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A Mathematical Modelling Approach to the Transmission Dynamics of Malaria | ||
| Al-Qadisiyah Journal of Pure Science | ||
| Volume 31, Special, July 2026 PDF (1.15 M) | ||
| Document Type: Mathematics | ||
| DOI: 10.29350/qjps.2026.170750.1064 | ||
| Authors | ||
| Taiye Oyedepo* 1; Adam Ajimoti ISHAQ2; Abdullahi Muhammed AYINDE3; Folashade Mistura Jimoh4 | ||
| 1Department of Health Information Management, Faculty of Healthcare Management and Leadership, Faderal University of Allied Health Sciences Enugu | ||
| 2Departments of Physical Sciences, Al-Hikmah University, Ilorin, Nigeria. | ||
| 3Department of Mathematics, University of Abuja, Abuja, Nigeria Faculty of Science, Department of Mathematics, 900105, Abuja, Nigeria | ||
| 4Department of Physical Sciences, Al-Hikmah University, Ilorin, Nigeria,[email protected] | ||
| Abstract | ||
| This study presents a modified mathematical model for malaria transmission dynamics, refining an existing SEIR model to account for disease dynamics in human populations. The modified model incorporates assumptions on susceptibility, mortality and disease progression in human populations. Specifically, the model considers that newborns are initially susceptible, all human compartments are subject to natural death rates and infected humans face an additional disease-induced mortality rate. Unique aspects of the modified model include the assumption that recovered individuals without immunity may become susceptible again, allowing the model to reflect realistic malaria relapse patterns. The study further refines the transmission dynamics by incorporating a schematic representation and corresponding equations to describe the flow between susceptible, exposed, infected and recovered compartments for both humans and mosquitoes. This model is analytically validated by demonstrating the existence and uniqueness of the solution and the stability of the disease-free equilibrium point is analyzed through the computation of the basic reproduction number, . Numerical simulations are conducted to observe malaria infection trends, highlighting the implications of the model parameters on infection rates. This refined model provides an enhanced framework for understanding malaria transmission and potential interventions, contributing valuable insights for policymakers and public health professionals. | ||
| Keywords | ||
| Disease Spread; Endemic Equilibrium; Malaria Transmission; Mathematical Modeling; Population Dynamics | ||
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