Modelling the COVID-19 pandemic in context: an international participatory approach
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1136/bmjgh-2020-003126 http://hdl.handle.net/11449/209824 |
Resumo: | The SARS-CoV-2 pandemic has had an unprecedented impact on multiple levels of society. Not only has the pandemic completely overwhelmed some health systems but it has also changed how scientific evidence is shared and increased the pace at which such evidence is published and consumed, by scientists, policymakers and the wider public. More significantly, the pandemic has created tremendous challenges for decision-makers, who have had to implement highly disruptive containment measures with very little empirical scientific evidence to support their decision-making process. Given this lack of data, predictive mathematical models have played an increasingly prominent role. In high-income countries, there is a long-standing history of established research groups advising policymakers, whereas a general lack of translational capacity has meant that mathematical models frequently remain inaccessible to policymakers in low-income and middle-income countries. Here, we describe a participatory approach to modelling that aims to circumvent this gap. Our approach involved the creation of an international group of infectious disease modellers and other public health experts, which culminated in the establishment of the COVID-19 Modelling (CoMo) Consortium. Here, we describe how the consortium was formed, the way it functions, the mathematical model used and, crucially, the high degree of engagement fostered between CoMo Consortium members and their respective local policymakers and ministries of health. |
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Modelling the COVID-19 pandemic in context: an international participatory approachhealth policyrespiratory infectionscontrol strategiesSARSThe SARS-CoV-2 pandemic has had an unprecedented impact on multiple levels of society. Not only has the pandemic completely overwhelmed some health systems but it has also changed how scientific evidence is shared and increased the pace at which such evidence is published and consumed, by scientists, policymakers and the wider public. More significantly, the pandemic has created tremendous challenges for decision-makers, who have had to implement highly disruptive containment measures with very little empirical scientific evidence to support their decision-making process. Given this lack of data, predictive mathematical models have played an increasingly prominent role. In high-income countries, there is a long-standing history of established research groups advising policymakers, whereas a general lack of translational capacity has meant that mathematical models frequently remain inaccessible to policymakers in low-income and middle-income countries. Here, we describe a participatory approach to modelling that aims to circumvent this gap. Our approach involved the creation of an international group of infectious disease modellers and other public health experts, which culminated in the establishment of the COVID-19 Modelling (CoMo) Consortium. Here, we describe how the consortium was formed, the way it functions, the mathematical model used and, crucially, the high degree of engagement fostered between CoMo Consortium members and their respective local policymakers and ministries of health.Bill and Melinda Gates FoundationLi Ka Shing FoundationFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Oxford University COVID-19 Research Response FundUniv Oxford, Nuffield Dept Med, Ctr Trop Med & Global Hlth, Oxford, EnglandMAEMOD, Mahidol Oxford Trop Med Res Unit, Bangkok, ThailandUniv Oxford, Ctr Trop Med & Global Hlth, Ctr Trop Med, Oxford, EnglandCornell Inst Dis & Disaster Preparedness, Weill Cornell Med, New York, NY USAUniv Oxford, Nuffield Dept Med, Oxford, EnglandFlorida Int Univ, Dept Epidemiol, Miami, FL 33199 USAUniv Calif San Francisco, Sch Med, San Francisco, CA USAKerman Univ Med Sci, Inst Futures Studies Hlth, WHO Collaborating Ctr HIV Surveillance, Kerman, IranWHO, Reg Off Eastern Mediterranean, Kabul, AfghanistanSao Paulo State Univ UNESP, Inst Theoret Phys, Waves & Nonlinear Patterns Res Grp, Sao Paulo, SP, BrazilFed Univ ABC, Ctr Math Computat & Cognit, Ctr Math Comp & Cognit, Santo Andre, SP, BrazilSao Paulo State Univ UNESP, Inst Theoret Phys, Waves & Nonlinear Patterns Res Grp, Sao Paulo, SP, BrazilBill and Melinda Gates Foundation: OPP1193472FAPESP: 2017/26770-8Oxford University COVID-19 Research Response Fund: 0009280Bmj Publishing GroupUniv OxfordMAEMODCornell Inst Dis & Disaster PreparednessFlorida Int UnivUniv Calif San FranciscoKerman Univ Med SciWHOUniversidade Estadual Paulista (Unesp)Universidade Federal do ABC (UFABC)Aguas, RicardoWhite, LisaHupert, NathanielShretta, RimaPan-Ngum, WirichadaCelhay, OlivierMoldokmatova, AinuraArifi, FatimaMirzazadeh, AliSharifi, HamidAdib, KeyrellousSahak, Mohammad NadirFranco, Caroline [UNESP]Coutinho, Renato2021-06-25T12:30:25Z2021-06-25T12:30:25Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article9http://dx.doi.org/10.1136/bmjgh-2020-003126Bmj Global Health. London: Bmj Publishing Group, v. 5, n. 12, 9 p., 2020.2059-7908http://hdl.handle.net/11449/20982410.1136/bmjgh-2020-003126WOS:000602736100001Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengBmj Global Healthinfo:eu-repo/semantics/openAccess2021-10-23T19:50:02Zoai:repositorio.unesp.br:11449/209824Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:36:41.960120Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Modelling the COVID-19 pandemic in context: an international participatory approach |
