Bayesian Probabilistic Reasoning Applied to Mathematical Epidemiology for Predictive Spatiotemporal Analysis of Infectious Diseases Page: 71
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CHAPTER 9
SOCIO-BEHAVIORAL ANALYSIS OF INFLUENZA OUTBREAKS
Prevalence, incidence, morbidity and mortality rates are used to evaluate the impact of an
infectious disease outbreak in a population. Knowledge of the social behavioral interactions
among the people will complement the understanding of the progression of air-borne diseases
such as influenza. A methodology using Hidden Markov models (HMMs) is presented to
gain a better understanding of the social behavioral interactions that directly relate to the
observed prevalence and incidence of an infectious disease.
9.1. Influenza Outbreak Data Simulator
Influenza outbreak data is generated using the simulator developed by S. Venkatachalam
and A. Mikler [49, 71, 72]. The simulator is based on the principle of global stochastic
field simulation (GSFS) and incorporates geographic and demographic interactions. The
interactions are based on the geographic information systems (GIS) gravity model. The GSFS
model is oriented for heterogeneous population, and can incorporate interactions based on
geography, demography, environment and migration patterns.
Spatial distribution of the population is represented as cells, similar to the traditional
cellular automata paradigm. Each cell represents an individual or a sub-population. Each
cell is characterized with state and likelihood risks for exposure and disease contraction. To
simulate the disease spread in such an environment, contacts need to be established between
cells. A cell is capable of interaction with any other cell in the environment. The probability of
contact is based on an interaction coefficient that takes into account the distance, population,
demographics and socio-economic factors.71
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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/m1/84/: accessed April 17, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .