This poster was featured at the 2013 Perot Museum of Nature and Science's 'Social Science' exhibit. It discusses the Global Stochastic Field Simulator, conceived in the summer of 2011

Camp, Taylor

Helsing, Joseph

Indrakanti, Saratchandra

Mikler, Armin R.

O'Neill, Marty

UNT Digital Library

GLOBAL STOCHASTIC FIELD SIMULATORCenter for Computational Epidemiology and Response AnalysisTHE UNIVERSITY OF NORTH TEXASPURPOSE OF THIS SIMULATORThe Global Stochastic Field Simulator was conceived in the Summer of 2011. The goal was to design a tool that allowed computational epidemiologists to experiment with different disease and vaccination parameters using agent-based and stochastic models. This simulator provides a framework for scientists to visualize and experiment with different types of disease outbreaks and intervention strategies.An agent-based model is one in which each of the members of a population is represented as an individual agent. Agents can come into contact with each other. As infectious agents come into contact with susceptible agents, the disease spreads through the population.THE AGENT BASED MODELThe global stochastic contact model uses a hybrid approach based on stochastic and agent-based modeling. It combines the stochastic model’s ability to make random choices with the agent-based representation of individuals. In this simulator, the computer randomly selects a limited number of contacts between individuals. These contacts create a pathway for the disease to spread through the population.THE GLOBAL STOCHASTIC CONTACT MODELA stochastic model is one that is based upon random processes. This is useful for modeling complex real-life situations in which choices and actions appear to be random occurrences.THE STOCHASTIC MODELThe Global Stochastic Field Simulator combines these models into a single framework. It allows the user to infect a section of the population of agents and examine how different parameters affect the spread of the disease. It enables the user to explore different vaccination intervention strategies to see how best to control a particular disease. This allows computational epidemiologists to gather data and visualize different types of disease outbreaks.THE GLOBAL STOCHASTIC FIELD SIMULATORBelow is a list and description of the different parameters that can be adjusted in this simulator.SEIR/SEIRS: Determines whether the SEIR or SEIRS model is used for the stages an agent goes through in relation to the disease. In the SEIR model, once an agent recovers it cannot become infected again. In the SEIRS model an agent can become infected again following the end of its recovery period.Contact Rate: The number of contacts each agent will make per day.Transmission Probability: The probability of a susceptible agent getting a disease after coming into contact with an infectious agent.Latent Period: The number of days an agent will remain non-infectious after being exposed and infected.Infectious Period: The number of days an agent will remain infectious after it stops being latent.Recovered Period: The number of days an agent will have immunity from a disease before becoming susceptible again.  In the SEIR model this period is innite.PARAMETERS

Global Stochastic Field Simulator

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Global Stochastic Field Simulator

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