tetano
Editor, Senior Moderator
Published online before print November 26, 2012, doi: 10.1073/pnas.1208772109 PNAS November 26, 2012
Forecasting seasonal outbreaks of influenza
Jeffrey Shamana,1 and
Alicia Karspeckb
+ Author Affiliations
aDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032; and
bClimate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, CO 80305
Edited* by Aaron A. King, University of Michigan, Ann Arbor, MI 48109, and approved October 25, 2012 (received for review May 23, 2012)
Abstract
Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of real-time, web-based estimates of local influenza infection rates makes this type of quantitative forecasting possible. Retrospective ensemble forecasts are generated on a weekly basis following assimilation of these web-based estimates for the 2003?2008 influenza seasons in New York City. The findings indicate that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak. In addition, confidence in those predictions can be inferred from the spread of the forecast ensemble. This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza.
http://www.pnas.org/content/early/2012/11/21/1208772109.abstract
Forecasting seasonal outbreaks of influenza
Jeffrey Shamana,1 and
Alicia Karspeckb
+ Author Affiliations
aDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032; and
bClimate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, CO 80305
Edited* by Aaron A. King, University of Michigan, Ann Arbor, MI 48109, and approved October 25, 2012 (received for review May 23, 2012)
Abstract
Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of real-time, web-based estimates of local influenza infection rates makes this type of quantitative forecasting possible. Retrospective ensemble forecasts are generated on a weekly basis following assimilation of these web-based estimates for the 2003?2008 influenza seasons in New York City. The findings indicate that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak. In addition, confidence in those predictions can be inferred from the spread of the forecast ensemble. This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza.
http://www.pnas.org/content/early/2012/11/21/1208772109.abstract
