This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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592
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Type:
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Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract - #306703 |
Title:
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Linear Approximations of Individual-Level Models for Infectious Disease
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Author(s):
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Grace Pui Sze Kwong*+ and Rob Deardon
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Companies:
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University of Guelph and University of Guelph
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Address:
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Population Medicine, OVC,, Guelph, ON, N1G 2W1, Canada
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Keywords:
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Infectious disease modelling ;
Model approximation ;
MCMC ;
Bayesian inference
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Abstract:
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Individual-level models (ILMs) for infectious diseases, fitted in a Bayesian MCMC framework, are an intuitive and flexible class of models that can take into account population heterogeneity via various individual-level covariates. ILMs containing a geometric distance kernel to account for geographic heterogeneity provide a natural way to model the spatial spread of many diseases. However, in even only moderately large populations, the likelihood calculations required can be prohibitively time consuming. It is possible to speed up the computation via a technique which makes use a linearized distance kernel. Here we examine some methods of carrying out this linearization and compare the performances of these methods.
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