[Preprint] High number of SARS-CoV-2 persistent infections uncovered through genetic analysis of samples from a large community surveillance study

High number of SARS-CoV-2 persistent infections uncovered through genetic analysis of samples from a large community-based surveillance study
Multiple researchers from Universities of Oxford and Northumbria, Office for National Statistics, Wellcome Sanger Institute COVID-19 Surveillance Team, COVID-19 Infection Survey Group, The COVID-19 Genomics UK (COG-UK) consortium

Persistent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections may act as viral reservoirs that could seed future outbreaks, give rise to highly divergent lineages, and contribute to cases with post-acute Coronavirus disease 2019 (COVID-19) sequelae (Long Covid). However, the population prevalence of persistent infections, their viral load kinetics, and evolutionary dynamics over the course of infections remain largely unknown.

We identified 381 infections lasting at least 30 days, of which 54 lasted at least 60 days. These persistently infected individuals had more than 50% higher odds of self-reporting Long Covid compared to the infected controls, and we estimate that 0.09-0.5% of SARS-CoV-2 infections can become persistent and last for at least 60 days.

In nearly 70% of the persistent infections we identified, there were long periods during which there were no consensus changes in virus sequences, consistent with prolonged presence of non-replicating virus. Our findings also suggest reinfections with the same major lineage are rare and that many persistent infections are characterised by relapsing viral load dynamics.

Furthermore, we found a strong signal for positive selection during persistent infections, with multiple amino acid substitutions in the Spike and ORF1ab genes emerging independently in different individuals, including mutations that are lineage-defining for SARS-CoV-2 variants, at target sites for several monoclonal antibodies, and commonly found in immunocompromised patients.

This work has significant implications for understanding and characterising SARS-CoV-2 infection, epidemiology, and evolution.

Link