Canada’s provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses
Setting Mathematical 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....
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| Veröffentlicht in: | Canadian journal of public health 2024-08, Vol.115 (4), p.541-557 |
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| creator | Xia, Yiqing Flores Anato, Jorge Luis Colijn, Caroline Janjua, Naveed Irvine, Mike Williamson, Tyler Varughese, Marie B. Li, Michael Osgood, Nathaniel Earn, David J. D. Sander, Beate Cipriano, Lauren E. Murty, Kumar Xiu, Fanyu Godin, Arnaud Buckeridge, David Hurford, Amy Mishra, Sharmistha Maheu-Giroux, Mathieu |
| description | Setting
Mathematical 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.
Intervention
Provinces 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. |
| doi_str_mv | 10.17269/s41997-024-00910-9 |
| format | Article |
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Mathematical 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.
Intervention
Provinces 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.</description><identifier>ISSN: 0008-4263</identifier><identifier>ISSN: 1920-7476</identifier><identifier>EISSN: 1920-7476</identifier><identifier>DOI: 10.17269/s41997-024-00910-9</identifier><identifier>PMID: 39060710</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Canada - epidemiology ; COVID-19 ; COVID-19 - epidemiology ; COVID-19 - prevention & control ; Data sources ; Disease transmission ; Dynamic structural analysis ; Epidemic models ; Epidemics ; Humans ; Importation ; Innovations in Policy and Practice ; Mathematical models ; Medicine ; Medicine & Public Health ; Models, Theoretical ; Pandemics ; Provinces ; Public Health ; Severe acute respiratory syndrome coronavirus 2 ; Surveillance ; Trajectory analysis ; Translation ; Uniqueness ; Viral diseases</subject><ispartof>Canadian journal of public health, 2024-08, Vol.115 (4), p.541-557</ispartof><rights>The Author(s) under exclusive license to The Canadian Public Health Association 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2024. The Author(s) under exclusive license to The Canadian Public Health Association.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c303t-3b386d889415856952698d52546957467aeb8fa27bdab378e40adf7242c634513</cites><orcidid>0000-0002-8363-4388</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.17269/s41997-024-00910-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.17269/s41997-024-00910-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51298</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39060710$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Xia, Yiqing</creatorcontrib><creatorcontrib>Flores Anato, Jorge Luis</creatorcontrib><creatorcontrib>Colijn, Caroline</creatorcontrib><creatorcontrib>Janjua, Naveed</creatorcontrib><creatorcontrib>Irvine, Mike</creatorcontrib><creatorcontrib>Williamson, Tyler</creatorcontrib><creatorcontrib>Varughese, Marie B.</creatorcontrib><creatorcontrib>Li, Michael</creatorcontrib><creatorcontrib>Osgood, Nathaniel</creatorcontrib><creatorcontrib>Earn, David J. D.</creatorcontrib><creatorcontrib>Sander, Beate</creatorcontrib><creatorcontrib>Cipriano, Lauren E.</creatorcontrib><creatorcontrib>Murty, Kumar</creatorcontrib><creatorcontrib>Xiu, Fanyu</creatorcontrib><creatorcontrib>Godin, Arnaud</creatorcontrib><creatorcontrib>Buckeridge, David</creatorcontrib><creatorcontrib>Hurford, Amy</creatorcontrib><creatorcontrib>Mishra, Sharmistha</creatorcontrib><creatorcontrib>Maheu-Giroux, Mathieu</creatorcontrib><title>Canada’s provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses</title><title>Canadian journal of public health</title><addtitle>Can J Public Health</addtitle><addtitle>Can J Public Health</addtitle><description>Setting
Mathematical 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.
