Bayesian Probabilistic Reasoning Applied to Mathematical Epidemiology for Predictive Spatiotemporal Analysis of Infectious Diseases Page: 3
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among the different demographic subgroups. A methodology using hidden Markov
models is introduced that enables to investigate the impact of social behavioral
interactions in the incidence and prevalence of infectious diseases. The methodology is
presented in the context of simulated disease outbreak data for influenza. Probabilistic
reasoning analysis enhances the understanding of disease progression in order to
identify the critical points of surveillance, control and prevention. Public health
resources, prioritized by the order of risk levels of the population, will efficiently minimize
the disease spread and curtail the epidemic at the earliest.
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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/m1/3/: accessed April 25, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .