Computational Epidemiology: Bayesian Disease Surveillance
Kaja Abbas, Armin R. Mikler, Amir Ramezani, Sheena Menezes University of North Texas
Dec 17, 2004 ICBA'04
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Presentation Outline z z z z z z z
Computational epidemiology Brief overview of past epidemics Mathematical epidemiological models Bayesian learning Bayesian disease outbreak model Results and analysis Conclusion and future work Dec 17, 2004 ICBA'04
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Computational Epidemiology
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Epidemic History z
14th century: Europe: 25 million: Plague
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1521: Aztecs: 3.5 million: Small pox
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1918: Worldwide: at-least 20 million: Influenza
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2003: SARS: rapid global spread
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Influenza Infection Timeline
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Mathematical Epidemiology Susceptibles Infectives Removals (SIR) model
SIR State Diagram Dec 17, 2004 ICBA'04
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SIR Epidemic Curve
Disease Prevalence Dec 17, 2004 ICBA'04
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Limitations of SIR model z z
Explicit representation of outbreak data Lack of demographic analysis
Bayesian Epidemic Model z z
Learning Implicit representation of population demographics
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Bayesian Learning z z
Probabilistic Reasoning Reasoning under uncertainty
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Disease Outbreak Bayesian Network
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Bayesian Network for Demographic Analysis
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Geographic Region I: Probability distributions
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Geographic Region I: Bayesian Network
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Geographic Region I: Analysis
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Geographic Region II: Probability distributions
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Geographic Region II: Bayesian Network
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Geographic Region II: Analysis
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Regional Comparison of Inferences Region I
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Region II
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Summary z
SIR Model
• Lack of demographic analysis z
Bayesian Epidemic Model
• Demographic analysis • Spatial portability of inferences
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Current Work
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Discussion, Questions & Comments Computational Epidemiology: Bayesian Disease Surveillance Kaja Abbas, Armin R. Mikler, Amir Ramezani, Sheena Menezes Computational Epidemiology Research Laboratory (cerl.unt.edu) Department of Computer Science and Engineering University of North Texas Email: {kaja, mikler}@cs.unt.edu, {ar0116, srm0034}@unt.edu
Dec 17, 2004 ICBA'04
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