A Mathematical Approach to Malaria Eradication: Insights from Vaccination Strategies
Keywords:
Vaccination, Vaccine efficacy, Malaria, Control reproduction number, Stability, Centre manifold theoryAbstract
In the continued effort to eradicate malaria, the World Health Organisation recently recommended the use of matrix RS1 as a malaria vaccine, the second vaccine after the launch of the first one in 2021. However, efficacy has been a critical issue with vaccines. This study used mathematical modelling to assess the potential impact of vaccines on malaria eradication. The aim of this study is to provide quantitative information on the impact of vaccines on malaria eradication. The formulated model considers vaccination and vaccine efficacy in studying their impact on malaria control. The malaria-free equilibrium and the metric that determines the spread of malaria and its extinction are obtained. Analysis of the existence of an endemic state reveals that there are multiple equilibria, which leads to a backward bifurcation when the bifurcation coefficient a >0. Global endemic stability is established when Rvo>1. Furthermore, the global sensitivity analysis conducted identifies parameters such as the probability of transmission from humans to mosquitoes, the probability of transmission from mosquitoes to humans, biting rate, mosquito recruitment rate, treatment rate, mosquito death rate, and vaccine efficacy as significant determinants of malaria spread and control. Further numerical simulations highlight the importance of vaccine effectiveness in controlling malaria, as a highly effective vaccine is required, along with other interventions, to successfully combat the spread of malaria.
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References
[1] WHO: Malaria. https://www.who.int/news-room/questions-and-answers/item/malaria (2024). Accessed 28 April 2024. 1
[2] UNICEF: Ten things you didn’t know about malaria. https://www.unicef.org/press-releases/ten-things-you-didnt-know-about-malaria (2018). Accessed 17 May 2024. 1
[3] WHO: WHO recommends groundbreaking malaria vaccine for children at risk. https://www.who.int/news/item/06-10-2021-who-recommends-groundbreaking-malaria-vaccine-for-children-at-risk (2021). Accessed 16 May 2024.1
[4] Venkatesan, P.: WHO World Malaria Report 2024. The Lancet Microbe. 6(4), 101073 (2025). 1
[5] WHO. Malaria. https://www.who.int/news-room/fact-sheets/detail/malaria. 1
[6] CD.: Strategies for Reducing Malaria’s Global Impact. https://www.cdc.gov/malaria/php/public-health-strategy/index.html (2024). Accessed 17 May 2024. 1
[7] WHO: Vaccines and immunization.https://www.who.int/health-topics/vaccines-and-immunization (2024). Accessed 15 May 2024. 1
[8] Gerberding, J. L., Haynes, B. F.: Vaccine Innovations—Past and Future. New England Journal of Medicine. 384, 393-396 (2021). https://doi.org/10.1056/NEJMp2029466. 1
[9] CDC: Malaria Vaccines. https://www.cdc.gov/malaria/php/public-health-strategy/malaria-vaccines.html (2024). Accessed 17 May 2024. 1
[10] Venkatesan, P.: Routine malaria vaccinations start in Africa. The Lancet Microbe. 5(6), e519 (2024). https://doi.org/10.1016/S2666-5247(24)00073-9. 1
[11] Kretzschmar, M.: Disease modeling for public health: Added value, challenges, and institutional constraints. Journal of Public Health Policy. 41, 39–51 (2020). https://doi.org/10.1057/s41271-019-00206-0. 1
[12] ˙I˘gret Araz, S., C¸ etin, M.A.: Fractal-Fractional Modeling of the Covid-19 Spread with Deterministic and Stochastic Approaches. Int. J. Appl. Comput. Math. 11, 4 (2025). 1
[13] Arik, rem A., Sari, H. K., Iˇgret Araz, S.: Numerical simulation of Covid-19 model with integer and non-integer order: The effect of environment and social distancing. Results in Physics, 51, 106725 (2023) https://doi.org/10.1016/j.rinp.2023.106725 1
[14] Atangana, A., Iˇgret Araz, S.: Epidemiological Modeling with Developing Countries Realities: Application to Ebola and Covid Spread. Fractals. 33:06 (2025) 1
