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Activity Number: 128 - Modeling for the Masses: Tackling Infectious Disease for the Public Good
Type: Topic Contributed
Date/Time: Monday, August 3, 2020 : 1:00 PM to 2:50 PM
Sponsor: Biometrics Section
Abstract #312491
Title: Adaptively Stacked Ensembles for Influenza Forecasting with Incomplete Data
Author(s): Thomas McAndrew* and Nicholas G Reich
Companies: University of Massachusetts and University of Massachusetts at Amherst
Keywords: Forecasting; Ensemble; Bayesian; Influenza; Variational Inference

Seasonal Influenza infects an average 30 million people in the US every year, overburdening hospitals during weeks of peak incidence. Named by the CDC as an important tool to fight the damaging effects of these epidemics, accurate forecasts of influenza like illness (ILI) forewarn public health officials about when, and where, seasonal influenza outbreaks will hit hardest. Ensemble forecasts have shown positive results in forecasting 1 to 4 week ahead ILI percentages and outperform any single model within the ensemble. Current ensembles are static within a season. They train on past ILI data before the season begins and generating optimal weights for each model that are kept constant throughout the season. We propose a novel adaptive ensemble forecast capable of changing model weights week by­ week throughout the flu season. This model performs similar to the more data-heavy static ensemble. In settings without substantial past data (i.e. emerging pandemics) or when new models do not have a long track ­record of performance, an adaptive ensemble approach will be the only option for performance­ based weighting of models and enhance the public health impact of ensemble forecasts.

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

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