This project will deliver computational models of the lung, to support the development of patient-specific treatment strategies for the COVID-19 pandemic. The models will i) automate analysis of the damaged lung, providing additional quantitative data to support more reliable and rapid conclusions about the presentation of the virus, ii) provide predictions of how the lung will perform in response to different management strategies (supplemental oxygen, mechanical ventilation, fluid balance) and potential future treatment strategies outlined in the RECOVERY/REMAP-CAP trial (e.g. steroids, anti-inflammatories, antibiotics and plasma from recovered patients); innovatively factoring specific parameters such as weight, height, age, general fitness and ethnicity - which unquestionably have acute relevance for recovery. COVID-19 is heterogenous - affecting everyone differently. Therefore, rapid and appropriate medical responses to individual cases are critical. Presently patients can remain on ineffective treatment pathways for 4-6 hours before alternative treatment strategies are employed. This project reduces waiting times, enabling prioritisation based on quantitative tools. The models deliver heightened understanding of individual lung mechanics, enabling clinicians to quickly make better informed treatment decisions to optimise COVID-19 survival rates. The model will use patient CT data, patient-specific calibration factors (age, sex, size) and risk factors (comorbidities, clinical frailty score, exercise tolerance, APACHE-II, ethnicity), state-of-the-art image analysis and computer simulation, in collaboration with 3DLifePrints to build human lung models. Patient data will be accessed via ICNARC and the SAIL databank. The model will mimic lung structure and mechanical function, accounting for the effect of tissue damage and providing dynamic feedback of lung health.