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BIOLOGICAL SCIENCES / POPULATION BIOLOGY
Uncertainty in predictions of disease spread and public health responses to bioterrorism and emerging diseases
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*Center for Integrating Statistical and Environmental Science, University of Chicago, 5734 South Ellis Avenue, Chicago, IL 60637; Department of Health Studies, University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637; and Department of Ecology and Evolution, University of Chicago, 1101 East 57th Street, Chicago, IL 60637
Edited by James O. Berger, Duke University, Durham, NC, and approved August 25, 2006 (received for review January 31, 2006)
Concerns over bioterrorism and emerging diseases have led to the widespread use of epidemic models for evaluating public health strategies. Partly because epidemic models often capture the dynamics of prior epidemics remarkably well, little attention has been paid to how uncertainty in parameter estimates might affect model predictions. To understand such effects, we used Bayesian statistics to rigorously estimate the uncertainty in the parameters of an epidemic model, focusing on smallpox bioterrorism. We then used a vaccination model to translate the uncertainty in the model parameters into uncertainty in which of two vaccination strategies would provide a better response to bioterrorism, mass vaccination, or vaccination of social contacts, so-called "trace vaccination." Our results show that the uncertainty in the model parameters is remarkably high and that this uncertainty has important implications for vaccination strategies. For example, under one plausible scenario, the most likely outcome is that mass vaccination would save 
100,000 more lives than trace vaccination. Because of the high uncertainty in the parameters, however, there is also a substantial probability that mass vaccination would save 200,000 or more lives than trace vaccination. In addition to providing the best response to the most likely outcome, mass vaccination thus has the advantage of preventing outcomes that are only slightly less likely but that are substantially more horrific. Rigorous estimates of uncertainty thus can reveal hidden advantages of public health strategies, suggesting that formal uncertainty estimation should play a key role in planning for epidemics.
susceptible–exposed–infected–removed model | trace versus mass vaccination | host–pathogen interaction | Bayesian hieracrchical model
Author contributions: B.D.E. and G.D. designed research; B.D.E. and V.M.D. performed research; B.D.E. and V.M.D. analyzed data; and B.D.E., V.M.D., and G.D. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS direct submission.
To whom correspondence should be addressed. E-mail: gdwyer{at}uchicago.edu
© 2006 by The National Academy of Sciences of the USA
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