Prevalence of Asymptomatic SARS-CoV-2 Infection
Notice bibliographique
Résumé
LettersFebruary 2021Prevalence of Asymptomatic SARS-CoV-2 InfectionFREEMuge Cevik, MD, Isaac I. Bogoch, MD, Gail Carson, MD, Eric D’Ortenzio, MD, Krutika Kuppalli, MDMuge Cevik, MDSchool of Medicine, University of St. Andrews, St. Andrews, United Kingdom, Isaac I. Bogoch, MDToronto General Hospital and University of Toronto, Toronto, Ontario, Canada, Gail Carson, MDISARIC Global Support Centre, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom, Eric D’Ortenzio, MDINSERM (Institut national de la santé et de la recherche médicale), Paris, France, Krutika Kuppalli, MDMedical University of South Carolina, Charleston, South CarolinaAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L20-1283 SectionsAboutVisual AbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:We read Oran and Topol's narrative review with interest (1). There is a clear need to better understand the contribution of persons with asymptomatic SARS-CoV-2 infection (that is, those with no symptoms throughout their infection) in driving the current pandemic. However, we believe that there are caveats that are pertinent when interpreting the findings of this review, including the lack of a clear definition of “asymptomatic infection” and selective inclusion of cross-sectional studies.First, a narrative review containing a dearth of poor-quality evidence resulting in an overestimate of asymptomatic infections is problematic and may misinform policy response. Of the 16 studies included in this review, 4 defined symptoms of COVID-19 as fever and respiratory symptoms, 3 did not clearly define symptoms, and 6 were media articles that provided no information about symptoms. Respiratory symptoms or fever do not cover the spectrum of COVID-19 presentations, and many persons with nonspecific or mild symptoms are misclassified as being asymptomatic.Second, cross-sectional studies cannot determine who will remain asymptomatic throughout their infection (2). For example, a study of 359 COVID-19 cases in Guangzhou, China, found that 71 persons (86%) later developed symptoms (3). Nine of the 16 studies that Oran and Topol include are cross-sectional in design, but Oran and Topol describe them as cohorts. As such, whether some patients might have developed symptoms later is unclear. Only 1 study included other symptoms (such as malaise, rhinorrhea, and those involving the throat) and followed patients, with 89% developing symptoms later (4).Third, none of the studies cited included contact tracing; therefore, we cannot comment on asymptomatic transmission based on included studies. In contrast to Oran and Topol's conclusions, recent studies assessing longitudinal characteristics of viral load and transmission have found that truly asymptomatic patients have significantly lower viral loads than those who develop symptoms and transmit infection to fewer secondary cases (5).Finally, a systematic review addressed the same question using a robust methodology, excluded several of the studies that Oran and Topol included, and concluded that 15% to 20% of persons infected with SARS-CoV-2 remain asymptomatic (3). There remains an immediate need to fill knowledge gaps on COVID-19; however, efforts must coalesce to conduct systematic reviews using robust and transparent methods to avoid selective reporting and provide a balanced synthesis of evidence. Academic groups should join forces to coordinate efforts, share the burden to deliver timely and robust systematic reviews, avoid duplication, and improve quality.References1. Oran DP, Topol EJ. Prevalence of asymptomatic SARS-CoV-2 infection. A narrative review. Ann Intern Med. 2020;173:362-7. doi:10.7326/M20-3012 LinkGoogle Scholar2. Buitrago-Garcia DC, Egli-Gany D, Counotte MJ, et al. The role of asymptomatic SARS-CoV-2 infections: rapid living systematic review and meta-analysis. medRxiv. Preprint posted online 28 July 2020. doi:2020.04.25.20079103 Google Scholar3. Zhang W, Cheng W, Luo L, et al. Secondary transmission of coronavirus disease from presymptomatic persons, China. Emerg Infect Dis. 2020;26:1924-6. [PMID: 32453686] doi:10.3201/eid2608.201142 CrossrefMedlineGoogle Scholar4. Arons MM, Hatfield KM, Reddy SC, et al; Public Health–Seattle and King County and CDC COVID-19 Investigation Team. Presymptomatic SARS-CoV-2 infections and transmission in a skilled nursing facility. N Engl J Med. 2020;382:2081-90. [PMID: 32329971] doi:10.1056/NEJMoa2008457 CrossrefMedlineGoogle Scholar5. Chau NVV, Thanh Lam, Thanh Dung, et al; OUCRU COVID-19 Research Group. The natural history and transmission potential of asymptomatic SARS-CoV-2 infection. Clin Infect Dis. 2020. [PMID: 32497212] doi:10.1093/cid/ciaa711 Google Scholar Comments 0 Comments Sign In to Submit A Comment Author, Article, and Disclosure InformationAuthors: Muge Cevik, MD; Isaac I. Bogoch, MD; Gail Carson, MD; Eric D’Ortenzio, MD; Krutika Kuppalli, MDAffiliations: School of Medicine, University of St. Andrews, St. Andrews, United KingdomToronto General Hospital and University of Toronto, Toronto, Ontario, CanadaISARIC Global Support Centre, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United KingdomINSERM (Institut national de la santé et de la recherche médicale), Paris, FranceMedical University of South Carolina, Charleston, South CarolinaAcknowledgment: The authors thank the CORRE Network (International COVID-19 Rapid Evidence Reviews Group).Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=L20-1283. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoPrevalence of Asymptomatic SARS-CoV-2 Infection Daniel P. Oran and Eric J. Topol Prevalence of Asymptomatic SARS-CoV-2 Infection Andrew N. Cohen , Bruce Kessel Prevalence of Asymptomatic SARS-CoV-2 Infection Daniel P. Oran , Eric J. Topol Prevalence of Asymptomatic SARS-CoV-2 Infection Daniel T. Halperin Prevalence of Asymptomatic SARS-CoV-2 Infection N. Belgin Akilli , Ramazan Koylu Prevalence of Asymptomatic SARS-CoV-2 Infection Dongsheng Han , Jinming Li Prevalence of Asymptomatic SARS-CoV-2 Infection Charles Elder Metrics Cited byYouths’ perceptions and behaviors on COVID-19 testingCOVID19-ResCapsNet: A Novel Residual Capsule Network for COVID-19 Detection from Chest X-Ray Scans ImagesThe immunology of asymptomatic SARS-CoV-2 infection: what are the key questions?COVID-19 false dichotomies and a comprehensive review of the evidence regarding public health, COVID-19 symptomatology, SARS-CoV-2 transmission, mask wearing, and reinfection February 2021Volume 174, Issue 2 Page: 283-284 Keywords Cohort studies COVID-19 Disclosure Fevers Longitudinal studies Pharynx Systematic reviews Viral load Viral transmission and infection ePublished: 16 February 2021 Issue Published: February 2021 Copyright & PermissionsCopyright © 2021 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».