title |
Modelling the COVID-19 pandemic in context: an international participatory approach |
spellingShingle |
Modelling the COVID-19 pandemic in context: an international participatory approach Aguas, Ricardo health policy respiratory infections control strategies SARS |
title_short |
Modelling the COVID-19 pandemic in context: an international participatory approach |
title_full |
Modelling the COVID-19 pandemic in context: an international participatory approach |
title_fullStr |
Modelling the COVID-19 pandemic in context: an international participatory approach |
title_full_unstemmed |
Modelling the COVID-19 pandemic in context: an international participatory approach |
title_sort |
Modelling the COVID-19 pandemic in context: an international participatory approach |
author |
Aguas, Ricardo |
author_facet |
Aguas, Ricardo White, Lisa Hupert, Nathaniel Shretta, Rima Pan-Ngum, Wirichada Celhay, Olivier Moldokmatova, Ainura Arifi, Fatima Mirzazadeh, Ali Sharifi, Hamid Adib, Keyrellous Sahak, Mohammad Nadir Franco, Caroline [UNESP] Coutinho, Renato |
author_role |
author |
author2 |
White, Lisa Hupert, Nathaniel Shretta, Rima Pan-Ngum, Wirichada Celhay, Olivier Moldokmatova, Ainura Arifi, Fatima Mirzazadeh, Ali Sharifi, Hamid Adib, Keyrellous Sahak, Mohammad Nadir Franco, Caroline [UNESP] Coutinho, Renato |
author2_role |
author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Univ Oxford MAEMOD Cornell Inst Dis & Disaster Preparedness Florida Int Univ Univ Calif San Francisco Kerman Univ Med Sci WHO Universidade Estadual Paulista (Unesp) Universidade Federal do ABC (UFABC) |
dc.contributor.author.fl_str_mv |
Aguas, Ricardo White, Lisa Hupert, Nathaniel Shretta, Rima Pan-Ngum, Wirichada Celhay, Olivier Moldokmatova, Ainura Arifi, Fatima Mirzazadeh, Ali Sharifi, Hamid Adib, Keyrellous Sahak, Mohammad Nadir Franco, Caroline [UNESP] Coutinho, Renato |
dc.subject.por.fl_str_mv |
health policy respiratory infections control strategies SARS |
topic |
health policy respiratory infections control strategies SARS |
description |
The SARS-CoV-2 pandemic has had an unprecedented impact on multiple levels of society. Not only has the pandemic completely overwhelmed some health systems but it has also changed how scientific evidence is shared and increased the pace at which such evidence is published and consumed, by scientists, policymakers and the wider public. More significantly, the pandemic has created tremendous challenges for decision-makers, who have had to implement highly disruptive containment measures with very little empirical scientific evidence to support their decision-making process. Given this lack of data, predictive mathematical models have played an increasingly prominent role. In high-income countries, there is a long-standing history of established research groups advising policymakers, whereas a general lack of translational capacity has meant that mathematical models frequently remain inaccessible to policymakers in low-income and middle-income countries. Here, we describe a participatory approach to modelling that aims to circumvent this gap. Our approach involved the creation of an international group of infectious disease modellers and other public health experts, which culminated in the establishment of the COVID-19 Modelling (CoMo) Consortium. Here, we describe how the consortium was formed, the way it functions, the mathematical model used and, crucially, the high degree of engagement fostered between CoMo Consortium members and their respective local policymakers and ministries of health. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01-01 2021-06-25T12:30:25Z 2021-06-25T12:30:25Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1136/bmjgh-2020-003126 Bmj Global Health. London: Bmj Publishing Group, v. 5, n. 12, 9 p., 2020. 2059-7908 http://hdl.handle.net/11449/209824 10.1136/bmjgh-2020-003126 WOS:000602736100001 |
url |
http://dx.doi.org/10.1136/bmjgh-2020-003126 http://hdl.handle.net/11449/209824 |
identifier_str_mv |
Bmj Global Health. London: Bmj Publishing Group, v. 5, n. 12, 9 p., 2020. 2059-7908 10.1136/bmjgh-2020-003126 WOS:000602736100001 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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Bmj Global Health |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
9 |
dc.publisher.none.fl_str_mv |
Bmj Publishing Group |
publisher.none.fl_str_mv |
Bmj Publishing Group |
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Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
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UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
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Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
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1808128834236579840 |