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Activity Number: 78 - Bayesian Generalized Linear Models for Medicine
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #329440
Title: Bayesian Hierarchical Models and Influenza Modeling
Author(s): Nehemias Ulloa* and Jarad Niemi
Companies: Iowa State University and Iowa State University
Keywords: Hierarchical Models; Non-linear; Influenza; Prediction

Influenza is a common illness which affects most people at some point in their lives. At best, a minor inconvenience, but as seen in the current winter season, influenza can lead to serious health problems including death especially among the young, the elderly and pregnant women. This season has highlighted the importance of being able to understand and predict the influenza season and hopefully, outbreaks. In this presentation, a Bayesian Hierarchical structure in conjunction with an Asymmetrical Gaussian mean is used to model the influenza season. This model is compared to a models using similar hierarchical structures with a different mean forms as well as a vague prior model. This presentation will also compare forecasting abilities of the different models.

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

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