A Digital Twin of COVID-19 Infected Lungs Allows for Personalized, Interactive Clinical Interventions

The COVID-19 pandemic revealed the urgent need for models bridging scales from molecules to organs to predict outcomes and guide interventions from a mechanistic perspective. Epidemiological and molecular simulations provided insights but remain limited in linking infection dynamics to organ-level pathology and personalized decisions. We present a digital twin of a SARS-CoV-2 infected lung coupling two tools: Alya, which models airflow and viral transport in patient-specific geometries, and PhysiBoSS, an agent-based simulator capturing cell–cell interactions, signaling, and the impact of viral replication and oxygen deprivation in alveoli.
A key innovation is a predictive layer atop this modeling. The simulations not only reproduce infection but also forecast trajectories under different scenarios: patient heterogeneity (e.g., vaccination, severity), therapeutic interventions (e.g., drugs, immunomodulation), and supportive care (e.g., ventilation). Iterative cycles provide a systems-level view of disease progression and enable real-time testing of interventions to evaluate organ-level impact.
This approach addresses a critical gap: the lack of integrative frameworks combining realistic organ-level transport with mechanistic cell-level models for quantitative, patient-tailored predictions. Our results show the feasibility of this coupling, highlight the role of multiscale interactions in shaping outcomes, and illustrate how digital twins can advance precision medicine.