A system for generating short-term (5-day to 3 month) ensemble-based predictions of epidemic influenza will be developed. To make skillful forecasts of influenza infection outcomes, this system will apply state-of the-art data assimilation techniques, similar to those used in numerical weather prediction, to incorporate real-time estimates of influenza infection into mathematical models of influenza transmission dynamics. The proposed work will establish a portable, locally relevant operational disease forecast system that is novel in its quantitative, statistically rigorous approach. This system is possible due to the recent advent of real-time, web-based estimates of influenza infection rates and the existence of observationally validated models of influenza transmission dynamics.
The aim of this project is the design of an ensemble-based model/data assimilation system that brings these informational resources together to create skillful, probabilistic forecasts of influenza outcomes. The forecast system will be developed using an assimilation technique called the ensemble adjustment Kalman filter. Questions to be answered include: How useful are web-based influenza estimates for initializing and constraining mathematical models of influenza? What levels of predictability can such model/data systems deliver at weekly and monthly lead-times? What are the uncertainty bounds on the timing and level of influenza in a population at the height of an outbreak, and how early in the season can these metrics be evaluated? Answers to these questions will determine the levels of predictability the model/data assimilation system can deliver at various time scales. Work in other discipline fields has demonstrated that model/data assimilation systems developed using the ensemble adjustment Kalman filter are practicable, optimize model behavior to better match observations, and provide a rigorous framework for quantifying predictability. It is hypothesized that skillful prediction of local influenza risk will be realized over a range of lead times. The limits of predictability will be explicitly detemined, and forecasts of local influenza risk will be made.

Public Health Relevance

Influenza kills an estimated 35,000 people each year in the United States alone and presents an enormous burden on worldwide public health. Local, short-term predictions based on real-time data and environmental forcing would provide public health officials the opportunity to develop locally appropriate, timely intervention strategies, the ability to gauge the severity of the developing epidemic, and the time to garner additional medical and public health resources

National Institute of Health (NIH)
Research Project (R01)
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Special Emphasis Panel (ZGM1)
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Ravichandran, Veerasamy
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Columbia University (N.Y.)
Public Health & Prev Medicine
Schools of Public Health
New York
United States
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