Impact of Clinical and Genomic Factors on SARS-CoV2 Disease Severity

The SARS-CoV2 virus behind the COVID-19 pandemic is manifesting itself in different ways among infected people. While many are experiencing mild flue-like symptoms or are even remaining asymptomatic after infection, the virus has also led to serious complications, overloading ICUs while claiming more than 2.6 million lives world-wide. In this work, we apply AI methods to better understand factors that drive the severity of the disease. From the UK BioBank dataset we analyzed both clinical and genomic data of patients infected by this virus. Leveraging positive-unlabeled machine learning algorithms coupled with RubricOE, a state-of-the-art genomic analysis framework for genomic feature extraction, we propose severity prediction algorithms with high F₁ score. Furthermore, we extracted insights on clinical and genomic factors driving the severity prediction. We also report on how these factors have evolved during the pandemic w.r.t. significant events such as the emergence of the B.1.1.7 SARS-CoV2 virus strain.