Abstract

Infectious disease diagnosis often relies on multiple diagnostic tests, each with varying levels of sensitivity and specificity. This introduces uncertainty that can significantly impact disease dynamics and treatment outcomes. This study presents an individual-level infectious disease model that incorporates the uncertainties associated with multiple, imperfect diagnostic tests. By simulating various testing scenarios, we analyze how these uncertainties affect disease transmission, progression, and control strategies. Our findings highlight the need for improved diagnostic algorithms and tailored interventions that account for diagnostic variability, ultimately aiming to enhance disease management and public health outcomes.