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Digital twin frameworks for Hepatitis C: Toward predictive and personalised management
Ruqaiyyah Siddiqui1, Naveed Ahmed Khan2
1Institute of Biological Chemistry, Biophysics and Bioengineering, Heriot-Watt University Edinburgh, EH14 4AS, UK; Microbiota Research Center, Istinye University, Istanbul, 34010, Turkey.
Annals of Hepatology
|May 4, 2026
Summary
Digital twins, adaptive computational models, offer a new approach to managing Hepatitis C (HCV) care. These models can personalize treatment and improve public health strategies for HCV elimination.
Area of Science:
- Hepatology and Viral Hepatitis
- Computational Biology and Bioinformatics
- Public Health and Epidemiology
Background:
- Hepatitis C (HCV) remains a significant global health issue, affecting over 50 million people, with persistent transmission and serious long-term complications.
- Disease progression, treatment outcomes, and transmission dynamics in HCV are complex and influenced by numerous host, viral, and environmental factors.
- Current management strategies face challenges in addressing the heterogeneity and dynamism of HCV infection and its complications.
Purpose of the Study:
- To introduce the concept of digital twins as a transformative approach for predictive and personalized Hepatitis C care.
- To outline a conceptual framework for developing digital twins tailored to Hepatitis C.
- To explore the potential clinical and public health applications of digital twins in combating HCV.
Main Methods:
- The study proposes the integration of diverse data streams including virological, immunological, biochemical, behavioral, and environmental factors.
- A conceptual framework for constructing adaptive computational models (digital twins) that continuously mirror an individual's biological and clinical state is presented.
- The methodology emphasizes leveraging these integrated data within digital twin models for predictive analytics.
Main Results:
- Digital twins have the potential to enable real-time forecasting of Hepatitis C disease progression.
- These models can optimize antiviral therapy, facilitate early detection of treatment failure or reinfection.
- Digital twins can support precision targeting of public health interventions for HCV elimination.
Conclusions:
- Digital twins represent a promising paradigm shift for personalized Hepatitis C management and care.
- Successful implementation requires addressing significant ethical, technical, and translational challenges.
- The development and application of digital twins are crucial for advancing global strategies towards Hepatitis C elimination.
Keywords:
Digital twinElimination strategiesHepatitis CInfectious diseasesPersonalised medicinePredictive modellingViral kineticsMore Related Videos
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