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 critic...
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| Veröffentlicht in: | Canadian journal of public health 2021-10, Vol.112 (5), p.799-806 |
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| container_title | Canadian journal of public health |
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| creator | Hillmer, Michael P. Feng, Patrick McLaughlin, John R. Murty, V. Kumar Sander, Beate Greenberg, Anna Brown, Adalsteinn D. |
| 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. |
| doi_str_mv | 10.17269/s41997-021-00559-8 |
| format | Article |
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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.</description><identifier>ISSN: 0008-4263</identifier><identifier>ISSN: 1920-7476</identifier><identifier>EISSN: 1920-7476</identifier><identifier>DOI: 10.17269/s41997-021-00559-8</identifier><identifier>PMID: 34462892</identifier><language>eng</language><publisher>Cham: Springer Science + Business Media</publisher><subject>Communication ; Consensus ; Coronaviruses ; COVID-19 ; COVID-19 - epidemiology ; COVID-19 - prevention & control ; Decision making ; Disease transmission ; Epidemic models ; Epidemiology ; Estimates ; Government policy ; Humans ; Medicine ; Medicine & Public Health ; Modelling ; Ontario - epidemiology ; Pandemics ; Pandemics - prevention & control ; Public Health ; Public policy ; Research facilities ; SPECIAL SECTION ON COVID-19: INNOVATIONS IN POLICY AND PRACTICE</subject><ispartof>Canadian journal of public health, 2021-10, Vol.112 (5), p.799-806</ispartof><rights>The Canadian Public Health Association 2021</rights><rights>2021. The Canadian Public Health Association.</rights><rights>The Canadian Public Health Association 2021.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c500t-dd0dda27df2fe3a0b3a78ab13df8507ec0a87b86be31de54c70474f45f3bd49f3</citedby><cites>FETCH-LOGICAL-c500t-dd0dda27df2fe3a0b3a78ab13df8507ec0a87b86be31de54c70474f45f3bd49f3</cites><orcidid>0000-0002-4896-2730</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8404759/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8404759/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,41464,42533,51294,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34462892$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hillmer, Michael P.</creatorcontrib><creatorcontrib>Feng, Patrick</creatorcontrib><creatorcontrib>McLaughlin, John R.</creatorcontrib><creatorcontrib>Murty, V. Kumar</creatorcontrib><creatorcontrib>Sander, Beate</creatorcontrib><creatorcontrib>Greenberg, Anna</creatorcontrib><creatorcontrib>Brown, Adalsteinn D.</creatorcontrib><title>Ontario’s COVID-19 Modelling Consensus Table: mobilizing scientific expertise to support pandemic response</title><title>Canadian journal of public health</title><addtitle>Can J Public Health</addtitle><addtitle>Can J Public Health</addtitle><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.</description><subject>Communication</subject><subject>Consensus</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 - epidemiology</subject><subject>COVID-19 - prevention & control</subject><subject>Decision making</subject><subject>Disease transmission</subject><subject>Epidemic models</subject><subject>Epidemiology</subject><subject>Estimates</subject><subject>Government policy</subject><subject>Humans</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Modelling</subject><subject>Ontario - epidemiology</subject><subject>Pandemics</subject><subject>Pandemics - prevention & control</subject><subject>Public Health</subject><subject>Public policy</subject><subject>Research facilities</subject><subject>SPECIAL SECTION ON COVID-19: INNOVATIONS IN POLICY AND PRACTICE</subject><issn>0008-4263</issn><issn>1920-7476</issn><issn>1920-7476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>BEC</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kc1u1DAUhS0EokPbJ0BUkdh049a_sb1BQqF_0qDZTLu1nNgZMsrYUztBYsdr8Ho8CW4zDLSLrizrfuf4Hh8A3mN0hgUp1XliWCkBEcEQIc4VlK_ADCuCoGCifA1mCCEJGSnpAXiX0jpfKRX0LTigjJVEKjIDZws_mNiF3z9_paJa3N18gVgVX4N1fd_5VVEFn5xPYyqWpu7dEXjTmj654915CG4vL5bVNZwvrm6qz3PYcIQGaC2y1hBhW9I6alBNjZCmxtS2kiPhGmSkqGVZO4qt46wRiAnWMt7S2jLV0kPwafLdjvXG2cb5IZpeb2O3MfGHDqbTTye--6ZX4buWLDtxlQ1OdwYx3I8uDXrTpSaHMt6FMWnCS6GkxExk9OMzdB3G6HO8TMn8TQLjB4pOVBNDStG1-2Uw0o996KkPnfvQj31omVUn_-fYa_4WkAE2ASmP_MrFf4-_7Pthkq3TEOLelghcYqw4_QMus6Ag</recordid><startdate>20211001</startdate><enddate>20211001</enddate><creator>Hillmer, Michael P.