Christian T. Michael

The paper introduces a method to predict longitudinal outcomes from single-timepoint data using a virtual library of granulomas, applied to tuberculosis in non-human primates.

This paper compares the mechanisms driving lesion outcomes in lung tumors and tuberculosis granulomas using agent-based models to study immune-tumor interactions and treatment responses.

This paper uses a computational model to simulate TB relapse mechanisms, showing that relapse after cure diagnosis is due to non-replicating bacteria reactivating, while unscreened relapse is due to incomplete sterilization.

The paper simulates TB antibiotic regimens in a computational model to show rankings are influenced by spatial scale, detection limit, and initial bacterial burden.

The paper reviews multiscale modeling approaches for infectious diseases and introduces two tools for calibrating and resolving multiscale models, using tuberculosis as an example.