[15] Alla Hamou, A., Azroul, E., Bouda, S., Gueddaet, M.: Mathematical modeling of HIV transmission in a heterosexual population: incorporating memory conservation. Model. Earth Syst. Environ. 10, 393–416 (2024). https://doi.org/10.1007/s40808-023-01791-6 1
[16] Alla Hamou, A., Azroul E, L’Kima, S.: The effect of migration on the transmission of HIV/AIDS using a fractional model: Local and global dynamics and numerical simulations, Math. Meth. Appl. Sci. 47, 6868–6891,(2024). https://doi.org/10.1002/mma.9946 1
[17] Zamir, M., Nadeem, F., Abdeljawad, T., Hammouch, Z.: Threshold condition and non-pharmaceutical interventions’ control strategies for elimination of COVID-19. Results in Physics, 20, 103698, (2020).https://doi.org/10.1016/j.rinp.2020.103698 1
[18] Oke, S. I., Ojo, M. M., Adeniyi, M. O., Matadi, M. B.: Mathematical modeling of malaria disease with control strategy. Communications in Mathematical Biology and Neuroscience. (2020). https://doi.org/10.28919/cmbn/4513. 1,4
[19] Kane, F., Toure, M., Sogoba, N., Traore, B., Keita, M., Konate, D., Diawara, S. I., Sanogo, D., Keita, S., Sanogo, I., Doumbia, C. O., Ke¨ıta, B., Traor´e, A. S., Sissoko, I., Coulibaly, H., Thiam, S. M., Barry, A., Shaffer, J. G., Diakite, M., Doumbia S.: Modeling clinical malaria episodes in different ecological settings in Mali, 2018-2022. IJID Regions. 10, 24–30 (2024). https://doi.org/10.1016/j.ijregi.2023.11.006. 1
[20] Champagne, C., Gerhards, M., Lana, J. T., Le Menach, A., Pothin, E.: Quantifying the impact of interventions against Plasmodium vivax: A model for country-specific use. Epidemics. 46, 100747 (2024). https://doi.org/10.1016/j.epidem.2024.100747. 1
[21] Xue, L., Sun, L., Guo, S.: Dynamic effects of asymptomatic infections on malaria transmission. Mathematics and Computers in Simulation. 214, 172–182 (2023). https://doi.org/10.1016/j.matcom.2023.07.004. 1
[22] Ajao, S., Olopade, I., Adewale, S., Adesanya, A.: Global Stability Analysis of Malaria Model with Prophylactic Treatment. Advances in Systems Science and Applications. 23(3), 91-107 (2023). https://doi.org/10.25728/assa.2023.23.3.1388. 1
[23] Mangongo, Y. T., Bukweli, J. K., Kampempe, J. D. B., Mabela, R. M., Munganga, J. M. W.: Stability and global sensitivity analysis of the transmission dynamics of malaria with relapse and ignorant infected humans. Physica Scripta. 97(2), 024002 (2022). https://doi.org/10.1088/1402-4896/ac4862. 1
[24] Adeniyi, M. O., Amalare, A. A., Oke, S. I., Salawu, S. O.: Bifurcation analysis and global sensitivity index for malaria disease transmission dynamics: Information and treated bed nets control. International Journal of Biomathematics. 17, 06 (2024). https://doi.org/10.1142/S1793524523500602. 1
[25] Ibrahim, M. M., Kamran, M. A., Naeem Mannan, M. M., Kim, S., Jung, I. H.: Impact of Awareness to Control Malaria Disease: A Mathematical Modeling Approach. Complexity. 8657410, 1–13 (2020). https://doi.org/10.1155/2020/8657410. 1
[26] OJO, M. M., GOUFO, E. F. D.: Assessing the impact of control interventions and awareness on malaria:A mathematical modeling approach. Communications in Mathematical Biology and Neuroscience. (2021). https://doi.org/10.28919/cmbn/6632. 1, 4
[27] Onifade, A. A., Ademola, I. O., Rycht´aˇr J., Taylor, D.: A deterministic mathematical model for quantifiable prediction of antimalarials limiting the prevalence of multidrug-resistant malaria. Healthcare Analytics. 5, 100333 (2024). https://doi.org/10.1016/j.health.2024.100333. 1
[28] Ochieng, F. O.: SEIRS model for malaria transmission dynamics incorporating seasonality and awareness campaign. Infectious Disease Modelling. 9, 84–102 (2024). https://doi.org/10.1016/j.idm.2023.11.010. 1
[29] Witbooi, P., Abiodun, G., Nsuami, M.: A model of malaria population dynamics with migrants. Mathematical Biosciences and Engineering. 18, 7301–7317. (2021). https://doi.org/10.3934/mbe.2021361. 1
