(Electrical Engineering, University of Manitoba)
Discrete Time and Space Scheduled Walkers for Epidemic Modeling
|Date||Tuesday, March 25, 2008|
This brief exploratory talk outlines an epidemic simulator built on data-driven modeling of the spread of a disease. The intent of the model is to provide insight into how a disease may reach a tipping point, spreading to epidemic and uncontrollable proportions. In contrast to analytical methods, simulation is arguably an effective means of gaining a better understanding of how a disease may spread within a population. There are a large number of theoretical models that can be applied to epidemics, all attempting to capture the basic phenomenon of disease dynamics. Our conjecture is to use data-driven models that are reasonable, realistic, and practical in an attempt to demonstrate their efficacy in studying the epidemic phenomenon. The talk will outline our conjectures providing a specification for implementation. Based on the model it should be clear that our underlying simulation model is that of a Discrete-Space Scheduled Walker (DSSW), in contrast to other models that are more traditionally based on random or Brownian walkers on irregular topologies, often computer generated. We attempt to capture the most important aspects of real-people networks, incorporating (by construction) notions such as "small world" networks. The talk will include a demonstration of the simulation tools and environment.