Using big sequencing data to identify chronic SARS-Coronavirus-2 infections

The evolution of SARS-Coronavirus-2 (SARS-CoV-2) has been characterized by the periodic emergence of highly divergent variants, many of which may have arisen during chronic infections of immunocompromised individuals. Here, we harness a global phylogeny of [~]11.7 million SARS-CoV-2 genomes and search for clades composed of sequences with identical metadata (location, age, and sex) spanning more than 21 days. We postulate that such clades represent repeated sampling from the same chronically infected individual. A set of 271 such chronic-like clades was inferred, and displayed signatures of an elevated rate of adaptive evolution, in line with validated chronic infections. More than 70% of adaptive mutations present in currently circulating variants are found in BA.1 chronic-like clades that predate the circulating variants by months, demonstrating the predictive nature of such clades. We find that in chronic-like clades the probability of observing adaptive mutations is approximately 10-20 higher than that in global transmission chains. We next employ language models to find mutations most predictive of chronic infections and use them to infer hundreds of additional chronic-like clades in the absence of metadata and phylogenetic information. Our proposed approach presents an innovative method for mining extensive sequencing data and providing valuable insights into future evolutionary patterns.