title: Real-time influenza forecasts during the 2012-2013 season.
abstract: Recently, we developed a seasonal influenza prediction system that uses an advanced data assimilation technique and real-time estimates of influenza incidence to optimize and initialize a population-based mathematical model of influenza transmission dynamics. This system was used to generate and evaluate retrospective forecasts of influenza peak timing in New York City. Here we present weekly forecasts of seasonal influenza developed and run in real time for 108 cities in the USA during the recent 2012-2013 season. Reliable ensemble forecasts of influenza outbreak peak timing with leads of up to 9 weeks were produced. Forecast accuracy increased as the season progressed, and the forecasts significantly outperformed alternate, analogue prediction methods. By week 52, prior to peak for the majority of cities, 63% of all ensemble forecasts were accurate. To our knowledge, this is the first time predictions of seasonal influenza have been made in real time and with demonstrated accuracy.
authors.affiliation: Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, USA.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Shaman
authors.forename: Alicia
authors.initials: A
authors.lastname: Karspeck
authors.forename: Wan
authors.initials: W
authors.lastname: Yang
authors.forename: James
authors.initials: J
authors.lastname: Tamerius
authors.forename: Marc
authors.initials: M
authors.lastname: Lipsitch
mesh_terms: D003198:Computer Simulation; D004196:Disease Outbreaks; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology* / Q000635:transmission / Q000821:virology; D008954:Models, Biological; D015233:Models, Statistical; D012189:Retrospective Studies; D012621:Seasons; D013997:Time Factors; D014481:United States / Q000453:epidemiology