Modeling Infectious Disease Spread Using Global Stochastic Field Simulation Page: I
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Venkatachalam, Sangeeta, Modelingq Infectious Disease Spread Usingq Global
Stochastic Field Simulation. Master of Science (Computer Science), August 2006, 65
pp., 9 tables, 31 illustrations, bibliography, 49 titles.
Susceptibles-infectives-removals (SIR) and its derivatives are the classic
mathematical models for the study of infectious diseases in epidemiology. In order to
model and simulate epidemics of an infectious disease, a global stochastic field
simulation paradigm (GSFS) is proposed, which incorporates geographic and
demographic based interactions. The interaction measure between regions is a function
of population density and geographical distance, and has been extended to include
demographic and migratory constraints. The progression of diseases using GSFS is
analyzed, and similar behavior to the SIR model is exhibited by GSFS, using the
geographic information systems (GIS) gravity model for interactions. The limitations of
the SIR and similar models of homogeneous population with uniform mixing are
addressed by the GSFS model. The GSFS model is oriented to heterogeneous
population, and can incorporate interactions based on geography, demography,
environment and migration patterns. The progression of diseases can be modeled at
higher levels of fidelity using the GSFS model, and facilitates optimal deployment of
public health resources for prevention, control and surveillance of infectious diseases.
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Venkatachalam, Sangeeta. Modeling Infectious Disease Spread Using Global Stochastic Field Simulation, thesis, August 2006; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc5335/m1/2/: accessed July 18, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .