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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
PubMed

Insights

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 kinetics

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