SARS-CoV-2 Infections in a Triad of Primary School Learners (Grades 1-7), Their Parents, and Teachers in KwaZulu-Natal, South Africa: Protocol for a Cross-Sectional and Nested Case-Cohort Study
Notice bibliographique
Résumé
BACKGROUND: In low- and middle-income countries (LMICs) such as South Africa, there is paucity of data on SARS-CoV-2 infections among children attending school, including seroprevalence and transmission dynamics. OBJECTIVE: This pilot study aims to assess (1) the prevalence of self-reported or confirmed SARS-CoV-2 prior infections, COVID-19 symptoms (including long COVID), seroprevalence of SARS-CoV-2 antibodies, and general/mental health, (2) longitudinal changes in SARS-CoV-2 seroprevalence, and (3) SARS-CoV-2 acute infections, immune responses, transmission dynamics, and symptomatic versus asymptomatic contacts in a unique cohort of unvaccinated primary school learners, their parents, teachers, and close contacts in semirural primary school settings. METHODS: Learners (grades 1-7) from primary schools in KwaZulu-Natal, South Africa, their parents, and teachers will be invited to enroll into the COVID kids school study (CoKiDSS). CoKiDSS comprises 3 parts: a cross-sectional survey (N=640), a follow-up survey (n=300), and a nested case-cohort substudy. Finger-prick blood and saliva samples will be collected for serological and future testing, respectively, in the cross-sectional (451 learners:147 parents:42 teachers) and follow-up (210 learners:70 parents:20 teachers) surveys. The nested case-cohort substudy will include cases from the cross-sectional survey with confirmed current SARS-CoV-2 infection (n=30) and their close contacts (n=up to 10 per infected participant). Finger-prick blood (from all substudy participants), venous blood (from cases), and nasal swabs (from cases and contacts) will be collected for serological testing, immunological testing, and viral genome sequencing, respectively. Questionnaires covering sociodemographic and general and mental health information, prior and current SARS-CoV-2 symptoms and testing information, vaccination status, preventative behavior, and lifestyle will be administered. Statistical methods will include generalized linear mixed models, intracluster correlation, descriptive analysis, and graphical techniques. RESULTS: A total of 645 participants were enrolled into the cross-sectional survey between May and August 2023. A subset of 300 participants were followed up in the follow-up survey in October 2023. Screening of the participants into the nested case-cohort substudy is planned between November 2023 and September 2024. Data cleanup and analysis for the cross-sectional survey is complete, while those for the follow-up survey and nested case substudy will be completed by the third quarter of 2024. The dissemination and publication of results is anticipated for the fourth quarter of 2024. CONCLUSIONS: This study provides data from an LMIC setting on the impact of SARS-CoV-2 on school-attending learners, their parents, and teachers 3 years after the SARS-CoV-2 pandemic was declared and 21-24 months after resumption of normal school attendance. In particular, this study will provide data on the prevalence of self-reported or confirmed SARS-CoV-2 prior infection, prior and current symptoms, seroprevalence, changes in seroprevalence, SARS-CoV-2 transmission, SARS-CoV-2 adaptive immune responses, and symptoms of long COVID and mental health among a triad of learners, their parents, and teachers. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52713.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».