Clinical- and Omic- Based Analysis of Trajectory for COVID-19 Infection and Recovery (COMBATC19IR)

We aim to develop and validate Artificial Intelligence (AI) models that can reliably predict the course of COVID-19 progression and its complications via: 1) modeling patients clinical trajectories to understand their recovery and potential development of pulmonary complications, 2) characterizing diversity and virulence of the co-infecting viral and microbial community to understand disease mechanisms and severity, and 3) analyzing the host's biological makeup to understand the individual susceptibility to infections in low and high-risk individuals with pre-existing lung conditions. We validate and inform our AI models by clinical datasets and bio-samples collected from five different international patient cohorts: three cohorts in Munich and two in the USA. In addition, we track the COVID-19 patients for their post discharge health status focusing on lung related outcome measures at 3 months to confirm the recovery trajectory observed during hospitalization and compare their disease course and outcome to those recovering from similar-symptom diseases such as Influenza and community-acquired pneumonia. Based on the results obtained by our study, we develop a decision support system for physicians to allocate resources and to plan individualized follow-up care for at-risk patients.