Longitudinal determination of seroprevalence and immune response to SARS-CoV-2 in a population of food and retail workers through decentralized testing and transformation of ELISA datasets
Notice bibliographique
Résumé
BACKGROUND: Since the onset of the global COVID-19 pandemic in early 2020, numerous studies have been conducted worldwide to understand our immune response to the virus and to vaccination. This study investigates the humoral response elicited by SARS-CoV-2 infection and by vaccination in the poorly studied population of food and retail workers. These occupations were classified as essential by the Public Health Agency of Canada, potentially placing this population at greater risk of infection. Such a risk requires access to reliable and adaptable serological assays that can be rapidly deployed to guide public health strategies. Here we investigate the benefits and limitations of applying adaptable, decentralized tests for population-level immune surveillance in response to a pandemic, even before centralized testing is available. METHODS AND FINDINGS: The 1.5-year study period spans from early 2021, when vaccination became available in this region, to mid-2022, following the emergence of the first Omicron variants. The cohort of 304 food and retail workers was recruited in the Québec City area. Participants attended five evenly spaced visits, providing blood samples as well as information on SARS-CoV-2 symptoms or risk factors, prior antigen or PCR test results and vaccination status, as well as work-related risk factors and protective measures. Parallel COVID-19 serological assays were performed using both a standardized chemiluminescent ELISA assay at the centralized platform operated in partnership with the Public Health Agency of Canada, and a semi-automated in-house colorimetric ELISA assay developed at our decentralized site. The YES/NO determination of SARS-CoV-2 vaccine seroconversion and/or infection events using the SARS-CoV-2 ancestral spike protein and nucleocapsid protein validated coherence of the centralized and decentralized assays. The flexibility of the decentralized assays allowed broadening the study to determine cross-reactivity of IgG directed against the spike protein of the SARS-CoV-2 Delta and Omicron VOCs, and IgM directed against the ancestral spike and nucleocapsid proteins. The nature of the data obtained in the decentralized assays allowed treatment with a recently developed mathematical transformation to obtain normal distribution, enabling ANOVA-Welsh statistical analysis. Although no significant differences were observed in humoral response as related to BMI, age, level of education, or chronic illnesses in this cohort of workers, statistically higher levels of vaccine-induced antibodies were observed for restaurant workers and hardware store workers in the early stages of the study, compared to workers in bars and grocery stores and in non-smokers versus smokers. CONCLUSIONS: This work highlights the importance of developing adaptable, decentralized tests for population-level immune surveillance in response to a pandemic, even before centralized testing is available. To our knowledge, no other study has reported such an extensive longitudinal investigation during key periods of the COVID-19 pandemic in a cohort of food and retail workers to analyze two types of immunoglobulin, three epitopes and antigens to three VOC. This study will inform strategies and measures to be implemented in the event of a future pandemic.
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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 | 0,002 | 0,003 |
| 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 | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».