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Prevalence of persistent SARS-CoV-2 in a large community surveillance study.

ABSTRACT: Persistent SARS-CoV-2 infections may act as viral reservoirs that could seed future outbreaks^1-5, give rise to highly divergent lineages^6-8 and contribute to cases with post-acute COVID-19 sequelae (long COVID)^9,10. However, the population prevalence of persistent infections, their viral load kinetics and evolutionary dynamics over the course of infections remain largely unknown. Here, using viral sequence data collected as part of a national infection survey, we identified 381 individuals with SARS-CoV-2 RNA at high titre persisting for at least 30 days, of which 54 had viral RNA persisting at least 60 days. We refer to these as 'persistent infections' as available evidence suggests that they represent ongoing viral replication, although the persistence of non-replicating RNA cannot be ruled out in all. Individuals with persistent infection had more than 50% higher odds of self-reporting long COVID than individuals with non-persistent infection. We estimate that 0.1-0.5% of infections may become persistent with typically rebounding high viral loads and last for at least 60 days. In some individuals, we identified many viral amino acid substitutions, indicating periods of strong positive selection, whereas others had no consensus change in the sequences for prolonged periods, consistent with weak selection. Substitutions included mutations that are lineage defining for SARS-CoV-2 variants, at target sites for monoclonal antibodies and/or are commonly found in immunocompromised people^11-14. This work has profound implications for understanding and characterizing SARS-CoV-2 infection, epidemiology and evolution.

SUBMITTER: Ghafari M

PROVIDER: S-EPMC10901734 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Prevalence of persistent SARS-CoV-2 in a large community surveillance study.

Ghafari Mahan M Hall Matthew M Golubchik Tanya T Ayoubkhani Daniel D House Thomas T MacIntyre-Cockett George G Fryer Helen R HR Thomson Laura L Nurtay Anel A Kemp Steven A SA Ferretti Luca L Buck David D Green Angie A Trebes Amy A Piazza Paolo P Lonie Lorne J LJ Studley Ruth R Rourke Emma E Smith Darren L DL Bashton Matthew M Nelson Andrew A Crown Matthew M McCann Clare C Young Gregory R GR Santos Rui Andre Nunes Dos RAND Richards Zack Z Tariq Mohammad Adnan MA Cahuantzi Roberto R Barrett Jeff J Fraser Christophe C Bonsall David D Walker Ann Sarah AS Lythgoe Katrina K

Nature 20240221 8001

Persistent SARS-CoV-2 infections may act as viral reservoirs that could seed future outbreaks1-5, give rise to highly divergent lineages6-8 and contribute to cases with post-acute COVID-19 sequelae (long COVID)9,10. However, the population prevalence of persistent infections, their viral load kinetics and evolutionary dynamics over the course of infections remain largely unknown. Here, using viral sequence data collected as part of a national infection survey, we ...[more]

PMID: 38383783

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Project description:BackgroundEffective Coronavirus Disease 2019 (COVID-19) response relies on good knowledge of population infection dynamics, but owing to under-ascertainment and delays in symptom-based reporting, obtaining reliable infection data has typically required large dedicated local population studies. Although many countries implemented Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) testing among travellers, it remains unclear how accurately arrival testing data can capture international patterns of infection, because those arrival testing data were rarely reported systematically, and predeparture testing was often in place as well, leading to nonrepresentative infection status among arrivals.Methods and findingsIn French Polynesia, testing data were reported systematically with enforced predeparture testing type and timing, making it possible to adjust for nonrepresentative infection status among arrivals. Combining statistical models of polymerase chain reaction (PCR) positivity with data on international travel protocols, we reconstructed estimates of prevalence at departure using only testing data from arrivals. We then applied this estimation approach to the United States of America and France, using data from over 220,000 tests from travellers arriving into French Polynesia between July 2020 and March 2022. We estimated a peak infection prevalence at departure of 2.1% (95% credible interval: 1.7, 2.6%) in France and 1% (95% CrI: 0.63, 1.4%) in the USA in late 2020/early 2021, with prevalence of 4.6% (95% CrI: 3.9, 5.2%) and 4.3% (95% CrI: 3.6, 5%), respectively, estimated for the Omicron BA.1 waves in early 2022. We found that our infection estimates were a leading indicator of later reported case dynamics, as well as being consistent with subsequent observed changes in seroprevalence over time. We did not have linked data on traveller demography or unbiased domestic infection estimates (e.g., from random community infection surveys) in the USA and France. However, our methodology would allow for the incorporation of prior data from additional sources if available in future.ConclusionsAs well as elucidating previously unmeasured infection dynamics in these countries, our analysis provides a proof-of-concept for scalable and accurate leading indicator of global infections during future pandemics.

Dataset Information

Prevalence of persistent SARS-CoV-2 in a large community surveillance study.

Publications

Prevalence of persistent SARS-CoV-2 in a large community surveillance study.

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