[30] Wang, S., Hu, L., Nie, L.: Global dynamics and optimal control of an age-structure Malaria transmission model with vaccination and relapse. Chaos, Solitons & Fractals. 150, 111216 (2021). https://doi.org/10.1016/j.chaos.2021.111216. 1
[31] Ngonghala, C. N.: The impact of temperature and decay in insecticide-treated net efficacy on malaria prevalence and control. Mathematical Biosciences. 355, 108936 (2023). https://doi.org/10.1016/j.mbs.2022.108936 1
[32] Collins, O. C.: A mathematical model for the dynamics and control of malaria in Nigeria. Infectious Disease Modelling. 7(4), 728-741 (2022). https://doi.org/10.1016/j.idm.2022.10.005 1
[33] Woldegerima, W. A.: Ouifki, R., Banasiak, J.: Mathematical analysis of the impact of transmission-blocking drugs on the population dynamics of malaria. Applied Mathematics and Computation. 400, 126005 (2021). https://doi.org/10.1016/j.amc.2021.126005. 1
[34] Massad, E., Laporta, G. Z., Conn, J. E., Chaves, L. S., Bergo, E. S., Figueira, E. A. G., Bezerra Coutinho, F. A., Lopez, L. F., Struchiner, C., Sallum, M. A. M.: The risk of malaria infection for travelers visiting the Brazilian Amazonian region: A mathematical modeling approach. Travel Medicine and Infectious Disease. 37, 101792 (2020). https://doi.org/10.1016/j.tmaid.2020.101792. 1
[35] Olaniyi, S., Falowo, O., Okosun, K., Mukamuri, M., Obabiyi, O., Adepoju, O.: Effect of saturated treatment on malaria spread with optimal intervention. Alexandria Engineering Journal. 65, 443–459 (2023). https://doi.org/10.1016/j.aej.2022.09.024 1
[36] Tchoumi, S., Rwezaura, H., Tchuenche, J.: A mathematical model with numerical simulations for malaria transmission dynamics with differential susceptibility and partial immunity. Healthcare Analytics. 3, 100165 (2023). https://doi.org/10.1016/j.health.2023.100165. 1, 4
[37] Adegbite, G., Edeki, S., Isewon, I., Emmanuel, J., Dokunmu, T., Rotimi, S., Oyelade, J., Adebiyi, E.: Mathematical modeling of malaria transmission dynamics in humans with mobility and control states. Infectious Disease Modelling. 8(4), 1015–1031 (2023). https://doi.org/10.1016/j.idm.2023.08.005. 1
[38] Traor´e, B., Koutou, O., Sangar´e, B.: A global mathematical model of malaria transmission dynamics with structured mosquito population and temperature variations. Nonlinear Analysis: Real World Applications. 53, 103081 (2020). https://doi.org/10.1016/j.nonrwa.2019.103081. 1
[39] Kabor´e, A., Sangar´e, B.,Traor´e, B.: Mathematical modeling of malaria transmission taking into account the role of male mosquitoes. Comp. Appl. Math. 44, 314 (2025). 1
[40] Haile, G. T., Koya, P. R., Mosisa Legesse, F.: Sensitivity analysis of a mathematical model for malaria transmission accounting for infected ignorant humans and relapse dynamics. Frontiers in Applied Mathematics and Statistics, 10, 1487291 (2025). https://doi.org/10.3389/fams.2024.1487291 1
[41] Naandam, S. M., Chataa, P., Gogovi, G. K.: Analysis of a Malaria Transmission Model with Vaccination Proportion and Vaccine-Induced Immunity. Mathematical and Computational Applications, 30(1), 15 (2025). https://doi.org/10.3390/mca30010015 1
[42] Castillo-Chavez, C., Feng, Z., Huang, W.: On the Computation of R0 and its Role on Global Stability. In: CastilloChavez, C., Blower, S., van den Driessche, P., Kirschner, D., Yakubu, A. (eds.). Mathematical Approaches for Emerging and Reemerging Infectious Diseases: An Introduction, pp. 229–250. Springer, New York (2002). 2.3, 2.3
[43] Madubueze, C. E., Ajao, S., Akanni, J. O., Fatmawati, F., Chazuka, Z. Impact of environmental contamination on Lassa fever transmission dynamics: a mathematical modelling approach. Eur. Phys. J. Plus 140, 202 (2025). https://doi.org/10.1140/epjp/s13360-025-06123-4. 2.3
[44] Akanni, J. O., Fatmawati, F., Ajao, S., Asamoah, J. K. K., Abimbade, S. F. Mathematical model of COVID-19 dynamics in the presence of multiple controls. Quality and Quantity (2024). https://doi.org/10.1007/s11135-024-01975-x 2.3
