Bayesian Probabilistic Reasoning Applied to Mathematical Epidemiology for Predictive Spatiotemporal Analysis of Infectious Diseases Page: 55
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CHAPTER 7
SPATIAL CORRELATION OF DISEASE PREVALENCE FOR
INFLUENZA AND PNEUMONIA
1 Disease monitoring plays a crucial role in the implementation of public health measures.
The demographic profiles of the people and the disease prevalence in a geographic region are
analyzed for inter-causal relationships. Bayesian analysis of the data identifies the pertinent
characteristics of the disease under study. The vital components of control and prevention
of the disease spread are identified by Bayesian learning for the efficient utilization of the
limited public health resources. Bayesian computing, layered with epidemiological expertise,
provides the public health personnel to utilize their available resources optimally to minimize
the prevalence of the disease. Bayesian analysis is implemented using synthetic data for two
different demographic and geographic scenarios for pneumonia and influenza, that exhibit
similar symptoms. The analysis infers results on the effects of the demographic parame-
ters, namely ethnicity, gender, age, and income levels, on the evidence of the prevalence of
the diseases. Bayesian learning brings in the probabilistic reasoning capabilities to port the
inferences derived from one region to another.
7.1. Bayesian Analysis
The Bayesian network (Fig. 7.1) analyzes the effects of the demographic parameters on
the incidence of symptoms and the related diseases in a geographic area. The demographic
1This chapter is reprinted from: Advances in Bioinformatics and its Applications, Proceedings of the Inter-
national Conference, Nova Southeastern University, Fort Lauderdale, Florida, USA 16 - 19 December 2004. K.
Abbas, A. Mikler, A. Ramezani and S. Menezes, Computational Epidemiology: Bayesian Disease Surveillance,
pp. 95-106, 2004, with permission from World Scientific Publishing Co. Pte. Ltd, Singapore.55
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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/m1/68/: accessed April 23, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .