Analysing the distribution of SARS-CoV-2 infections in schools and shelters
Notice bibliographique
Résumé
The recent SARS-CoV-2 pandemic severely impacted lives around the world. In this study, we limit our focus to the school and homeless shelter communities. Widespread school closures were enacted in an attempt to mitigate transmission among the school populace, which adversely affected the academic performance of students, specifically those from a low socioeconomic background, as suggested by recent studies. Shelters house a highly vulnerable population that have access to limited housing and healthcare opportunities in the event of an outbreak. A deeper understanding of SARS-CoV-2 outbreaks would enable policymakers to respond to future pandemics through precision preventive measures in schools and shelters. The infection distribution encapsulates the statistics of outbreak spread, highlighting the key factors that drive transmission dynamics in these indoor locations. Though past works have studied such distributions from infection data, modeling them remain relatively unexplored.In this study, our primary objective is to model the probability distribution of SARS-CoV-2 secondary infections from first principles, resulting in a distribution modeled exclusively from the underlying physics coupled with the biological parameters of the virus. The model accounts for both the long-range airborne transmission route arising from smaller aerosols airborne for extended periods, and the short-range route encompassing direct exposure to a wide range of aerosol sizes in proximity to the index case. Expected sources of transmission variability like viral load of the index case, dose-response, occupancy, indoor flow, virus-half life, etc., have been accounted for in model development. To validate our model, available infection data from the Ontario public school system and Toronto shelters was collected and processed. Comparison of infection distributions from these datasets with modeled results display strong quantitative and qualitative match, demonstrating the model’s capability at capturing the key mechanisms that underpin the transmission process. The results showcase the overdispersed nature of SARS-CoV-2 transmission arising from rare but high-impact superspreading events catalyzed by long-range transmission, along with frequent low-impact short-range transmission driven outbreaks. As the results are informed by the underlying governing parameters, effect of various mitigation measures on a large-scale system can be studied through appropriate modification of the model inputs, enabling the user to find optimal measures at combating future outbreaks.This study puts forward a practical tool capable of predicting indoor airborne transmission statistics facilitating pandemic readiness for the future while providing insights into the fundamental mechanics governing the overdispersed nature of SARS-CoV-2 outbreak in schools and shelters.
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 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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 ».