[45] Akanni, J. O., Ajao, S., Abimbade, S. F., Fatmawati, F. Dynamical analysis of COVID-19 and tuberculosis co-infection using mathematical modelling approach[J]. Mathematical Modelling and Control, (2024), 4(2): 208-229. doi:10.3934/mmc.2024018. 2.3
[46] Chukwu, C.W., Fatmawati, F., Utoyo, M.I., Setiawan, A., Akanni, J.O. Fractional model of HIV transmission on workplace productivity using real data from Indonesia, Mathematics and Computers in Simulation, Vol. 225, (2024), Pages 1089-1103, doi.org/10.1016/j.matcom.2023.11.014. 2.3
[47] Akanni, J. O. A non-linear optimal control model for illicit drug use and terrorism dynamics in developing countries with time-dependent control variables, Decision Analytics Journal, Vol. 8, (2023), 100281, doi.org/10.1016/j.dajour.2023.100281. 2.3
[48] Abidemi, A., Akanni, J. O., Makinde, O.D. A non-linear mathematical model for analysing the impact of COVID-19 disease on higher education in developing countries, Healthcare Analytics,Vo. 3, (2023), 100193, doi.org/10.1016/j.health.2023.100193. 2.3
[49] Abidemi, A., Akanni, J.O. Dynamics of illicit drug use and banditry population with optimal control strategies and cost-effectiveness analysis. Comp. Appl. Math. 41, 53 (2022). https://doi.org/10.1007/s40314-022-01760-2 2.3
[50] van den Driessche, P., Watmough, J.: Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Mathematical Biosciences. 180(1-2): 29–48 (2002). https://doi.org/10.1016/s0025-5564(02)00108-6. 2.3
[51] Castillo-Chavez, C., Song, B.: Dynamical models of tuberculosis and their applications. Mathematical Biosciences and Engineering: MBE. 1(2), 361–404 (2004). https://doi.org/10.3934/mbe.2004.1.361. 2.5
[52] Carr, J.: Applications of Centre Manifold Theory. Applied Mathematical Sciences, Springer US, New York, NY, 1981. 2.5, 2.5
[53] Ajao, S., Olopade, I., Akinwumi, T., Adewale, S., Adesanya, A.: Understanding the Transmission Dynamics and Control of HIV Infection: A Mathematical Model Approach. Journal of the Nigerian Society of Physical Sciences. 5(2), 1389 (2023). https://doi.org/10.46481/jnsps.2023.1389. 2.5
[54] LaSalle, J. P.: The Stability of Dynamical Systems. Society for Industrial and Applied Mathematics. (1976). https://doi.org/10.1137/1.9781611970432. 2.6
[55] WHO: Fact sheet about malaria. https://www.who.int/news-room/fact-sheets/detail/malaria (2023). Accessed 26 May 2024. 4
[56] Mohammed-Awel, J., Numfor, E.: Optimal insecticide-treated bed-net coverage and malaria treatment in a malaria-HIV coinfection model. JOURNAL OF BIOLOGICAL DYNAMICS. 11, 160–191 (2017). https://doi.org/10.1080/17513758.2016.1192228. 4
[57] Ngonghala, C. N., Del Valle, S. Y., Zhao, R., Mohammed-Awel, J.: Quantifying the impact of decay in bed-net efficacy on malaria transmission. Journal of Theoretical Biology. 363, 247–261 (2014). https://doi.org/10.1016/j.jtbi.2014.08.018. 4
[58] Ajao, S., Olopade, I., Adewale, S., Adesanya, A.:Global Stability Analysis of Malaria Model with Prophylactic Treatment. Advances in Systems Science and Applications, 23(3), 91-107 (2023). https://doi.org/10.25728/assa.2023.23.3.13884
[59] Agusto, F. B., Leite, M. C. A., Orive, M. E.: The transmission dynamics of a within-and between-hosts malaria model. Ecological Complexity. 38, 31–55 (2019). https://doi.org/10.1016/j.ecocom.2019.02.002. 4
[60] Agusto, F. B., Del Valle, S. Y., Blayneh, K. W., Ngonghala, C. N., Goncalves, M. J., Li, N., Zhao, R., Gong, H.: The impact of bed-net use on malaria prevalence. Journal of Theoretical Biology. 320, 58–65 (2013). https://doi.org/10.1016/j.jtbi.2012.12.007. 4
[61] Chitnis, N., Hyman, J. M., Cushing, J. M.: Determining Important Parameters in the Spread of Malaria Through the Sensitivity Analysis of a Mathematical Model. Bulletin of Mathematical Biology. 70, 1272–1296 (2008). https://doi.org/10.1007/s11538-008-9299-0. 4
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