies to manage population growth in developing economies while fostering equitable development.

Keywords

Demographic Modelling Developing Economies Growth Control Population Dynamics Stochastic Logistic Growth Sustainable Development

License

CC BY 4.0

This article is published under the Creative Commons Attribution 4.0 International License . Free to read, share, and adapt with attribution.

Cite This Article

Ikegwu, Emmanuel M., Nkemnole, E. Bridget, Arowolo, O. Taofik (2026). Extended Stochastic Logistic Growth Model with Population Dynamics: Development and Simulation. British Journal of Contemporary Research, 1(6), Article 076. https://doi.org/10.67693/BJCR-WBK4QHKS
Ikegwu, Emmanuel M.. “Extended Stochastic Logistic Growth Model with Population Dynamics: Development and Simulation.” British Journal of Contemporary Research, vol. 1, no. 6, 2026.
Ikegwu, Emmanuel M.. “Extended Stochastic Logistic Growth Model with Population Dynamics: Development and Simulation.” British Journal of Contemporary Research 1, no. 6.

Metadata

ISSN 2979-8582
DOI Prefix 10.67693
Tracking ID BEX_OCT_26_285

British Journal of Contemporary Research

Open Access · Peer Reviewed · Published by Bexford Publishing Ltd

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