Bayesian Biosurveillance

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Bayesian Biosurveillance

• Gregory F. Cooper
• Center for Biomedical Informatics
• University of Pittsburgh
• gfc_at_cbmi.pitt.edu
• The research described in this talk is based on
  collaborative work with members of the Bayesian
  Biosurveillance project and the RODS Laboratory
  at the University of Pittsburgh, and the Auton
  Laboratory at Carnegie Mellon University. Special
  thanks Bill Hogan for the BARD slides that are
  included in this presentation.

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Outline

• Provide a brief overview of Bayesian inference as
  applied to outbreak detection
• Show an example of a Bayesian biosurveillance
  algorithm

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Biosurveillance

• Definition Biosurveillance is the process of
  monitoring for new outbreaks of infectious
  disease
• Goal Detect an infectious disease outbreak in a
  population rapidly and accurately

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Bayes Rule
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Bayes Rule
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Bayes Rule
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Bayes Rule
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Bayes Rule
parameter prior
hypothesis prior
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Bayes Rule forOutbreak Detection
One hypothesis is that there is no disease
outbreak at the present time. Other hypothesis
postulates various types of outbreaks, such as
anthrax, small pox, plague, and many others.
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Some Advantages of a Bayesian Approach
to Biosurveillance

• Permits specification of prior knowledge and
  belief
• Knowledge about outbreak diseases
• Belief about whether, when and how an outbreak
  will occur, based on experience, intel, and
  intelligent guesses.
• Facilitates modeling
• of complex outbreaks
• with multiple data streams
• Yields inferences
• of P(outbreak data), which can be used directly
  in a decision analysis about what to do
• of other statistics of interest, such as the
  expected number of people infected in a probable
  outbreak situation

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An Example of a Bayesian Biosurveillance Algorithm

• BARD (Bayesian Aerosol Release Detector) is an
  outbreak detection system that is designed to
  compute the posterior probability of an outdoor,
  windborne release of anthrax spores
• Outbreak data
• Emergency Dept (ED) chief complaints
• OTC
• BioWatch sensors
• Additional data
• Weather data
• Dispersion data

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BARD Overview

• Seeks earlier, more sensitive detection of
  windborne outbreaks through recognition of a
  characteristic dispersion pattern
• An alert not only detects outbreak, but
  characterizes it as windborne
• Derives estimates of release location, quantity
  and timing
• Has been running in Pittsburgh (since 1/2005) and
  Philadelphia (since 6/2005)

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Typical Computation for Aerosol Releases Predict
Consequences of Release Parameters
Weather
Quantity Released
Dispersion Model
Location of Release
Downwind airborne concentrations
Model of Effects of Aerosol on People
Time of Release
Predicted effect on biosurveillance data over time
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BARD Uses Bayesian Inference to Derive Release
Parameters from Data
Weather
Quantity released
Inversion of Dispersion Model
Location of release
Downwind airborne concentrations
Inversion of Model of Aerosol Effects on
Biosurveillance Data
Time of Release
Observed effect on biosurveillance data over time
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BARD Searches for the Optimal Release Parameters
Wind direction 2 days ago
Wind direction 3 days ago
P(Data Release Params) is very low
P(Data Release Params) is relatively high
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The Structure of the BARD Model
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The Structure of the BARD Model
Dispersion Model
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The Gaussian Plume Model
where d is the number of spores inhaled by an
individual Q is the number of kilograms of spores
released w is the number of spores per
kilogram VE is minute ventilation (x, y, h) is
the coordinate of the hypothesized release
location where x and y specify the location
on the surface of the earth and h specifies
height above ground (xi yi, hi) is similarly the
coordinate of the patients location ?x and ?Z
are the distributions of spores in the crosswind
direction u is the wind speed s is the
atmospheric stability
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The Structure of the BARD Model
Model of effects on an person
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BARD Evaluation Methods

• Used BARD to generate data for 20 simulated
  windborne anthrax releases (Thus, this is a
  preliminary evaluation.)
• Injected that ED respiratory chief complaint data
  into a real historical dataset
• Used historical weather data for simulation and
  detection

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BARD Evaluation Results

• Sensitivity 100 at false alarm rate of zero
  (for detection within seven days of the simulated
  release)
• Mean timeliness at false alarm rate of zero
• From time of release, 3.1 days
• From time of first ED visit, 1.2 days (28 hours)
• Mean accuracy of release parameters output by
  BARD
• X coordinate of release location 3,400
  meters
• Y coordinate of release location 84
  meters
• Height of release
  124 meters
• Quantity of release
  0.5 kilograms
• Time of release
  0.008 days

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Accuracy of Identified Release Parameters
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BARD Search Time

• 3 minutes to consider 200,000 release
  scenarios in searching for an outbreak in the
  Pittsburgh metropolitan area

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Summary

• Bayesian biosurveillance
• has a number of attractive qualities
• has been implemented in several algorithms
• is practical
• has many unexplored, promising directions for
  future work

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Acknowledgments

• This research was supported by the National
  Science Foundation, the
  Pennsylvania Department of Health, the Department
  of Homeland Security, DARPA, and the Centers for
  Disease Control and Prevention.

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Additional Information

• Bayesian Biosurveillance Project
  www.cbmi.pitt.edu/panda
• Real-Time Outbreak and Disease Surveillance
  (RODS) Laboratory rods.health.pitt.edu
• Greg Cooper gfc_at_cbmi.pitt.edu

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