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2012-02-28 - Kyle Hickmann


Tuesday, February 28, 2012
101 Stanley Thomas Hall
Tulane University (Uptown)

Refreshments will be served


Kyle Hickmann, Center for Computational Science, Tulane University


Uncertainty Quantification for Epidemic Dynamics of Stochastic Simulations


It is standard practice to run complex simulations of many natural phenomenon using mathematical models based on  small  scale properties of  the system. However, when  this is  done  the  output of the simulation may  be as  difficult to  understand  as  the original observed  phenomenon.  Researchers  have  developed uncertainty  quantification  methods  to  statistically  describe  the  effect  of  parameter inputs on simulation output  to  analyze  these models. In the beginning of this talk I  will describe some of the common methods used for  uncertainty  quantification  and  sensitivity  analysis.  The  second  part  of  this  talk  will  focus  on uncertainty quantification for EpiSimS, an agent based model of epidemic spread through a large population in use at Los Alamos National Laboratory.  I will  describe tools developed to study the dynamics of disease spread using stochastic simulations. The methods  focus  on  finding  a set of variables that characterize the deterministic and stochastic parts of the disease progression separately. These variables may then be used for a sensitivity analysis and uncertainty quantification study.

Center for Computational Science, Stanley Thomas Hall 402, New Orleans, LA 70118