A One Health digital twin framework for leprosy: linking host, pathogen, and population dynamics

Leprosy, or Hansen’s disease, remains one of the oldest yet persistently misunderstood infections, caused by Mycobacterium leprae and Mycobacterium lepromatosis. Despite advances in multidrug therapy, delays in diagnosis, unpredictable immune reactions, and irreversible nerve damage continue to cause disability. Herein we synthesise current understanding of leprosy biology, host-pathogen interaction, and immune dysregulation, and outline a translational framework for a digital twin of leprosy. The proposed system integrates clinical, molecular, and immunological data into a continuously updating computational model capable of forecasting disease progression, immune reaction risk, and treatment response. Mechanistic and probabilistic submodels represent bacillary dynamics, nerve injury, and immune modulation, while Bayesian updating enables real-time recalibration as new data emerge. This approach offers opportunities for precision management, risk stratification, and programme planning. A digital twin for leprosy could transform case management and reframe this ancient disease in the era of data-driven medicine.