Evidence map›Paper›PMID 33330884›Full record

ArticlemedRxiv : the preprint server for health sciences2021

Multifaceted strategies for the control of COVID-19 outbreaks in long-term care facilities in Ontario, Canada.

Thomas N Vilches, Shokoofeh Nourbakhsh, Kevin Zhang, Lyndon Juden-Kelly, Lauren E Cipriano, Joanne M Langley, Pratha Sah, Alison P Galvani, Seyed M Moghadas

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 13 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 6 institutions in 3 countries.

Thomas N VilchesInstitute of Mathematics, Statistics and Scientific Computing, University of Campinas, Campinas SP, Brazil.
Shokoofeh NourbakhshAgent-Based Modelling Laboratory, York University, Toronto, Ontario, M3J 1P3 Canada.
Kevin ZhangFaculty of Medicine, University of Toronto, Toronto, Ontario, M5S 1A8 Canada.
Lyndon Juden-KellyAgent-Based Modelling Laboratory, York University, Toronto, Ontario, M3J 1P3 Canada.
Lauren E CiprianoIvey Business School and Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University, London, Ontario N6G 0N1 Canada.
Joanne M LangleyCanadian Center for Vaccinology, Dalhousie University, IWK Health Centre and Nova Scotia Health Authority, Halifax, Nova Scotia, B3K 6R8 Canada.
Pratha SahCenter for Infectious Disease Modeling and Analysis (CIDMA), Yale School of Public Health, New Haven, Connecticut, USA.
Alison P GalvaniCenter for Infectious Disease Modeling and Analysis (CIDMA), Yale School of Public Health, New Haven, Connecticut, USA.
Seyed M MoghadasAgent-Based Modelling Laboratory, York University, Toronto, Ontario, M3J 1P3 Canada.
York University · CAYale University · USDalhousie University · CAUniversidade Estadual de Campinas (UNICAMP) · BRUniversity of Toronto · CAWestern University · CA

Funding

Accelerating viral outbreak detection in US cities using mechanistic models, machine learning and diverse geospatial dataR01AI151176 · NIAID · YALE UNIVERSITY · PI GALVANI, ALISON P, MEYERS, LAUREN ANCEL · 2020 to 2024
$3.9M
NIAID NIH HHS R01 AI151176
6 · The paper itself

Abstract

The novel coronavirus disease 2019 (COVID-19) has caused severe outbreaks in Canadian long-term care facilities (LTCFs). In Canada, over 80% of COVID-19 deaths during the first pandemic wave occurred in LTCFs. We sought to evaluate the effect of mitigation measures in LTCFs including frequent testing of staff, and vaccination of staff and residents. We developed an agent-based transmission model and parameterized it with disease-specific estimates, temporal sensitivity of nasopharyngeal and saliva testing, results of vaccine efficacy trials, and data from initial COVID-19 outbreaks in LTCFs in Ontario, Canada. Characteristics of staff and residents, including contact patterns, were integrated into the model with age-dependent risk of hospitalization and death. Estimates of infection and outcomes were obtained and 95% credible intervals were generated using a bias-corrected and accelerated bootstrap method. Weekly routine testing of staff with 2-day turnaround time reduced infections among residents by at least 25.9% (95% CrI: 23.3% - 28.3%), compared to baseline measures of mask-wearing, symptom screening, and staff cohorting alone. A similar reduction of hospitalizations and deaths was achieved in residents. Vaccination averted 2-4 times more infections in both staff and residents as compared to routine testing, and markedly reduced hospitalizations and deaths among residents by 95.9% (95% CrI: 95.4% - 96.3%) and 95.8% (95% CrI: 95.5% - 96.1%), respectively, over 200 days from the start of vaccination. Vaccination could have a substantial impact on mitigating disease burden among residents, but may not eliminate the need for other measures before population-level control of COVID-19 is achieved.

Indexed as

agent-based simulationsCOVID-19long-term caretestingvaccination

Identifiers

PMID33330884
PMCPMC7743093
OpenAlexW3112981074

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.