Integrating mosquito behavior and transfusion risk for evidence-based malaria policy: a case study of three South Sudan provinces

Abstract Malaria is a major public health burden in South Sudan, one of the most malaria-endemic countries worldwide. In 2020, approximately 2.3 million cases and 4,500 deaths were reported. This study developed and analyzed a deterministic malaria transmission model incorporating transfusion-mediated infections and distinct diurnal and nocturnal mosquito biting rates. Using surveillance data from Central Equatoria State (CES), Warrap (WRP), and Western Bahr el Ghazal (WBGZ), Bayesian inference estimated key transmission parameters. Posterior results showed nocturnal biting dominance in WRP and WBGZ, while CES had balanced day-night exposure, with reproduction number $$\mathcal {R}_0$$ ranging from 2.81 in CES to 5.61 in WBGZ. Model calibration reproduced weekly incidence, capturing peak intensities and inter-epidemic troughs. Simulations indicated that increasing either day- or night-biting independently amplifies infections, but combined increases produce synergistic growth in exposed and infectious populations. Treatment delays disproportionately increased infectious populations in high-transmission CES. Sensitivity analysis identified transmission probabilities and mosquito biting parameters as most influential, with CES more sensitive to daytime biting and WRP/WBGZ to nighttime biting. Optimal control simulations showed that combining bed-net/repellent use ( $$u_1$$ ) with parasite clearance in donated blood ( $$u_3$$ ) rapidly reduced infections. Cost-effectiveness analysis confirmed Strategy VI ( $$u_1 + u_3$$ ) as the most efficient, averting 1,925,821 cases at $110 (ICER $$\approx 5.712 \times 10^{-5}$$ ). Results emphasize spatially adaptive strategies: integrated day-night vector control and transfusion safety in CES, nocturnal vector suppression in WRP and WBGZ, and prompt treatment across all provinces. The study provides actionable, evidence-based guidance linking epidemiological mechanisms to cost-effective policy interventions.