Optimising tuberculosis treatment: causal inference framework and mathematical modelling to support the development, prioritisation, and impact assessment of novel tools and strategies

• Applicant: Florian Marx, Ph.D.
• Subject Area: Epidemiology and Medical Biometry/Statistics ; Clinical Infectiology and Tropical Medicine
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 549218607

Tuberculosis (TB) remains the leading infectious disease cause of death globally. Despite the availability of curative drug therapy for decades, many high TB burden countries struggle to translate high cure rates observed in clinical trials to everyday practice. While promising new tools and strategies are in development to improve treatment outcomes, understanding the determinants and mechanisms driving unfavorable outcomes in routine TB care is crucial for effective implementation. This project aims to address this gap by combining a causal inference framework with advanced mathematical modeling techniques. The project will synthesise existing knowledge to identify individual determinants and causal mechanisms of unfavorable treatment outcomes in national TB programs. By incorporating this knowledge into an individual-level microsimulation model, it will assess the health and economic impact of new tools and strategies for optimising treatment outcomes in routine TB care. Furthermore, the project will utilise calibrated transmission-dynamic mathematical models to estimate the population-level benefits and cost implications of scaling up the most promising interventions in high-burden countries.

• DFG Programme: Independent Junior Research Groups