Bayesian Probabilistic Reasoning Applied to Mathematical Epidemiology for Predictive Spatiotemporal Analysis of Infectious Diseases Page: 87
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fields complement each other and collaborate on concepts and methodologies to facilitate
the public health decision and policy making process. The intrinsic complexity of modeling
a suite of known and unknown parameters affecting an infectious disease outbreak make it
imperative to take advantage of today's high performance computing infrastructure. High
performance computing facilitates hypothesis testing and what-if analyses of scenarios that
do not readily lend themselves to field testing. These include epidemic analysis, vaccination
strategies and different resource allocation policies. The upcoming field of computational
epidemiology will aid in the prediction and analysis of disease manifestation and spread in a
given population through modeling, simulation and visualization; thereby leading to effective
public health policy making.
10.5. Final Remarks
Human population dynamics are continually increasing in complexity, especially in today's
modern world with rapid mobility and interaction patterns. The disease pathogens of molec-
ular size and invisible to our naked eye co-exist, adapt continually, and challenge the human
existence at the top of the food chain. The biochemical path of the pathogen within species
and across species, and the immune response systems in fighting the pathogen add another
layer of complexity. All living organisms are continually evolving to the changes in the co-
habiting environment. Disease progression analysis couples together the understanding of
viral genetics, environment and population dynamics, to infer the best prevention strategies
in thwarting disease outbreaks. The road to discovery in this realm of science will fascinate
and challenge our human mind and spirit for times to come.87
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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/m1/100/: accessed April 25, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .