The Diagnostic Accuracy Optimization Model: A Systems-Based Approach to Malaria Testing in Resource-Limited Settings

Malaria remains a leading cause of morbidity and mortality in resource-limited settings, where the accuracy of diagnostic systems plays a pivotal role in disease management and public health planning. Despite the availability of diagnostic tools such as rapid diagnostic tests (RDTs), microscopy, and polymerase chain reaction (PCR), the balance between sensitivity, specificity, and operational efficiency is often suboptimal. This paper introduces the "Diagnostic Accuracy Optimization Model (DAOM)," a systems-based framework for improving diagnostic performance by integrating quantitative validation metrics, workflow efficiency analysis, and contextual health system constraints. By synthesizing real-world data, statistical simulations, and field-based implementation outcomes, the model offers a scalable approach to improving diagnostic reliability and treatment outcomes. The paper further validates DAOM using comparative performance indicators from pilot deployments in sub-Saharan African clinics. This work contributes a structured evaluation model that aligns with WHO policy recommendations and enhances national malaria control programs' ability to meet Global Technical Strategy milestones.