Session 1C: A Computational Graph-Theory Approach to Analyzing the Spread of Epidemics
Session Number
Session 1C: 2nd Presentation
Advisor(s)
Jordan Hasler, Illinois Mathematics and Science Academy
Location
Room A151
Start Date
28-4-2017 8:30 AM
End Date
28-4-2017 9:45 AM
Abstract
This investigation utilized both the SIR (Susceptible, Infected, and Recovered) and SI models to determine the progression of various diseases within blocks of a population. The constraints for experiment, chosen comprehensively as possible considering practicality were gender, age, and location (degrees of rural and urban both in the representative countries of each stage of the demographic transition). The experimenters modeled viruses real time by manipulation of compartment variables and transmission rates between them using Python, Java, and C#. A machine learning, neural network type programming mechanism was used to mimic interactions. Network and graph theory as well as differential equations were also used for modeling. Potentially implications include predicting disease epidemiology and better understanding the influence of the different aforementioned variables (compartments) in a population, useful especially in the event of an endemic.
Session 1C: A Computational Graph-Theory Approach to Analyzing the Spread of Epidemics
Room A151
This investigation utilized both the SIR (Susceptible, Infected, and Recovered) and SI models to determine the progression of various diseases within blocks of a population. The constraints for experiment, chosen comprehensively as possible considering practicality were gender, age, and location (degrees of rural and urban both in the representative countries of each stage of the demographic transition). The experimenters modeled viruses real time by manipulation of compartment variables and transmission rates between them using Python, Java, and C#. A machine learning, neural network type programming mechanism was used to mimic interactions. Network and graph theory as well as differential equations were also used for modeling. Potentially implications include predicting disease epidemiology and better understanding the influence of the different aforementioned variables (compartments) in a population, useful especially in the event of an endemic.