Bayesian Probabilistic Reasoning Applied to Mathematical Epidemiology for Predictive Spatiotemporal Analysis of Infectious Diseases Page: X
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LIST OF FIGURES
1.1 Public Health - Multi-disciplinary Domain 5
1.2 Population Dynamics, Genetics and Environment Correlate in the
Study of Disease Analysis 6
2.1 Reasoning Methodologies in Bayesian Networks 12
2.2 Bayesian Network Example for Four Random Variables {a, b, c, d} 13
2.3 Network Complexity 15
2.4 Markov Processes 16
2.5 Generic Dynamic Bayesian Network 17
2.6 HMMs Correlate the Hidden States and the Observed States 18
4.1 Epidemic Curves for Deterministic and Stochastic Models 30
4.2 SIR Epidemic Curve for a Sample Population 32
4.3 SIR/SIRS State Diagram 33
4.4 Cellular Automata Update from time step t-1 to t 35
4.5 Infection Time-line 35
5.1 Bayesian Network Illustrating the Relationships between Demography,
Symptoms and Diseases 41
5.2 Disease Outbreak Simulator 42
5.3 Dynamic Bayesian Network Analysis of Disease Progression 43
6.1 Infection Time-line for Influenza 46
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Abbas, Kaja Moinudeen. Bayesian Probabilistic Reasoning Applied to Mathematical Epidemiology for Predictive Spatiotemporal Analysis of Infectious Diseases, dissertation, May 2006; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc5302/m1/12/: accessed April 18, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .