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Abstract Details
Activity Number:
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634
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Type:
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Invited
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Date/Time:
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Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract - #303542 |
Title:
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Graphical Methods for Making Empirically Supported Decisions in Emerging Epidemics
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Author(s):
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Justin Lessler*+
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Companies:
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Johns Hopkins Bloomberg School of Public Health
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Address:
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Department of Epidemiology, Baltimore, MD, 21205, USA
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Keywords:
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uncertainty ;
infectious disease ;
graphical displays
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Abstract:
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During the early stages of disease emergence policy makers are must make crucial decisions under significant uncertainty. In the early stages of an epidemic, only limited evidence may be available concerning the disease's virulence, transmissibility, incubation period and other critical parameters. However, the early stages of an emergent epidemic are when critical decisions are made about how to respond and people most want to know how the outbreak will unfold. While decision theory provides an approach for making decisions under uncertainty, these methods tend to be poorly understood by all but experts, and their results tend to be interpreted as rock solid predictions, washing away the uncertainty they were meant to capture. Graphical displays of uncertainty offer a method for making these techniques accessible, potentially providing intuitive displays of current uncertainty and decision rules. In this talk I will present techniques for generating "uncertainty landscapes" given limited empirical evidence using Bayesian and likelihood based techniques. I will show how decision rules can be embedded in these landscapes and used by policy makers to reason under uncertainty.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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