Optimizing MDA and antimalarial treatment in the presence of drug resistance for effective malaria control

• Published in: Mathematical biosciences and engineering : MBE Vol. 22; no. 8; pp. 1898 - 1930
• Main Authors: Nimpa, Manuela M., Teytsa, Hyacinthe N., Mbang, Joseph, Wondji, Charles S., Djidjou-Demasse, Ramsès
• Format: Journal Article
• Language: English
• Published: United States AIMS Press 18.06.2025
• Subjects: Animals; Antimalarials - administration & dosage; Antimalarials - therapeutic use; Computer Simulation; Drug Resistance; Humans; malaria; Malaria - drug therapy; Malaria - epidemiology; Malaria - prevention & control; Malaria - transmission; Malaria, Falciparum - drug therapy; Mass Drug Administration; Mathematics; Mutation; nonlinear dynamical systems; optimal control; Plasmodium falciparum - drug effects; Prevalence; optimal control; drug resistance; mass drug administration; malaria; nonlinear dynamical systems
• ISSN: 1551-0018; 1547-1063; 1551-0018
• DOI: 10.3934/mbe.2025069

Antimalarial drugs are critical for controlling malaria, but the emergence of drug resistance poses a significant challenge to global eradication efforts. This study explores strategies to minimize resistance prevalence and improve malaria control, particularly through the use of mass drug administration (MDA) in combination with antimalarial drugs. We develop a compartmental mathematical model that incorporates asymptomatic, paucisymptomatic, and clinical states of infection and evaluates the impact of resistance mutations on transmission dynamics. The model includes both treated and untreated states among infected and recovered individuals, with a focus on optimizing control strategies through MDA and antimalarial treatment. A global sensitivity analysis identifies the critical factors that influence malaria dynamics, including MDA coverage, treatment access for different infection states, the probability of mutation from treated sensitive human infections, to treated resistant human infections and the initial prevalence of resistance. The model is extended to include optimal control strategies that provide time-dependent control interventions for treatment and MDA. Intuitively, when the mutation rate is relatively low, the optimal strategy combines the use of antimalarial drugs and MDA, with a gradual decrease in antimalarial drug use over time, ensuring sustainable malaria control. In contrast, at higher mutation rates, the strategy prioritizes broader deployment of MDA while significantly reducing reliance on antimalarial to minimize the risk of resistance developing. Numerical simulations of the optimal control problem reinforce the importance of strategic intervention in mitigating drug resistance. This study contributes to understanding the role of MDA and treatment strategies in the control of malaria, with implications for optimizing malaria control programs in endemic regions.