Unveiling the hidden threat: the impact of sub-optimum treatment on acquired immunity, asymptomatic cases, and malaria dynamics

Malaria persists worldwide, exerting its greatest impact in sub-Saharan Africa. This study develops and uses a mathematical model to assess how sub-optimum versus optimum treatment of malaria drives asymptomatic infections, immunity build-up, and sustained transmission, providing insights for effective control. Fitted to case data from Kenya and Nigeria, the framework is used to quantify the burden of malaria and the additional cost associated with sub-optimum treatment. Global sensitivity analysis identifies mosquito demographic parameters, biting rates, and malaria treatment rate among major disease drivers under sub-optimum treatment, emphasizing the need for integrated strategies that improve access to optimum treatment and reduce mosquito-human contact. Model simulations show that sub-optimum treatment amplifies asymptomatic prevalence, sustaining/increasing malaria transmission and burden. Further simulations reveal that optimum treatment could avert more than one-third of infections and deaths, while asymptomatic infections contribute up to $96%$ ($75%$) of malaria-related Years Lived with Disability in Kenya (Nigeria). Cost analysis shows that optimum treatment lowers malaria burden significantly and can reduce annual total treatment costs by $\approx $12$ million, underscoring the substantial economic and public health gains of limiting sub-optimum care. This study demonstrates that effective and sustained malaria control requires strengthening adherence to treatment, minimizing sub-optimum treatment, reducing mosquito-human contact, and targeting asymptomatic carriers to curb hidden transmission and reduce long-term health and economic losses.