Bayesian Probabilistic Reasoning Applied to Mathematical Epidemiology for Predictive Spatiotemporal Analysis of Infectious Diseases Page: 4
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The significance of computational epidemiology as a new field has been underscored by a
special program at the Center for Discrete Mathematics and Theoretical Computer Science
(DIMACS) [28], funded as an National Science Foundation (NSF) Technology Center. A 5-
year program, consisting of working groups and short-courses focusing on computational and
mathematical epidemiology began in the summer of 2002. It emphasizes the development
and strengthening of collaborations and partnerships between mathematicians, computer sci-
entists, biologists, sociologists, bio-statisticians, and epidemiologists.
1.3. Motivation
During the last century, life sciences has made tremendous progress in identifying, treating,
or even eradicating many infectious diseases. This can primarily be attributed to the increased
understanding of the etiology and pathogenesis of such diseases. Nevertheless, newly emerg-
ing or re-emerging infectious diseases continue to occur regularly [40]. Some diseases have
changed their appearance, some have become resistant to drug treatment, while others are
new that no previous outbreaks have ever been studied. It is ironic that epidemiologists have
to take advantage of a disease outbreak in order to collect data necessary to formulate public
health policy. Medical research has enhanced the understanding of disease characteristics in
an individual. For example, the characteristic epidemiological stages of influenza as described
by latent period, infectious period, and recovery period [15] as experienced by an individual
are well known [15, 25]. So are the symptomatic stages of influenza (i.e., incubation period
until symptoms occur) . The manifestation and the spread of many infectious diseases in the
population remains elusive and is dependent on the socio-behavioral interaction patterns and
population dynamics.
To gain insight into the intricacies of disease dynamics in a specific population, statistical
and mathematical models of infectious disease epidemics have been developed. However,
further understanding into the composition of an epidemic will facilitate better policy and and
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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/m1/17/: accessed April 24, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .