Digital twin frameworks for Hepatitis C: Toward predictive, and personalised management

Despite the availability of antivirals, Hepatitis C remains a major global public health challenge, with over 50 million people living with chronic infection and many remaining undiagnosed or untreated. Transmission persists in marginalised populations, reinfection occurs in high-risk groups, and long-term complications including cirrhosis and hepatocellular carcinoma continue to drive morbidity and mortality. Disease progression, treatment response, and transmission risk are highly heterogeneous and dynamic, shaped by host genetics, immune status, viral kinetics, comorbidities, behavioural exposures, and health-system access. Digital twin, defined as adaptive computational models that continuously mirror an individual's biological and clinical state, offers a transformative approach for predictive and personalised Hepatitis C care. By integrating virological, immunological, biochemical, behavioural, and environmental data, digital twins could enable real-time forecasting of disease progression, optimisation of antiviral therapy, early detection of treatment failure or reinfection, and precision targeting of public-health interventions. This article outlines a conceptual framework for constructing digital twins for Hepatitis C, discusses clinical and population-level applications, and examines the ethical, technical, and translational challenges that must be addressed to support global elimination strategies.