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

Research output: Contribution to journalJournal articlepeer-review

  • Matthew Biggerstaff
  • Frederick Dahlgren
  • Julia Fitzner
  • Dylan George
  • Aspen Hammond
  • Ian Hall
  • David Haw
  • Natsuko Imai
  • Michael Johansson
  • Sarah Kramer
  • James McCaw
  • Robert Moss
  • Richard Pebody
  • Jonathan Read
  • Carrie Reed
  • Nicolas Reich
  • Steven Riley
  • Katelijn Vandemaele
  • Cecile Viboud
  • Joseph Wu
<mark>Journal publication date</mark>1/03/2020
<mark>Journal</mark>Influenza and Other Respiratory Viruses
Issue number2
Number of pages6
Pages (from-to)105-110
Publication StatusPublished
Early online date3/12/19
<mark>Original language</mark>English


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.