Mathematical Modelling and Analysis of Malaria Transmission Dynamics with Early and Late Treatment Interventions

International audience Malaria, a life-threatening disease caused by Plasmodium parasites transmitted through the bites of infected Anopheles mosquitoes, poses a persistent public health challenge in Nigeria due to its complex transmission dynamics. This study develops a compartmental SEIIR-SEI model to evaluate the impact of early (λ1) and late (λ2) treatment interventions on malaria transmission among children under 5, aiming to guide effective control strategies. Parameterized with Nigerian malaria case data (2007–2021), the model integrates human and mosquito populations to examine how treatment timing affects the basic reproduction number (R0) and disease prevalence. Using stability analysis, sensitivity analysis, and numerical simulations, we find a baseline R0 = 2.24, indicating endemicity. Early treatment reduces this to R0,λ1 = 1.46, outperforming late treatment (R0,λ2 = 1.65). Sensitivity analysis highlights mosquito biting rates (b) and λ1 as key drivers of R0. Simulations show that 60–80% early treatment coverage (λ1 ≥ 0.6) within 24 hours significantly lowers prevalence within 120 days, unlike 100% late treatment (λ2 = 1.0). The disease-free equilibrium is stable when R0 < 1, achievable with high λ1. Rapid diagnosis, Artemisinin-based Combination Therapies, and vector control are critical for eradication. Policymakers should enhance healthcare access and surveillance to reduce Nigeria’s malaria burden.