</creator><creator>Feng, Patrick</creator><creator>McLaughlin, John R.</creator><creator>Murty, V. Kumar</creator><creator>Sander, Beate</creator><creator>Greenberg, Anna</creator><creator>Brown, Adalsteinn D.</creator><general>Springer Science + Business Media</general><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>0-V</scope><scope>3V.</scope><scope>4S-</scope><scope>4U-</scope><scope>7QP</scope><scope>7QR</scope><scope>7RV</scope><scope>7T2</scope><scope>7TK</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88C</scope><scope>88E</scope><scope>88J</scope><scope>8C1</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FQ</scope><scope>8FV</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ALSLI</scope><scope>AN0</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DPSOV</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>KC-</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>M2L</scope><scope>M2O</scope><scope>M2R</scope><scope>M3G</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>P64</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0X</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-4896-2730</orcidid></search><sort><creationdate>20211001</creationdate><title>Ontario’s COVID-19 Modelling Consensus Table</title><author>Hillmer, Michael P. ; Feng, Patrick ; McLaughlin, John R. ; Murty, V. Kumar ; Sander, Beate ; Greenberg, Anna ; Brown, Adalsteinn D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c500t-dd0dda27df2fe3a0b3a78ab13df8507ec0a87b86be31de54c70474f45f3bd49f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Communication</topic><topic>Consensus</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>COVID-19 - epidemiology</topic><topic>COVID-19 - prevention & control</topic><topic>Decision making</topic><topic>Disease transmission</topic><topic>Epidemic models</topic><topic>Epidemiology</topic><topic>Estimates</topic><topic>Government policy</topic><topic>Humans</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Modelling</topic><topic>Ontario - epidemiology</topic><topic>Pandemics</topic><topic>Pandemics - prevention & control</topic><topic>Public Health</topic><topic>Public policy</topic><topic>Research facilities</topic><topic>SPECIAL SECTION ON COVID-19: INNOVATIONS IN POLICY AND PRACTICE</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hillmer, Michael P.</creatorcontrib><creatorcontrib>Feng, Patrick</creatorcontrib><creatorcontrib>McLaughlin, John R.</creatorcontrib><creatorcontrib>Murty, V. 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Kumar</au><au>Sander, Beate</au><au>Greenberg, Anna</au><au>Brown, Adalsteinn D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Ontario’s COVID-19 Modelling Consensus Table: mobilizing scientific expertise to support pandemic response</atitle><jtitle>Canadian journal of public health</jtitle><stitle>Can J Public Health</stitle><addtitle>Can J Public Health</addtitle><date>2021-10-01</date><risdate>2021</risdate><volume>112</volume><issue>5</issue><spage>799</spage><epage>806</epage><pages>799-806</pages><issn>0008-4263</issn><issn>1920-7476</issn><eissn>1920-7476</eissn><abstract>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.</abstract><cop>Cham</cop><pub>Springer Science + Business Media</pub><pmid>34462892</pmid><doi>10.17269/s41997-021-00559-8</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-4896-2730</orcidid><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 0008-4263 |
| ispartof | Canadian journal of public health, 2021-10, Vol.112 (5), p.799-806 |
| issn | 0008-4263 1920-7476 1920-7476 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8404759 |
| source | MEDLINE; SpringerLink; PubMed Central; REPÈRE - Free; EZB Electronic Journals Library |
| subjects | Communication Consensus Coronaviruses COVID-19 COVID-19 - epidemiology COVID-19 - prevention & control Decision making Disease transmission Epidemic models Epidemiology Estimates Government policy Humans Medicine Medicine & Public Health Modelling Ontario - epidemiology Pandemics Pandemics - prevention & control Public Health Public policy Research facilities SPECIAL SECTION ON COVID-19: INNOVATIONS IN POLICY AND PRACTICE |
| title | Ontario’s COVID-19 Modelling Consensus Table: mobilizing scientific expertise to support pandemic response |
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