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Activity Number: 515
Type: Invited
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
Sponsor: WNAR
Abstract #318224 View Presentation
Title: Approximate Bayesian Computation for Compartmental Epidemic Models: Methods and Software
Author(s): Grant Donald Brown* and Aaron T. Porter and Jacob J. Oleson
Companies: University of Iowa and Colorado School of Mines and University of Iowa
Keywords: Spatial Statistics ; Epidemic Modeling ; Approximate Bayesian Computation ; Statistical Computing ; Compartmental Models ; Sequential Monte Carlo

Epidemic modeling techniques allow investigators to better understand the spread of diseases by quantifying pathogen behaviors, and allowing users to weigh the evidence for particular modes of transmission. These models also provide the ability to forecast future spread, suggest new public health interventions, and evaluate existing ones. Nevertheless, implementation of epidemic models can be difficult due to their complex nature and the presence of poor or missing data. We propose a general class of spatial SEIRS compartmental models in a hierarchical Bayesian framework, along with software designed to perform such analyses efficiently using Approximate Bayesian Computation via Sequential Monte Carlo (ABC-SMC). We will begin by introducing ABC techniques, followed by a brief introduction to the ABSEIR R package. Particular attention will be paid to the evaluation of spatial and intervention related hypotheses, using the examples of epidemic and endemic cholera spread in Haiti and the Dominican Republic.

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

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