The Impact of Antimalarial Drug on the Dynamics of Malaria Transmission Using Nonlinear Mathematical Model

This study presents a nonlinear mathematical model for investigating the impact of antimalarial drug intervention on the transmission dynamics of malaria in a coupled human–mosquito population. The total population is divided into susceptible, exposed, infected, and recovered human compartments together with susceptible, exposed, and infected mosquito compartments. The model preserves the original transmission structure while incorporating the effectiveness of antimalarial drugs as a control parameter influencing the recovery of infected individuals. Qualitative analyses of the model are performed by determining the disease-free equilibrium, endemic equilibrium, and the basic reproduction number, $R_0$, using the next-generation matrix approach. Local asymptotic stability of the disease-free equilibrium is established under the threshold condition $R_0<1$, indicating that malaria transmission can be eliminated when each infectious individual generates less than one secondary infection. Conversely, when $R_0>1$, the endemic equilibrium becomes feasible and the disease persists within the population. Numerical simulations and graphical analyses are carried out to illustrate the temporal evolution of the human and mosquito compartments under varying levels of antimalarial drug effectiveness. The simulation results demonstrate that increasing treatment effectiveness substantially reduces the infected human and mosquito populations while increasing the recovered population, thereby lowering disease prevalence. Sensitivity analysis further reveals that treatment effectiveness, transmission rates, and mosquito biting rates are among the most influential parameters governing malaria transmission. The findings highlight the critical role of sustained antimalarial drug administration, alongside vector control strategies, in reducing the malaria burden and provide additional quantitative evidence supporting the use of mathematical modelling for evaluating intervention policies and informing public health decision-making.