Evidence map›Paper›PMID 32269018›Full record

ArticleCMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne2020

Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada.

Ashleigh R Tuite, David N Fisman, Amy L Greer

Open access · goldAbstract read
In one paragraph

Article in CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 183 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
183citing papers in PubMed, 6 pooled it
14.9field-weighted citation impact, top 1% of its field
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

183 citing papers in PubMed, 6 syntheses or guidelines pooled it, 466 citations in OpenAlex.

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  3. Saliva sample for the massive screening of SARS-CoV-2 infection: a systematic review.Oral surgery, oral medicine, oral pathology and oral radiology · 2021
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123 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Ashleigh R TuiteDalla Lana School of Public Health (Tuite, Fisman), University of Toronto, Ont.; Department of Population Medicine (Greer), University of Guelph, Guelph, Ont. ashleigh.tuite@utoronto.ca.
David N FismanDalla Lana School of Public Health (Tuite, Fisman), University of Toronto, Ont.; Department of Population Medicine (Greer), University of Guelph, Guelph, Ont.
Amy L GreerDalla Lana School of Public Health (Tuite, Fisman), University of Toronto, Ont.; Department of Population Medicine (Greer), University of Guelph, Guelph, Ont.
Public Health Ontario · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPhysical-distancing interventions are being used in Canada to slow the spread of severe acute respiratory syndrome coronavirus 2, but it is not clear how effective they will be. We evaluated how different nonpharmaceutical interventions could be used to control the coronavirus disease 2019 (COVID-19) pandemic and reduce the burden on the health care system.

methodsWe used an age-structured compartmental model of COVID-19 transmission in the population of Ontario, Canada. We compared a base case with limited testing, isolation and quarantine to scenarios with the following: enhanced case finding, restrictive physical-distancing measures, or a combination of enhanced case finding and less restrictive physical distancing. Interventions were either implemented for fixed durations or dynamically cycled on and off, based on projected occupancy of intensive care unit (ICU) beds. We present medians and credible intervals from 100 replicates per scenario using a 2-year time horizon.

resultsWe estimated that 56% (95% credible interval 42%-63%) of the Ontario population would be infected over the course of the epidemic in the base case. At the epidemic peak, we projected 107 000 (95% credible interval 60 760-149 000) cases in hospital (non-ICU) and 55 500 (95% credible interval 32 700-75 200) cases in ICU. For fixed-duration scenarios, all interventions were projected to delay and reduce the height of the epidemic peak relative to the base case, with restrictive physical distancing estimated to have the greatest effect. Longer duration interventions were more effective. Dynamic interventions were projected to reduce the proportion of the population infected at the end of the 2-year period and could reduce the median number of cases in ICU below current estimates of Ontario's ICU capacity.

interpretationWithout substantial physical distancing or a combination of moderate physical distancing with enhanced case finding, we project that ICU resources would be overwhelmed. Dynamic physical distancing could maintain health-system capacity and also allow periodic psychological and economic respite for populations.

Indexed as

Models, TheoreticalCoronavirus InfectionsCOVID-19HumansInfection ControlOntarioPandemicsPneumonia, Viral

Identifiers

PMID32269018
PMCPMC7234271
OpenAlexW3015623674

What OpenQuestion holds

Textmetadata
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.