Modeling the Impact of Vector Reduction and Natural Recovery on the Transmission Dynamics of Malaria

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Abstract

A mathematical modeling of the impact of vector reduction and natural recovery on the transmission dynamics of malaria was carried out. We present a deterministic model for the transmission dynamics of malaria in which natural recovery and vector reduction were both important for the disease management. We estimated the basic reproduction number using the next generation matrix method and investigated the local stability of the disease free equilibrium points of the model. Sensitivity analysis and Numerical simulations of the basic reproduction number with respect to the model parameters were carried out. Our result shows that effective vector reduction and increased natural recovery will reduce the spread of malaria.

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Article Details

How to Cite
K, A. A., O, A. E., A, O. I., M, B. S., W, B., & D, Y. (2025). Modeling the Impact of Vector Reduction and Natural Recovery on the Transmission Dynamics of Malaria. Asian Journal of Science, Technology, Engineering, and Art, 3(3), 638-659. https://doi.org/10.58578/ajstea.v3i3.5396

References

A. Baeza, M. J. Bouma, R. Dhiman, M. Pascual, (2014). Malaria control under unstable dynamics: Reactive vs. climate-based strategies, Acta Trop. 129, 42–51.

A. M. Niger, A. B. Gumel, (2008). Mathematical analysis of the role of repeated exposure on malaria transmission dynamics, Differ. Equ. Dyn. Syst. 16 (3), 251–287.

Beck-Johnson, L.M., Nelson, W.A., Paaijmans, (2013). The effect of temperature on Anopheles mosquito population dynamics and the potential for malaria transmission. PLoS ONE 8(11), Article ID e79276

Chitnis, N., Hyman, J.M., Cushing, J.M. (2008). Determining important parameters in the spread of malaria through the sensitivity analysis of a mathematical model. Bull. Math. Biol. 70(5), 1272–1296

Ducrot, A., Sirima, S., Somé, B., Zongo, P. (2009). A mathematical model for malaria involving differential susceptibility, exposedness and infectivity of human host. J. Biol. Dyn. 3(6), 574–598

F. Forouzannia, A. B. Gumel (2014). Mathematical analysis of an age-structured model for malaria transmission dynamics, Math. Biosci. 247, 80–94.

G. Macdonald (1957). The epidemiology and control of malaria, Oxford University Press, London.

G. Macdonald (1957). The Epidemiology and Control of Malaria. London, New York, Oxford University Press, Toronto,.

G.A. Ngwa and W.S. Shu (2000). A mathematical model for endemic malaria with variable human and mosquito populations, Math Comput Model, 32, 747-763, doi: 10.1016/S0895-7177(00)00169-2. [14] J.L. Aron, Mathematical modeling of immunity to malaria, Math Bios., 90 (1988), 385-396, doi: 10.1016/0025-5564(88)90076-4.

H.M. Yang (2000). Malaria transmission model for different levels of acquired immunity and temperature-dependent parameters (vector), Revista de SaudePublica 34, 223-231, doi: 10.10.1590/S0034-89102000000300003.

H.M. Yang and M.U. Ferreira (2000). Assessing the effects of global warming and local social and economic conditions on the malaria transmission. Revista de SaudePublica, 34, 214-222, doi: 10.1590/S0034- 89102000000300002.

J. C. Kamgang, V. C. Kamla, S. Y. Tchoumi (2014). Modeling the dynamics of malaria transmission with bed net protection perspective, Appl. Math. 5 (19), 3156.

J.L. Aron and R.M. May (1982). The population dynamics of malaria, In Population Dynamics of Infectious Disease, Chapman and Hall, 139-179.

J. M. Addawe, J. E. C. Lope (2012). Analysis of age-structured malaria transmission model. Philipp. Sci. Lett. 5(2), 169–186

J. Li, R.M. Welch, U.S. Nair, T.L. Sever, D.E. Irwin, C. Cordon-Rosales, ( 2002). Dynamic Malaria Models with Environmental Changes, Proceedings of the Thirty- Fourth Southeastern Symposium on System Theory Huntsville, AL, 396-400.

K. Okuneye, A. B. Gumel (2017). Analysis of a temperature-and rainfall-dependent model for malaria transmission dynamics, Math. Biosci. 287, 72–92.

K. Dietz, L. Molineaux, A. Thomas (1974). A malaria model tested in the African savannah, Bull World Health Organ, 50, 347-357.

K. O. Okosun and O. D. Makinde (2011). Modelling the impact of drug resistance in malaria transmission and its optimal control analysis, Interna-tional Journal of the Physical Sciences, 6 No.28, 6479-6487, doi: 10.5897/IJPS10.542.

L. Esteva, A. B. Gumel, C. V. De LeoN (2009). Qualitative study of transmission dynamics of drug-resistant malaria, ´ Math. Computer Model. 50 (3-4), 611–630.

N.T.J. Bailey (1982). The Biomathematics of malaria, Charles Griffin and Co Ltd, London.

R.M. Anderson and R.M. May (1991). Infectious diseases of humans: dynamics and control, Oxford University Press, London.

R. Ross (1911). The prevention of malaria, John Murray, London.

S. D. Hove-Musekwa, et al. (2008). Determining effective spraying periods to control malaria via indoor residual spraying in sub-saharanafrica, Adv. Decis. Sci. 2008, 745463.

S. Dawaki, H. M. Al-Mekhlafi, I. Ithoi, J. Ibrahim, W. M. Atroosh, A. M. et al. (2016) Is nigeria winning the battle against malaria? prevalence, risk factors and kap assessment among hausa communities in kano state, Malaria J. 15, 351.

S. Olaniyi, K. O. Okosun, S. O. Adesanya, E. A. Areo (2018). Global stability and optimal control analysis of malaria dynamics in the presence of human travelers, Open Infect. Dis. J. 10, 166–186

R. Ross, L. O. Howard, W. C. Gorgas (1911). The prevention of malaria, John Murray, London.

W. A. Woldegerima, M. I. Teboh-Ewungkem, G. A. Ngwa (2018). Sensitivity analysis for a within-human-host immuno-pathogenesis dynamics of Plasmodium falciparum parasites, Texts Biomath. 1, 140–168.

Z. Sang, Z. Qiu, Q. Kong, Y. Zou (2012). Assessment of vector control and pharmaceutical treatment in reducing malaria burden: a sensitivity and optimal control analysis, J. Biol. Syst. 20, 67–85.