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Activity Number: 88
Type: Invited
Date/Time: Sunday, July 31, 2016 : 6:00 PM to 8:00 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #320941
Title: Forecasting Seasonal Epidemics with Ensemble Methods and Collective Human Judgment
Author(s): Logan Conrad Brooks* and Sangwon Hyun and Ryan Tibshirani
Companies: Carnegie Mellon University and Carnegie Mellon University and Carnegie Mellon University
Keywords: forecasting ; epidemiology ; ensemble methods ; collective human judgment ; empirical Bayes ; influenza

Semi-regular seasonal epidemics of respiratory and vector-born diseases such as influenza and dengue impose a heavy economic and mortality burden on society. Seasonal (non-pandemic) influenza, for example, is associated with ~250k--500k deaths worldwide each year (WHO estimate). Case counts follow a pattern most years: a sharp peak or interval of high activity with relatively low and flat surroundings. Accurate and reliable forecasts of disease prevalence should enable more effective countermeasures and preparation. However, variations in the timing and intensity of the epidemics, nonlinear dynamics, hidden states, and holiday effects impair the performance of popular time-series modeling tools such as SARIMA. We present some of our group's forecasting systems, which have performed well in several government initiatives. They include an ensemble of data-driven techniques incorporating Bayesian reasoning about timing and intensity; Epicast, a wisdom-of-crowds approach leveraging real-time human predictions; and Archefilter, a filtering approach which incorporates digital surveillance data such as Twitter.

Authors who are presenting talks have a * after their name.

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