Predicting COVID-19 disease severity from SARS-CoV-2 spike protein sequence by mixed effects machine learning.

ABSTRACT: Epidemiological studies show that COVID-19 variants-of-concern, like Delta and Omicron, pose different risks for severe disease, but they typically lack sequence-level information for the virus. Studies which do obtain viral genome sequences are generally limited in time, location, and population scope. Retrospective meta-analyses require time-consuming data extraction from heterogeneous formats and are limited to publicly available reports. Fortuitously, a subset of GISAID, the global SARS-CoV-2 sequence repository, includes "patient status" metadata that can indicate whether a sequence record is associated with mild or severe disease. While GISAID lacks data on comorbidities relevant to severity, such as obesity and chronic disease, it does include metadata for age and sex to use as addi

SUBMITTER: Sokhansanj BA 

PROVIDER: S-EPMC9384346 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature