Evidence map›Paper›PMID 39060710›Full record

ReviewCanadian journal of public health = Revue canadienne de sante publique2024

Canada's provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses.

Yiqing Xia, Jorge Luis Flores Anato, Caroline Colijn, Naveed Janjua, Mike Irvine, Tyler Williamson, Marie B Varughese, Michael Li, Nathaniel Osgood, David J D Earn and 9 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Canadian journal of public health = Revue canadienne de sante publique, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Mathematical modelling for pandemic preparedness in Canada: Learning from COVID-19.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2024
    Article
  3. COVID-19 data and modeling: We need to learn from and act on our experiences.Canadian journal of public health = Revue canadienne de sante publique · 2024
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

Yiqing XiaDepartment of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada.
Jorge Luis Flores AnatoDepartment of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada.
Caroline ColijnDepartment of Mathematics, Faculty of Science, Simon Fraser University, Burnaby, BC, Canada.
Naveed JanjuaSchool of Population and Public Health, University of British Columbia, Vancouver, BC, Canada.
Mike IrvineBritish Columbia Centre for Disease Control (BCCDC), Vancouver, BC, Canada.
Tyler WilliamsonDepartment of Community Health Sciences, University of Calgary, Calgary, AB, Canada.
Marie B VarugheseAnalytics and Performance Reporting Branch, Alberta Health, Edmonton, AB, Canada.
Michael LiDepartment of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada.
Nathaniel OsgoodDepartment of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.
David J D EarnDepartment of Mathematics & Statistics, McMaster University, Hamilton, ON, Canada.
Beate SanderInstitute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Lauren E CiprianoIvey Business School, University of Western Ontario, London, ON, Canada.
Kumar MurtyDepartment of Mathematics, University of Toronto, Toronto, ON, Canada.
Fanyu XiuDepartment of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada.
Arnaud GodinDepartment of Medicine, Faculty of Medicine and Health Science, McGill University, Montréal, QC, Canada.
David BuckeridgeDepartment of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada.
Amy HurfordDepartment of Biology and Department of Mathematics and Statistics, Faculty of Science, Memorial University of Newfoundland and Labrador, St. John's, NL, Canada.
Sharmistha MishraInstitute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Mathieu Maheu-GirouxDepartment of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada. mathieu.maheu-giroux@mcgill.ca.ORCID 0000-0002-8363-4388

Funding

CIHR
6 · The paper itself

Abstract

settingMathematical modelling played an important role in the public health response to COVID-19 in Canada. Variability in epidemic trajectories, modelling approaches, and data infrastructure across provinces provides a unique opportunity to understand the factors that shaped modelling strategies.

interventionProvinces implemented stringent pandemic interventions to mitigate SARS-CoV-2 transmission, considering evidence from epidemic models. This study aimed to summarize provincial COVID-19 modelling efforts. We identified modelling teams working with provincial decision-makers, through referrals and membership in Canadian modelling networks. Information on models, data sources, and knowledge translation were abstracted using standardized instruments. OUTCOMES: We obtained information from six provinces. For provinces with sustained community transmission, initial modelling efforts focused on projecting epidemic trajectories and healthcare demands, and evaluating impacts of proposed interventions. In provinces with low community transmission, models emphasized quantifying importation risks. Most of the models were compartmental and deterministic, with projection horizons of a few weeks. Models were updated regularly or replaced by new ones, adapting to changing local epidemic dynamics, pathogen characteristics, vaccines, and requests from public health. Surveillance datasets for cases, hospitalizations and deaths, and serological studies were the main data sources for model calibration. Access to data for modelling and the structure for knowledge translation differed markedly between provinces. IMPLICATION: Provincial modelling efforts during the COVID-19 pandemic were tailored to local contexts and modulated by available resources. Strengthening Canadian modelling capacity, developing and sustaining collaborations between modellers and governments, and ensuring earlier access to linked and timely surveillance data could help improve pandemic preparedness.

Indexed as

COVID-19Models, TheoreticalCanadaHumansPandemicsCOVID-19Knowledge translationMathematical modellingPandemicPolicymakingSARS-CoV-2

Identifiers

PMID39060710
PMCPMC11382646

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.