Bayesian Probabilistic Reasoning Applied to Mathematical Epidemiology for Predictive Spatiotemporal Analysis of Infectious Diseases Page: 82
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10.1.1. Computational Epidemiology
The primary aim of computational epidemiology is to apply computational science paradigms
to the field of public health, thereby providing methodologies and tools for epidemiologists
and scientists in the public health domain. These novel methods will aid in the prediction and
analysis of disease manifestation and spread in a given population through modeling, simu-
lation, and visualization, thereby enable epidemiologists to conduct focused what-if-analyses
that facilitate the allocation of public health resources.
The ability to predict how a disease might manifest itself in the population at large is
essential for identifying disease monitoring and control strategies. Epidemiologists are tra-
ditionally relying on data that have been collected during previous outbreaks. However, for
newly emerging or re-emerging infectious diseases, such data are often unavailable or out-
dated. Changes in population composition and dynamics require the design of models that
bring together knowledge of the specific infectious diseases with the demographics and geog-
raphy of the region under investigation. Expertise from diverse domains are forged together
to develop new scientific methods that will enhance the understanding of the complexities of
disease dynamics in a population.
10.2. Epidemic Analysis using Probabilistic Reasoning
Spatiotemporal analysis of infectious diseases using probabilistic reasoning enhances the
planning and development of policies for optimal allocation of public health resources. Prob-
abilistic reasoning under uncertainty suits well to analysis of infectious disease dynamics.
The principles of Bayesian probabilistic reasoning is used to learn the stochastic dynamics of
the progression of infectious diseases. Quantitative and qualitative analyses of the disease
progression, including the incidence and prevalence, will aid in predicting the spatiotemporal
outbreak patterns for a demographic population in a specific geographic region.
Enhanced understanding of disease progression will facilitate in identifying the critical
points of surveillance, control and prevention. Public health resources are to be prioritized to82
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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/m1/95/: accessed April 23, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .