Data-based modeling analysis of transfusion-transmitted malaria.

Malaria is one of leading causes of morbidity and mortality in sub-Saharan Africa, with Nigeria bearing the highest global burden. Although vector-borne transmission through female Anopheles mosquitoes is well-characterized, increasing evidence of malaria parasite prevalence among asymptomatic blood donors uncover that transfusion-transmitted malaria cannot be ignored. This paper proposes a compartmental model that integrates both conventional mosquito-borne transmission and transfusion-transmitted pathways. Based on this model, contributions of transfusion-mediated transmission to the basic reproduction number (R 0) and new infection flows are quantified rigorously. Key parameters related to malaria transmission are estimated via Markov Chain Monte Carlo (MCMC) methods utilizing malaria incidence data of Nigeria from 2015 to 2024 years. The model accurately fits the observed data. Sensitivity analysis using Partial Rank Correlation Coefficients shows that R 0 is highly sensitive to the transfusion-transmitted rate (σ t ), highlighting the epidemiological importance of blood safety. Time-series and bivariate contour analyses further demonstrate that both mosquito-borne and transfusion-mediated transmission drastically elevate the outbreak potential. Route-specific and counterfactual contribution analyses show that mosquito-borne transmission remains the main driver of malaria persistence while transfusion-mediated transmission can substantially increase cumulative infections. Notably, eliminating transfusion-mediated transmission may prevent more cases than its direct contribution suggests, highlighting the indirect epidemiological impact of transfusion-mediated pathway. These findings support integrated malaria control strategies combining sustained vector control with strengthened blood-safety measures, especially in endemic regions with high transfusion demand and limited donor screening.