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Activity Number:
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47
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 8, 2004 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #300740 |
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Title:
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Bayesian Inferences of Disease Epidemics: Smallpox Outbreaks and Public Policy
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Author(s):
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Bret D. Elderd*+ and Vanja Dukic and Greg Dwyer
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Companies:
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University of Chicago and University of Chicago and University of Chicago
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Address:
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Center for Integrating Statistical and Environmental Science, Chicago, IL, 60637,
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Keywords:
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disease epidemics ; MCMC ; smallpox ; SEIR model
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Abstract:
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Public-policy debate about the release of smallpox into the general population has centered on whether the protection of the public can be better achieved by mass vaccination or trace vaccination. Mass vaccination inoculates the entire population, whereas trace vaccination inoculates those who have been in contact with infected individuals. Previous studies have used relatively complex deterministic models. These models forecast disease epidemics and subsequently public policy based on a single point estimate of the disease reproductive rate (i.e., the number of newly infected individuals arising from a single infected individual). We present a Bayesian analysis of past smallpox epidemics where the number of individuals who have died in each epidemic is known. For this analysis, we used a Markov chain Monte Carlo simulation coupled with a simple set of differential equations. This analysis provided an estimate of the distribution of the disease reproductive rate rather than a single point estimate. By understanding the variation surrounding the disease's spread, a more informed set of decisions can be reached with regards to the public policy of smallpox inoculation.
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