Week 1

First week of the REU has gone fairly smoothly I believe. Getting settled in and figuring out what I want to do has been fun. We started the week by getting to know everyone and showing the out of town students around downtown Denton. This was a good time and I think everyone had fun checking out some of the cool places around the square.

Dr. Mikler (my faculty sponsor) had been out of town this week so Dr. O'Neil has been helping me find some topics to research and finding a place to get started. Which leads us to our next topic.

Computational Epidemiology

Let's start by talking about this lab and what we do. We are the Computational Epidemiology Research Laboratory (CERL). The lab applies Computer Science techniques to epidemiology in order to provide better ways of modeling data and studying the spread of diseases. One particularly useful part of this subject is simulating the way a disease can spread through a given population. For example, a simulation can be created where a given number of people in a population have the flu. Using a mathematical data model known as the SEIR model (Susceptible, Exposed, Infected, Recovered), we can simulate the way this disease is transmitted through the public. We can control different parameters such as the starting number of infected, how likely a person is to be infected, how long the person is in the exposed state before transforming into infected and being able to infect others, and how likely a person is to recover from the infection. So we'll input these parameters, run the simulation for some amount of time and examine the results. The results will show us how many people were infected, how fast the infection spread, and how many people were able to recover. This data is very useful to health officials as they can use it to prepare for a possible pandemic. It will give them an insight to how this disease will spread and help them to plan prevention measures.

My Research

Before I talk about what I plan to research regarding this topic, let me take a step back. Part of my duties in the lab are restoring our cluster back to full functionality. Our cluster consists of 8 nodes, each with 2 quad core processors and 32GB of RAM a piece. This allows us to do some serious processing. This week I spent some time writing user management scripts so that we could add people on to the cluster easily for other members of the lab to use it. Now that these are in place, the cluster is in a usable state.

So with this in mind, what I would like to do is expand upon the simulation I discussed in the previous section. Current implementations of this type of simulation tend to focus on smaller population areas, like a concert or a festival. I believe this could be expanded into a larger population area such as a large city like Fort Worth or Dallas. When you scale things up to this magnitude, single processors would take an extremely long time to simulate something like this. This is where our cluster comes in. We can utilize the greater number of processors and large amount of RAM to scale the simulation up to potentially millions of individuals. This would speed up simulation time and allow us to analyze larger geographical areas more efficiently. We could also add more parameters to get more comprehensive data. If we add variables like income ranges and age ranges, we can see the way the disease affects different parts of the population.

Software Testing

With an application like this, we will need an effective way to test it. Currently there aren't readily available solutions for testing parallel applications such as this. So as I develop this simulation, I will be researching an developing a solution for Unit Testing or a similar testing method for cluster computing applications. This is a difficult subject because you have any number of processes running across the cluster and you need a way to examine them all to be sure they are providing correct results.

This is all pending approval by Dr. Mikler on his return.