IMPACT OF UNIVERSITY RE-OPENING ON TOTAL COMMUNITY COVID-19 BURDEN
Notice bibliographique
Résumé
ABSTRACT Purpose Post-secondary students have higher than average contacts than the general population due to congregate living, use of public transit, high-density academic and social activities, and employment in the services sector. We evaluated the impact of a large student population returning to a mid-sized city currently experiencing a low rate of COVID-19 on community health outcomes. We consider whether targeted routine or one-time screening in this population can mitigate community COVID-19 impacts. Methods We developed a dynamic transmission model of COVID-19 subdivided into three interacting populations: general population, university students, and long-term care residents. We parameterized the model using the medical literature and expert opinion. We calibrated the model to the observed outcomes in a mid-sized Canadian city between March 1 and August 15, 2020 prior to the arrival of a relatively large post-secondary student population. We evaluated the impact of the student population (20,000 people arriving on September 1) on cumulative COVID-19 infections over the fall semester, the timing of peak infections, the timing and peak level of critical care occupancy, and the timing of re-engaged social and economic restrictions. We consider multiple scenarios with different student and general population COVID-19 prevention behaviours as well as different COVID-19 screening strategies in students. Results In a city with low levels of COVID-19 activity, the return of a relatively large student population substantially increases the total number of COVID-19 infections in the community. In a scenario in which students immediately engage in a 24% contact reduction compared to pre-COVID levels, the total number of infections in the community increases by 87% (from 3,900 without the students to 7,299 infections with the students), with 71% of the incremental infections occurring in the general population, causing social and economic restrictions to be re-engaged 3 weeks earlier and an incremental 17 COVID-19 deaths. Scenarios in which students have an initial, short-term increase in contacts with other students before engaging in contact reduction behaviours can increase infections in the community by 150% or more. In such scenarios, screening asymptomatic students every 5 days reduces the number of infections attributable to the introduction of the university student population by 42% and delays the re-engagement of social and economic restrictions by 1 week. Compared to screening every 5 days, one-time mass screening of students prevents fewer infections, but is highly efficient in terms of infections prevented per screening test performed. Discussion University students are highly inter-connected with the city communities in which they live and go to school, and they have a higher number of contacts than the general population. High density living environments, enthusiasm for the new school year, and relatively high rates of asymptomatic presentation may decrease their self-protective behaviours and contribute to increased community transmission of COVID-19 affecting at-risk members of the city community. Screening targeted at this population provides significant public health benefits to the community through averted infections, critical care admissions, and COVID-19 deaths.
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 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».