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Coordinating the real-time use of global influenza activity data for better public health planning.

Identifieur interne : 000050 ( PubMed/Checkpoint ); précédent : 000049; suivant : 000051

Coordinating the real-time use of global influenza activity data for better public health planning.

Auteurs : Matthew Biggerstaff [États-Unis] ; Fredrick Scott Dahlgren [États-Unis] ; Julia Fitzner [Suisse] ; Dylan George [États-Unis] ; Aspen Hammond [Suisse] ; Ian Hall [Royaume-Uni] ; David Haw [Royaume-Uni] ; Natsuko Imai [Royaume-Uni] ; Michael A. Johansson [États-Unis] ; Sarah Kramer [États-Unis] ; James M. Mccaw [Australie] ; Robert Moss [Australie] ; Richard Pebody [Royaume-Uni] ; Jonathan M. Read [Royaume-Uni] ; Carrie Reed [États-Unis] ; Nicholas G. Reich [États-Unis] ; Steven Riley [Royaume-Uni] ; Katelijn Vandemaele [Suisse] ; Cecile Viboud [États-Unis] ; Joseph T. Wu [République populaire de Chine]

Source :

RBID : pubmed:32096594

Abstract

Health planners from global to local levels must anticipate year-to-year and week-to-week variation in seasonal influenza activity when planning for and responding to epidemics to mitigate their impact. To help with this, countries routinely collect incidence of mild and severe respiratory illness and virologic data on circulating subtypes and use these data for situational awareness, burden of disease estimates and severity assessments. Advanced analytics and modelling are increasingly used to aid planning and response activities by describing key features of influenza activity for a given location and generating forecasts that can be translated to useful actions such as enhanced risk communications, and informing clinical supply chains. Here, we describe the formation of the Influenza Incidence Analytics Group (IIAG), a coordinated global effort to apply advanced analytics and modelling to public influenza data, both epidemiological and virologic, in real-time and thus provide additional insights to countries who provide routine surveillance data to WHO. Our objectives are to systematically increase the value of data to health planners by applying advanced analytics and forecasting and for results to be immediately reproducible and deployable using an open repository of data and code. We expect the resources we develop and the associated community to provide an attractive option for the open analysis of key epidemiological data during seasonal epidemics and the early stages of an influenza pandemic.

