Tau-leaped Particle Learning

Description: 

Development of effective policy interventions to stem disease outbreaks requires knowledge of the current state of affairs, e.g. how many individuals are currently infected, a strain’s virulence, etc, as well as our uncertainty of these values. A Bayesian inferential approach provides this information, but at a computational expense. We develop a sequential Bayesian approach based on an epidemiological compartment model and noisy count observations of the transitions between compartments.

Objective:

Develop fast sequential Bayesian inference for disease outbreak counts.
 

Primary Topic Areas: 
Original Publication Year: 
2012
Event/Publication Date: 
December, 2012

July 10, 2018

Contact Us

NSSP Community of Practice

Email: syndromic@cste.org

 

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