Quantifying the value of diagnostics for infectious disease burden mitigation A health economic modeling perspective

Diagnostics are fundamental to effective infectious disease management, yet access remains inequitable globally. This thesis evaluates the economic and public health value of existing and novel diagnostics in resource-limited settings across multiple infectious diseases, employing health economic and mathematical modeling to generate evidence to inform policy. This work investigates the cost-effective implementation of existing diagnostic tools for HIV to prevent vertical transmission using Markov modeling, and the roll-out and cost-effectiveness of rapid diagnostic tests for SARS-CoV-2 through a micro-costing study and agent-based modeling. Modeling analyses in this thesis further assess the potential impact and costs of novel diagnostic tools, including a point-of-care test for neonatal sepsis, a culture-dependent molecular diagnostic for antimicrobial resistance profiling in bloodstream infections, and a non-invasive malaria screening diagnostic. By contributing to the United Nations' 2025 AIDS report and directly informing the World Health Organization's Target Product Profile for a new neonatal sepsis diagnostic, the findings of this thesis have had real-world impact. As foreign aid contracts and implementation trials become increasingly unfeasible, this thesis demonstrates how health economic modeling is an indispensable tool for guiding diagnostic investment and placement, shaping clinical guidelines, and advancing health equity in resource-limited settings.