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Ontario’s COVID-19 Modelling Consensus Table: mobilizing scientific expertise to support pandemic response
SETTING: COVID-19 has highlighted the need for credible epidemiological models to inform pandemic policy. Traditional mechanisms of commissioning research are ill-suited to guide policy during a rapidly evolving pandemic. At the same time, contracting with a single centre of expertise has been criti...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8404759/ https://www.ncbi.nlm.nih.gov/pubmed/34462892 http://dx.doi.org/10.17269/s41997-021-00559-8 |
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author | Hillmer, Michael P. Feng, Patrick McLaughlin, John R. Murty, V. Kumar Sander, Beate Greenberg, Anna Brown, Adalsteinn D. |
author_facet | Hillmer, Michael P. Feng, Patrick McLaughlin, John R. Murty, V. Kumar Sander, Beate Greenberg, Anna Brown, Adalsteinn D. |
author_sort | Hillmer, Michael P. |
collection | PubMed |
description | SETTING: COVID-19 has highlighted the need for credible epidemiological models to inform pandemic policy. Traditional mechanisms of commissioning research are ill-suited to guide policy during a rapidly evolving pandemic. At the same time, contracting with a single centre of expertise has been criticized for failing to reflect challenges inherent in specific modelling approaches. INTERVENTION: This report describes an alternative approach to mobilizing scientific expertise. Ontario’s COVID-19 Modelling Consensus Table (MCT) was created in March 2020 to enable rapid communication of credible estimates of the impact of COVID-19 and to accelerate learning on how the disease is spreading and what could slow its transmission. The MCT is a partnership between the province and academic modellers and consists of multiple groups of experts, health system leaders, and senior decision-makers. Armed with Ministry of Health data, the MCT meets once per week to share results from modelling exercises, generate consensus judgements of the likely future impact of COVID-19, and discuss decision-makers’ priorities. OUTCOMES: The MCT has enabled swift access to data for participants, a structure for developing consensus estimates and communicating these to decision-makers, credible models to inform health system planning, and increased transparency in public reporting of COVID-19 data. It has also facilitated the rapid publication of research findings and its incorporation into government policy. IMPLICATIONS: The MCT approach is one way to quickly draw on scientific advice outside of government and public health agencies. Beyond speed, this approach allows for nimbleness as experts from different organizations can be added as needed. It also shows how universities and research institutes have a role to play in crisis situations, and how this expertise can be marshalled to inform policy while respecting academic freedom and confidentiality. |
format | Online Article Text |
id | pubmed-8404759 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-84047592021-08-31 Ontario’s COVID-19 Modelling Consensus Table: mobilizing scientific expertise to support pandemic response Hillmer, Michael P. Feng, Patrick McLaughlin, John R. Murty, V. Kumar Sander, Beate Greenberg, Anna Brown, Adalsteinn D. Can J Public Health Special Section on COVID-19: Innovations in Policy and Practice SETTING: COVID-19 has highlighted the need for credible epidemiological models to inform pandemic policy. Traditional mechanisms of commissioning research are ill-suited to guide policy during a rapidly evolving pandemic. At the same time, contracting with a single centre of expertise has been criticized for failing to reflect challenges inherent in specific modelling approaches. INTERVENTION: This report describes an alternative approach to mobilizing scientific expertise. Ontario’s COVID-19 Modelling Consensus Table (MCT) was created in March 2020 to enable rapid communication of credible estimates of the impact of COVID-19 and to accelerate learning on how the disease is spreading and what could slow its transmission. The MCT is a partnership between the province and academic modellers and consists of multiple groups of experts, health system leaders, and senior decision-makers. Armed with Ministry of Health data, the MCT meets once per week to share results from modelling exercises, generate consensus judgements of the likely future impact of COVID-19, and discuss decision-makers’ priorities. OUTCOMES: The MCT has enabled swift access to data for participants, a structure for developing consensus estimates and communicating these to decision-makers, credible models to inform health system planning, and increased transparency in public reporting of COVID-19 data. It has also facilitated the rapid publication of research findings and its incorporation into government policy. IMPLICATIONS: The MCT approach is one way to quickly draw on scientific advice outside of government and public health agencies. Beyond speed, this approach allows for nimbleness as experts from different organizations can be added as needed. It also shows how universities and research institutes have a role to play in crisis situations, and how this expertise can be marshalled to inform policy while respecting academic freedom and confidentiality. Springer International Publishing 2021-08-30 /pmc/articles/PMC8404759/ /pubmed/34462892 http://dx.doi.org/10.17269/s41997-021-00559-8 Text en © The Canadian Public Health Association 2021 |
spellingShingle | Special Section on COVID-19: Innovations in Policy and Practice Hillmer, Michael P. Feng, Patrick McLaughlin, John R. Murty, V. Kumar Sander, Beate Greenberg, Anna Brown, Adalsteinn D. Ontario’s COVID-19 Modelling Consensus Table: mobilizing scientific expertise to support pandemic response |
title | Ontario’s COVID-19 Modelling Consensus Table: mobilizing scientific expertise to support pandemic response |
title_full | Ontario’s COVID-19 Modelling Consensus Table: mobilizing scientific expertise to support pandemic response |
title_fullStr | Ontario’s COVID-19 Modelling Consensus Table: mobilizing scientific expertise to support pandemic response |
title_full_unstemmed | Ontario’s COVID-19 Modelling Consensus Table: mobilizing scientific expertise to support pandemic response |
title_short | Ontario’s COVID-19 Modelling Consensus Table: mobilizing scientific expertise to support pandemic response |
title_sort | ontario’s covid-19 modelling consensus table: mobilizing scientific expertise to support pandemic response |
topic | Special Section on COVID-19: Innovations in Policy and Practice |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8404759/ https://www.ncbi.nlm.nih.gov/pubmed/34462892 http://dx.doi.org/10.17269/s41997-021-00559-8 |
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