Population Behavior Changes Underlying Phasic Shifts of SARS-CoV-2 Exposure Settings Across 3 Omicron Epidemic Waves in Hong Kong: Prospective Cohort Study
Notice bibliographique
Résumé
BACKGROUND: Exposure risk was shown to have affected individual susceptibility and the epidemic spread of COVID-19. The dynamics of risk by and across exposure settings alongside the variations following the implementation of social distancing interventions are understudied. OBJECTIVE: This study aims to examine the population's trajectory of exposure risk in different settings and its association with SARS-CoV-2 infection across 3 consecutive Omicron epidemic waves in Hong Kong. METHODS: From March to June 2022, invitation letters were posted to 41,132 randomly selected residential addresses for the recruitment of households into a prospective population cohort. Through web-based monthly surveys coupled with email reminders, a representative from each enrolled household self-reported incidents of SARS-CoV-2 infections, COVID-19 vaccination uptake, their activity pattern in the workplace, and daily and social settings in the preceding month. As a proxy of their exposure risk, the reported activity trend in each setting was differentiated into trajectories based on latent class growth analyses. The associations of different trajectories of SARS-CoV-2 infection overall and by Omicron wave (wave 1: February-April; wave 2: May-September; wave 3: October-December) in 2022 were evaluated by using Cox proportional hazards models and Kaplan-Meier analysis. RESULTS: In total, 33,501 monthly responses in the observation period of February-December 2022 were collected from 5321 individuals, with 41.7% (2221/5321) being male and a median age of 46 (IQR 34-57) years. Against an expanding COVID-19 vaccination coverage from 81.9% to 95.9% for 2 doses and 20% to 77.7% for 3 doses, the cumulative incidence of SARS-CoV-2 infection escalated from <0.2% to 25.3%, 32.4%, and 43.8% by the end of waves 1, 2, and 3, respectively. Throughout February-December 2022, 52.2% (647/1240) of participants had worked regularly on-site, 28.7% (356/1240) worked remotely, and 19.1% (237/1240) showed an assorted pattern. For daily and social settings, 4 and 5 trajectories were identified, respectively, with 11.5% (142/1240) and 14.6% (181/1240) of the participants gauged to have a high exposure risk. Compared to remote working, working regularly on-site (adjusted hazard ratio [aHR] 1.47, 95% CI 1.19-1.80) and living in a larger household (aHR 1.12, 95% CI 1.06-1.18) were associated with a higher risk of SARS-CoV-2 infection in wave 1. Those from the highest daily exposure risk trajectory (aHR 1.46, 95% CI 1.07-2.00) and the second highest social exposure risk trajectory (aHR 1.52, 95% CI 1.18-1.97) were also at an increased risk of infection in waves 2 and 3, respectively, relative to the lowest risk trajectory. CONCLUSIONS: In an infection-naive population, SARS-CoV-2 transmission was predominantly initiated at the workplace, accelerated in the household, and perpetuated in the daily and social environments, as stringent restrictions were scaled down. These patterns highlight the phasic shift of exposure settings, which is important for informing the effective calibration of targeted social distancing measures as an alternative to lockdown.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».