An Age Structured Malaria Transmission Dynamics Model Incorporating Severe and Uncomplicated Infections Compartments

Authors

  • Yusuf Bala Department of Mathematical Sciences, Federal University of Health Sciences, Azare, Bauchi State. Author
  • Abdul H. Bala Department of General studies, Federal College of Education, Yola, Adamawa State. Author
  • Ashiru Umar Department of General Studies, School of General Studies, Federal Polytechnic Damaturu, Yobe State. Author

DOI:

https://doi.org/10.62054/ijdm/0302.12

Abstract

In this study, a mathematical model of malaria was formulated. System of ordinary differential equations were used to described the transmission dynamics of the malaria disease in an age-structured population. The model is proved to be mathematically well-posed and epidemiologically feasible. The Malaria free equilibrium was obtained, and the method of next generation matrix approach were employed to determine the basic reproduction number .  The analysis of the malaria-free equilibrium shows that; the system is locally asymptotically stable if   and unstable if  The approach by Castillo-Chavez was employed to determine the global stability analysis of the Malaria-free equilibrium. The result of the  global stability analysis show that, the system is  locally asymptotically stable when  and unstable if This indicates that, the Malaria transmission elimination is only possible if the threshold parameter is kept below unity. Therefore, all efforts must be geared towards ensuring that the minimum number of secondary infections is less than one in both children and adult populations respectively.

References

Adeogun, A. O., Babalola, A. S., Oyale, O. O., Oyeniyi, T., Omotayo, A., Izekor, R. T., et al. (2025). Spatial distribution and geospatial modeling of potential spread of secondary malaria vectors species in Nigeria using recently collected empirical data. PLOS One, 20(4), e0320531. https://doi.org/10.1371/journal.pone.0320531.

Bhunu, C., Garira, W., & Mukandavire, Z. (2009). Modeling hiv/aids and tuberculosis coinfection. Bulletin of mathematical biology, 71(7), 1745–1780.

Beloconi, A., Nyawanda, B. O., Bigogo, G., Khagayi, S., Obor, D., Danquah, I. et al. … (2023). Malaria, climate variability, and interventions: modelling transmission dynamics. Scientific Reports. (2023) 13, 7367. https://doi.org/10.1038/s41598-023-33868-8.

Castillo-Chavez, C., Feng, Z. & Huang, W. (2001). On the Computation of and its Role on Global Stability. Institute for mathematics and its applications, 125,229.

Collins, O. C. & Duffy, K. J. (2022). A mathematical model for the dynamics and control of malaria in Nigeria. Infectious Disease Modelling, 7 (2022) 728-741.

Dalu, C. E., Dennis, O. C., Chuwkuemeka, O. A., Chifurumnanya, A. E. & Ugochi, C. C. (2022).

Prevalence of Malaria and Vector Abundance in Amichi Community, Nnewi South Local Government Area, Anambra State, Nigeria. Journal of Global Ecology and Environment, 16(4), 68-81.

Driessche, P. V. D. & Watmough, J. (2002). Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Mathematical Biosciences, 180, 29-48.

Grosso, A., Hens, N. & Abrams, S. (2025). An integrative review of the combined use of mathematical and statistical models for estimating malaria transmission parameters. Malaria Journal. (2025)24,173. https://doi.org/10.1186/s12936-025-05415-5.

Li, J., Docile, H. J., Fisher, D., Pronyuk, K. & Zhao, L. (2024). Current Status of Malaria Control and Elimination in Africa Epidemiology, Diagnosis, Treatment, Progress and Challenges. Journal of Epidemiology and Global Health, 14 (2024), 561–57. DOI: https://doi.org//10.1007/s44197-024-00228-29.

Madito, G. T. & Silal, S. P. (2024). Comparing different approaches of modelling the effects of temperature and rainfall on malaria transmission in high and low transmission settings. (In press).

Nwankwo, A. & Okuonghae, D. (2019). Mathematical assessment of the impact of different microclimate conditions on malaria transmission dynamics. Mathematical Biosciences and Engineering, 16(3), 1414–1444. http://www.aimspress.com/journal/MBE

Olutimo, A. L., Mbah, N. U., Abass, F. A., & Adeyanju, A. A., (2024). Effect of Environmental Immunity on Mathematical Modeling of Malaria Transmission between Vector and Host Population. Journal of Applied Science and Environmental Management. 28 (1), 205-212. https://www.ajol.info/index.php/jasem.

Ozodiegwu, I. D., Ambrose, M., Galatas, B., Runge, M., Nandi, A…. (2023). Application of mathematical modelling to inform national malaria intervention planning in Nigeria. Malaria Journal, 22(137), 1-19. https://doi.org/10.1186/s12936-023-04563-w

Perko, N., Kebede, T. & Mousa, S. A. (2022). Current and future directions in the prevention and treatment of Malaria. Journal of Pharmacy and Pharmacology research, 6(3), 131.

Traoré, B., Barro, M., Sangaré, B. & Traoré S. (2021). A temperature-dependent mathematical model of malaria transmission with stage-structured mosquito population dynamics. Non-autonomous dynamical systems. 8 (1), 267-296. 10.1515/msds-2020-0138. hal-04691252.

World Health Organization (2023). Report on malaria in Nigeria 2022. http://apps.who.int/iris.

World Health Organization. (2024). World malaria report 2024. https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2024

Downloads

Published

2026-06-12

Data Availability Statement

The research is an ongoing work. Presented here is the phase I which comprises only the qualitative analysis. Phase II will be presented in due course when data is obtain from various sources on malaria transmission such as Nimet.

How to Cite

An Age Structured Malaria Transmission Dynamics Model Incorporating Severe and Uncomplicated Infections Compartments. (2026). International Journal of Development Mathematics (IJDM), 3(2), 170-189. https://doi.org/10.62054/ijdm/0302.12