An exploration of the interplay between treatment and vaccination in an Age-Structured Malaria Model using non-linear ordinary differential equations

Malaria continues to be a significant global health challenge, particularly in tropical regions. Resistance to key antimalarial drugs is spreading, complicating treatment efforts. While progress toward eradication has been slow, the development and introduction of novel malaria vaccines offer hope for reducing the disease burden in endemic areas. To address these challenges, we develop an extended Susceptible–Exposed–Infected–Recovered (SEIR) age-structured model incorporating malaria vaccination for children, drug-sensitive and drug-resistant strains, and interactions between human hosts and mosquitoes. Our research evaluates how malaria vaccination coverage influences disease prevalence and transmission dynamics. We derive both strains’ basic, intervention, and invasion reproduction numbers and conduct sensitivity analysis to identify key parameters affecting infection prevalence. Our findings reveal that model outcomes are primarily influenced by scale factors that reduce transmission and natural recovery rates for the resistant strain, as well as by drug treatment and vaccination efficacies and mosquito death rates. Numerical simulations indicate that while treatment reduces the malaria disease burden, it also increases the proportion of drug-resistant cases. Conversely, higher vaccination efficacy correlates with lower infection cases for both strains. These results suggest that a synergistic approach involving vaccination and treatment could effectively decrease the overall proportion of the infected population.