DOI: 10.1111/irv.12705
PubMed: 32096594


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<name sortKey="Reed, Carrie" sort="Reed, Carrie" uniqKey="Reed C" first="Carrie" last="Reed">Carrie Reed</name>
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<name sortKey="Reich, Nicholas G" sort="Reich, Nicholas G" uniqKey="Reich N" first="Nicholas G" last="Reich">Nicholas G. Reich</name>
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<name sortKey="Riley, Steven" sort="Riley, Steven" uniqKey="Riley S" first="Steven" last="Riley">Steven Riley</name>
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<region type="country">Angleterre</region>
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<name sortKey="Vandemaele, Katelijn" sort="Vandemaele, Katelijn" uniqKey="Vandemaele K" first="Katelijn" last="Vandemaele">Katelijn Vandemaele</name>
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<nlm:affiliation>Global Influenza Programme, World Health Organization, Geneva, Switzerland.</nlm:affiliation>
<country xml:lang="fr">Suisse</country>
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<name sortKey="Viboud, Cecile" sort="Viboud, Cecile" uniqKey="Viboud C" first="Cecile" last="Viboud">Cecile Viboud</name>
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<name sortKey="Wu, Joseph T" sort="Wu, Joseph T" uniqKey="Wu J" first="Joseph T" last="Wu">Joseph T. Wu</name>
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<nlm:affiliation>WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong SAR, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong SAR</wicri:regionArea>
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<title level="j">Influenza and other respiratory viruses</title>
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<div type="abstract" xml:lang="en">Health planners from global to local levels must anticipate year-to-year and week-to-week variation in seasonal influenza activity when planning for and responding to epidemics to mitigate their impact. To help with this, countries routinely collect incidence of mild and severe respiratory illness and virologic data on circulating subtypes and use these data for situational awareness, burden of disease estimates and severity assessments. Advanced analytics and modelling are increasingly used to aid planning and response activities by describing key features of influenza activity for a given location and generating forecasts that can be translated to useful actions such as enhanced risk communications, and informing clinical supply chains. Here, we describe the formation of the Influenza Incidence Analytics Group (IIAG), a coordinated global effort to apply advanced analytics and modelling to public influenza data, both epidemiological and virologic, in real-time and thus provide additional insights to countries who provide routine surveillance data to WHO. Our objectives are to systematically increase the value of data to health planners by applying advanced analytics and forecasting and for results to be immediately reproducible and deployable using an open repository of data and code. We expect the resources we develop and the associated community to provide an attractive option for the open analysis of key epidemiological data during seasonal epidemics and the early stages of an influenza pandemic.</div>
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<Month>03</Month>
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<Title>Influenza and other respiratory viruses</Title>
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<ArticleTitle>Coordinating the real-time use of global influenza activity data for better public health planning.</ArticleTitle>
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<AbstractText>Health planners from global to local levels must anticipate year-to-year and week-to-week variation in seasonal influenza activity when planning for and responding to epidemics to mitigate their impact. To help with this, countries routinely collect incidence of mild and severe respiratory illness and virologic data on circulating subtypes and use these data for situational awareness, burden of disease estimates and severity assessments. Advanced analytics and modelling are increasingly used to aid planning and response activities by describing key features of influenza activity for a given location and generating forecasts that can be translated to useful actions such as enhanced risk communications, and informing clinical supply chains. Here, we describe the formation of the Influenza Incidence Analytics Group (IIAG), a coordinated global effort to apply advanced analytics and modelling to public influenza data, both epidemiological and virologic, in real-time and thus provide additional insights to countries who provide routine surveillance data to WHO. Our objectives are to systematically increase the value of data to health planners by applying advanced analytics and forecasting and for results to be immediately reproducible and deployable using an open repository of data and code. We expect the resources we develop and the associated community to provide an attractive option for the open analysis of key epidemiological data during seasonal epidemics and the early stages of an influenza pandemic.</AbstractText>
<CopyrightInformation>© 2019 The Authors. Influenza and Other Respiratory Viruses Published by John Wiley & Sons Ltd.</CopyrightInformation>
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<LastName>Biggerstaff</LastName>
<ForeName>Matthew</ForeName>
<Initials>M</Initials>
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<Affiliation>Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA.</Affiliation>
</AffiliationInfo>
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<LastName>Dahlgren</LastName>
<ForeName>Fredrick Scott</ForeName>
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<Affiliation>Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA.</Affiliation>
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<LastName>Fitzner</LastName>
<ForeName>Julia</ForeName>
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<Affiliation>Global Influenza Programme, World Health Organization, Geneva, Switzerland.</Affiliation>
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<Affiliation>Department of Mathematics and School of Health Sciences, University of Manchester, Manchester, UK.</Affiliation>
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<ForeName>David</ForeName>
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<Affiliation>Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, PR, USA.</Affiliation>
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<LastName>Kramer</LastName>
<ForeName>Sarah</ForeName>
<Initials>S</Initials>
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<LastName>McCaw</LastName>
<ForeName>James M</ForeName>
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<Affiliation>Modelling and Simulation Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Vic., Australia.</Affiliation>
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<AffiliationInfo>
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<LastName>Moss</LastName>
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<Identifier Source="ORCID">0000-0002-4568-2012</Identifier>
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<Affiliation>Modelling and Simulation Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Vic., Australia.</Affiliation>
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<LastName>Pebody</LastName>
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<Author ValidYN="Y">
<LastName>Read</LastName>
<ForeName>Jonathan M</ForeName>
<Initials>JM</Initials>
<Identifier Source="ORCID">0000-0002-9697-0962</Identifier>
<AffiliationInfo>
<Affiliation>Centre for Health Informatics, Computing, and Statistics, Lancaster Medical School, Faculty of Health and Medicine, Lancaster University, Lancashire, UK.</Affiliation>
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<LastName>Reed</LastName>
<ForeName>Carrie</ForeName>
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<Affiliation>Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA.</Affiliation>
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<Affiliation>Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, USA.</Affiliation>
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<Affiliation>MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK.</Affiliation>
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<Author ValidYN="Y">
<LastName>Vandemaele</LastName>
<ForeName>Katelijn</ForeName>
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<Author ValidYN="Y">
<LastName>Viboud</LastName>
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<LastName>Wu</LastName>
<ForeName>Joseph T</ForeName>
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<AffiliationInfo>
<Affiliation>WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong SAR, China.</Affiliation>
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<name sortKey="Viboud, Cecile" sort="Viboud, Cecile" uniqKey="Viboud C" first="Cecile" last="Viboud">Cecile Viboud</name>
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<name sortKey="Fitzner, Julia" sort="Fitzner, Julia" uniqKey="Fitzner J" first="Julia" last="Fitzner">Julia Fitzner</name>
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<name sortKey="Pebody, Richard" sort="Pebody, Richard" uniqKey="Pebody R" first="Richard" last="Pebody">Richard Pebody</name>
<name sortKey="Read, Jonathan M" sort="Read, Jonathan M" uniqKey="Read J" first="Jonathan M" last="Read">Jonathan M. Read</name>
<name sortKey="Riley, Steven" sort="Riley, Steven" uniqKey="Riley S" first="Steven" last="Riley">Steven Riley</name>
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