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Modelling the COVID-19 pandemic in context: an international participatory approach

dc.contributor.authorAguas, Ricardo
dc.contributor.authorWhite, Lisa
dc.contributor.authorHupert, Nathaniel
dc.contributor.authorShretta, Rima
dc.contributor.authorPan-Ngum, Wirichada
dc.contributor.authorCelhay, Olivier
dc.contributor.authorMoldokmatova, Ainura
dc.contributor.authorArifi, Fatima
dc.contributor.authorMirzazadeh, Ali
dc.contributor.authorSharifi, Hamid
dc.contributor.authorAdib, Keyrellous
dc.contributor.authorSahak, Mohammad Nadir
dc.contributor.authorFranco, Caroline [UNESP]
dc.contributor.authorCoutinho, Renato
dc.contributor.institutionUniv Oxford
dc.contributor.institutionMAEMOD
dc.contributor.institutionCornell Inst Dis & Disaster Preparedness
dc.contributor.institutionFlorida Int Univ
dc.contributor.institutionUniv Calif San Francisco
dc.contributor.institutionKerman Univ Med Sci
dc.contributor.institutionWHO
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Federal do ABC (UFABC)
dc.date.accessioned2021-06-25T12:30:25Z
dc.date.available2021-06-25T12:30:25Z
dc.date.issued2020-01-01
dc.description.abstractThe 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.en
dc.description.affiliationUniv Oxford, Nuffield Dept Med, Ctr Trop Med & Global Hlth, Oxford, England
dc.description.affiliationMAEMOD, Mahidol Oxford Trop Med Res Unit, Bangkok, Thailand
dc.description.affiliationUniv Oxford, Ctr Trop Med & Global Hlth, Ctr Trop Med, Oxford, England
dc.description.affiliationCornell Inst Dis & Disaster Preparedness, Weill Cornell Med, New York, NY USA
dc.description.affiliationUniv Oxford, Nuffield Dept Med, Oxford, England
dc.description.affiliationFlorida Int Univ, Dept Epidemiol, Miami, FL 33199 USA
dc.description.affiliationUniv Calif San Francisco, Sch Med, San Francisco, CA USA
dc.description.affiliationKerman Univ Med Sci, Inst Futures Studies Hlth, WHO Collaborating Ctr HIV Surveillance, Kerman, Iran
dc.description.affiliationWHO, Reg Off Eastern Mediterranean, Kabul, Afghanistan
dc.description.affiliationSao Paulo State Univ UNESP, Inst Theoret Phys, Waves & Nonlinear Patterns Res Grp, Sao Paulo, SP, Brazil
dc.description.affiliationFed Univ ABC, Ctr Math Computat & Cognit, Ctr Math Comp & Cognit, Santo Andre, SP, Brazil
dc.description.affiliationUnespSao Paulo State Univ UNESP, Inst Theoret Phys, Waves & Nonlinear Patterns Res Grp, Sao Paulo, SP, Brazil
dc.description.sponsorshipBill and Melinda Gates Foundation
dc.description.sponsorshipLi Ka Shing Foundation
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipOxford University COVID-19 Research Response Fund
dc.description.sponsorshipIdBill and Melinda Gates Foundation: OPP1193472
dc.description.sponsorshipIdFAPESP: 2017/26770-8
dc.description.sponsorshipIdOxford University COVID-19 Research Response Fund: 0009280
dc.format.extent9
dc.identifierhttp://dx.doi.org/10.1136/bmjgh-2020-003126
dc.identifier.citationBmj Global Health. London: Bmj Publishing Group, v. 5, n. 12, 9 p., 2020.
dc.identifier.doi10.1136/bmjgh-2020-003126
dc.identifier.issn2059-7908
dc.identifier.urihttp://hdl.handle.net/11449/209824
dc.identifier.wosWOS:000602736100001
dc.language.isoeng
dc.publisherBmj Publishing Group
dc.relation.ispartofBmj Global Health
dc.sourceWeb of Science
dc.subjecthealth policy
dc.subjectrespiratory infections
dc.subjectcontrol strategies
dc.subjectSARS
dc.titleModelling the COVID-19 pandemic in context: an international participatory approachen
dc.typeArtigo
dcterms.rightsHolderBmj Publishing Group
dspace.entity.typePublication
unesp.author.orcid0000-0002-3006-3737[7]
unesp.campusUniversidade Estadual Paulista (UNESP), Instituto de Física Teórica (IFT), São Paulopt

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