Vanja Dukic
Department of Heatlh Studies
The University of Chicago
A Bayesian SEIR Approach to Modeling Smallpox Epidemic
Much of the recent public-policy debate regarding smallpox vaccination has
been focused on mass versus trace vaccination strategies; namely, whether
the public can be better protected by vaccination of the entire population
or of only those who have been in contact with infected individuals. Much of
the previous work on smallpox epidemics has generally employed relatively
complex deterministic models, with many biological parameters fixed, and
focusing mostly on a single point estimate of the disease reproductive rate
(the number of newly infected individuals arising from a single infected
individual). We present a Bayesian analysis of multiple past smallpox
epidemics, using a Markov chain Monte Carlo approach coupled with a simple
set of differential equations. This analysis provides an estimate of the
distribution of the disease reproductive rate rather than a single point
estimate, taking into account the uncertainty in all other parameters in the
model, and consequently
allowing a more informed decision making with regards to the public policy
of smallpox inoculation.
Joint work with B. Elderd and G. Dwyer, Dept. of Ecology and Evolution and
Center for Integrating Statistics and Environmental Sciences, University of
Chicago. Although this research is supported by
EPA STAR grant R-82940201-0, it does not necessarily reflect EPA views.
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