New Mexico Supercomputing Challenge

Comparison of Disease Propagation Models that Predict the Speed of an Outbreak

Team: 5

School: Albuquerque Academy

Area of Science: Epidemiology


Interim: Interim Report

Team 5

1. Definition of the Problem

Diseases are common and often dangerous without proper precautions. Throughout the years, analysts and scientists have studied outbreaks of viruses to predict what would happen in the case of another outbreak, and could then plan what to do. Predicting the speed and scale of an outbreak would greatly help medical authorities prepare for it. This takes large amounts of time, which can be a major problem if outbreaks are quick to occur. This year we plan to create software simulations that model the propagation of several diseases. This is built upon our project’s accomplishments from last year, in which only one disease and algorithm were simulated.
We are going to utilize several different algorithms to model the propagation of a few diseases. Some of these algorithms may include: RBAs (Rule Based Algorithm), MBAs (Math Based Algorithm) and RMBA (Rule and Math hybrid Based Algorithm). The RBA algorithm uses rules to determine whether an agent’s state of infection (susceptible, infected, or recovered). The MBA algorithm uses math to determine an agent’s state. The goal of our project is to effectively and accurately model the spread of multiple viruses, through the use of separate algorithms for each.


2. Plan for Computationally Solving the Problem

We have chosen to simulate the propagation of several diseases using the Java programming language. Propagation model simulations are graphically displayed inside of a UI (User Interface) software application. A simulation run will affect thousands of Agents over several hundred days. The agents will be independent of one another using threads and will travel daily according to their occupation. We plan to implement a transportation algorithm so that the agents will travel along routes and provide their location and state of infection. Our application will show propagation quantitatively through a graph plotting the number of infected agents per day. Last, actual data will be gathered on previous outbreaks for comparison analysis.


3. Description of the Progress Made to Date

A Java software engine has been created which can successfully run a sample simulation. This simulation moves thousands of agents randomly on a map of the United States showing a 365 day simulation. In a simulation, thousands of independent agents are instantiated and each agent runs in its own thread which allows the agents, like people, to "live" individual lives. This is a step above last year’s project which used a static-structure to model agents. Software stubs have been added into each agent's class for instantiating child classes which will govern agent life patterns including travel and social behavior. Actual virus propagation data from epidemics has successfully been acquired. This raw data acquired has been plotted and initial analysis has begun.

4. Expected Results

The goal of this project is to accurately model the spread of selected diseases using different algorithms for each disease. We intend to do this with both graphical and computational analysis to gain an understanding of the basis of disease propagation, and to determine any general patterns.


5. Citations

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Team Members:

  Marcus Dominguez-Kuhne
  David Kohler
  Josh Konopka
  Trevor Kann
  Hisham Temmar

Sponsoring Teacher: Jim Mims

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