Intervention
Provinces 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.</description><subject>Canada - epidemiology</subject><subject>COVID-19</subject><subject>COVID-19 - epidemiology</subject><subject>COVID-19 - prevention & control</subject><subject>Data sources</subject><subject>Disease transmission</subject><subject>Dynamic structural analysis</subject><subject>Epidemic models</subject><subject>Epidemics</subject><subject>Humans</subject><subject>Importation</subject><subject>Innovations in Policy and Practice</subject><subject>Mathematical models</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Models, Theoretical</subject><subject>Pandemics</subject><subject>Provinces</subject><subject>Public Health</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Surveillance</subject><subject>Trajectory analysis</subject><subject>Translation</subject><subject>Uniqueness</subject><subject>Viral diseases</subject><issn>0008-4263</issn><issn>1920-7476</issn><issn>1920-7476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kc1u1DAUhS0EokPhCZCQJTZsTO3Y8Q-7aqBQqVI3wNZyEqe4mtjBN1PEAonX4PX6JNyZKUViwcY_19859vUh5Lngr4VptDsBJZwzjDeKce4EZ-4BWQnXcGaU0Q_JinNumWq0PCJPAK5xK6WRj8mRdFxzI_iK_FiHHIZw-_MX0LmWm5T7FDZ0ffn5_C0Tjs4hD3FKPZ3KEDeblK9oHMdSF3hDT2mNNyl-o2WkU1i-RBxSj-o9CxSlFKup0jTNoV-AlrwroAzmkiHCU_JoDBuIz-7mY_Lp7N3H9Qd2cfn-fH16wXrJ5cJkJ60erHVKtLbVrsXu7dA2rcK1UdqE2NkxNKYbQieNjYqHYTSNanotVSvkMXl18MUWv24jLH5K0GM_IceyBS-5bYXQrTKIvvwHvS7bmvF1O8rhp3G7o-SB6msBqHH0c01TqN-94H6fjj-k4zEdv0_HO1S9uPPedlMc7jV_4kBAHQDAo3wV69_L_-f7G86tmmY</recordid><startdate>20240801</startdate><enddate>20240801</enddate><creator>Xia, Yiqing</creator><creator>Flores Anato, Jorge Luis</creator><creator>Colijn, Caroline</creator><creator>Janjua, Naveed</creator><creator>Irvine, Mike</creator><creator>Williamson, Tyler</creator><creator>Varughese, Marie B.</creator><creator>Li, Michael</creator><creator>Osgood, Nathaniel</creator><creator>Earn, David J. 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D.</au><au>Sander, Beate</au><au>Cipriano, Lauren E.</au><au>Murty, Kumar</au><au>Xiu, Fanyu</au><au>Godin, Arnaud</au><au>Buckeridge, David</au><au>Hurford, Amy</au><au>Mishra, Sharmistha</au><au>Maheu-Giroux, Mathieu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Canada’s provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses</atitle><jtitle>Canadian journal of public health</jtitle><stitle>Can J Public Health</stitle><addtitle>Can J Public Health</addtitle><date>2024-08-01</date><risdate>2024</risdate><volume>115</volume><issue>4</issue><spage>541</spage><epage>557</epage><pages>541-557</pages><issn>0008-4263</issn><issn>1920-7476</issn><eissn>1920-7476</eissn><abstract>Setting
Mathematical 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.
Intervention
Provinces 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.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>39060710</pmid><doi>10.17269/s41997-024-00910-9</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0002-8363-4388</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0008-4263 |
| ispartof | Canadian journal of public health, 2024-08, Vol.115 (4), p.541-557 |
| issn | 0008-4263 1920-7476 1920-7476 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_3085116547 |
| source | MEDLINE; REPÈRE - Free; Springer Nature - Complete Springer Journals |
| subjects | Canada - epidemiology COVID-19 COVID-19 - epidemiology COVID-19 - prevention & control Data sources Disease transmission Dynamic structural analysis Epidemic models Epidemics Humans Importation Innovations in Policy and Practice Mathematical models Medicine Medicine & Public Health Models, Theoretical Pandemics Provinces Public Health Severe acute respiratory syndrome coronavirus 2 Surveillance Trajectory analysis Translation Uniqueness Viral diseases |
| title | Canada’s provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses |
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