Evaluating the contributions of strategies to prevent SARS-CoV-2 transmission in the healthcare setting: a modelling study
Notice bibliographique
Résumé
Summary Background Since its onset, the COVID-19 pandemic has caused significant morbidity and mortality worldwide, with particularly severe outcomes in healthcare institutions and congregate settings. To mitigate spread, healthcare systems have been cohorting patients to limit contacts between uninfected patients and potentially infected patients or healthcare workers (HCWs). A major challenge in managing the pandemic is the presence of currently asymptomatic individuals capable of transmitting the virus, who could introduce COVID-19 into uninfected cohorts. The optimal combination of personal protective equipment (PPE) and testing approaches to prevent these events is unclear, especially in light of ongoing limitations in access to both. Methods Using stochastic simulations with an SEIR model we quantified and compared the impacts of PPE use, patient and HCWs testing, and cohorting. Findings In the base case without testing or PPE, the healthcare system was rapidly overwhelmed, and became a net contributor to the force of infection. We found that effective use of PPE by both HCWs and patients could prevent this scenario, while random testing of apparently asymptomatic individuals on a weekly basis was less effective. We also found that even imperfect use of PPE could provide substantial protection by decreasing the force of infection, and that creation of smaller patient/HCW subcohorts can provide additional resilience to outbreak development. Interpretation These findings reinforce the importance of ensuring adequate PPE supplies even in the absence of testing, and provide support for strict subcohorting regimens to reduce outbreak potential in healthcare institutions. Funding National Institute of General Medical Sciences, National Institutes of Health. Research in context Evidence before Preserving healthcare from outbreaks of respiratory viruses is a longstanding concern, brought into sharp relief by the covid-19 pandemic. Early case series and numerous anecdotal reports suggest that health care workers (HCWs) and patients receiving treatment for conditions other than SARS-CoV-2 infection are at elevated risk of becoming infected, and the consequences of infections in long term care facilities are well known. In addition, the early stages of the pandemic have been marked by shortages of personal protective equipment (PPE) and diagnostic testing, but the most effective strategies for their use given the specific characteristics of SARS-CoV-2 transmission are unclear. Value added Our research plainly shows the importance of presymptomatic transmission. Given reasonable estimates of this, random testing of currently asymptomatic staff and patients once a week is not able to prevent large outbreaks. We show that PPE is, as expected, the most effective intervention and moreover even suboptimal PPE use is highly beneficial. To further limit transmission, we show the benefit of sub-cohorting into smaller groups of HCWs and patients. When the force of infection in the community is low, this can entirely prevent the establishment of infection in a large fraction of healthcare. Implications PPE should be used throughout healthcare, on the assumption that any patient or HCWs is potentially infected. Further work should determine the most effective means of PPE for the non-COVID cohort. If PPE resources are limited, whether in general or due to a second surge, we recommend subcohorting to limit the impact of introductions from the community.
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 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| 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 | 0,003 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».