Date of Award

2014

Degree Type

Dissertation

Degree Name

Ph.D.

Department

Computer Science

First Advisor

Gras, Robin

Keywords

Applied sciences, EcoDemics, Susceptible-infected-removed, Fuzzy cognitive map

Rights

CC BY-NC-ND 4.0

Abstract

Modeling the progress of an epidemic in a population has received significant attention among various fields of science. Many epidemiological models assume random mixing of the population, homogeneous hosts, and a static environment. We are interested in modeling epidemic spread in a dynamic evolving ecosystem with behavioral models associated to its individuals. To this end, we present EcoDemics; which integrates the classical SIR (Susceptible-Infected-Removed) disease model to an individual-based evolutionary predator-prey ecosystem simulation, EcoSim. The behavioral model of each agent in EcoDemics is based on a fuzzy cognitive map (FCM) that determines the heterogeneous interactions between individuals. We present the disease model used and we demonstrate how the epidemic spread in a random mixing ecosystem differs from a heterogeneous ecosystem with its behavioral model. We observed that dynamics of the ecosystem, along with the spatial distribution of agents, play a significant role in disease progression. Due to the high mitigation capacity and significance of the immunization intervention, we explore vaccination techniques with various time delays and population proportions in EcoDemics. Based on the herd immunity theory, the whole population can be protected against a contagious disease by vaccination of a fraction of individuals. We investigate this principle in EcoDemics and compare our results with real epidemics data. A number of mathematical simulations have been used to analyze host-pathogen dynamics in the presence of predators; however, to the best of our knowledge, this is the first individual-based modeling study exploring the effect of predators on prey infection dynamics in a predator-prey ecosystem simulation. We used the EcoDemics framework to investigate the effect of predation on infection dynamics in EcoDemics. Our results are in agreement with both numerical and field studies.

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