Experimental and Computational analysis for linking infection dynamics and lung pathology in COVID-19 (ExComPat-COVID19)

The infection dynamics of SARS-CoV-2 within the lung and its association with disease progression is still incompletely understood. To which extent viral replication kinetics and virus-associated tissue pathology determine patient outcome remains an open question that is of urgent importance to advise effective treatment strategies. In this project, we will combine experimental data of lung organoids with detailed mathematical and computational models to assess SARS-CoV-2 infection kinetics under physiologically relevant conditions. By integrating spatially-resolved microscopy images with time course infection data of SARS-CoV-2 in lung organoids by mathematical analyses we will be able to quantify key parameters governing viral replication and spread, and their association with pathological changes. Extending the computational model to extrapolate infection dynamics within a human lung, we will be able to relate observed viral load kinetics in SARS-CoV-2 infected patients to expected tissue pathology and disease progression. This will help to assess the significance of viral load levels for associated lung pathology during infection. Our interdisciplinary approach will improve our understanding of SARS-CoV-2 replication and spread within lung tissue and its association with disease progression. Furthermore, the inferred computational model provides a tool to test the efficacy of antiviral therapies and represents a first step towards a mechanistic model of infection progression within tissue that could improve our understanding of individual patient outcome.