There’s a terrible disease that could threaten an entire colony of chimpanzees. The disease is highly contagious and very deadly. Fortunately, a new vaccine is available. But the vaccine is expensive, and it is difficult to administer to the chimpanzees. Due to these complications, only a select few of the entire colony of chimpanzees can be vaccinated. How do you choose?
Any time one is faced with such a problem, the first step should be to collect some data. In this situation we need to learn about how the disease might spread through the population. We need to learn about which chimpanzees have contact with each other. Perhaps there are certain chimpanzees that bridge different groups. We probably should also know how infectious the disease is, how long is a chimpanzee infectious for, which chimpanzee is likely to be the first to get the disease and other important factors.
Let’s say that you have your data, you know about how this disease is spread, and you want to identify the optimal set of chimpanzees to vaccinate. How can we do this? Lives are at stake and we have limited resources. The pressure is on and we need to make the right choice!
This was the situation I was faced with when I started working with my colleagues Dr. Kevin Potts, Dr. Pavel Belik, and Anika Clark. As it turns out, the disease is not just some scary idea, Ebola is a real threat for chimpanzees. In order to learn about this very issue, Dr. Potts and Anika (a student here at Augsburg College) had collected the necessary data from a chimpanzee colony in Uganda, and now they wanted to use their data to make some decisions.
It is always exciting for me to encounter a new data source and a new problem. I especially am energized by the fact that the results of our analyses are going to have a real impact on saving chimpanzees!
Dr. Potts, Dr. Belik, Anika Clark, and I have talked over the important factors that could impact the disease dynamics. We created a stochastic (i.e. incorporating randomness) disease simulation model to learn about how the disease will spread. I coded up the model, and have developed an app so that anyone can test out different scenarios. Check it out here.
In this model you can decide which chimpanzee gets the disease first, what the infectivity is, how long an infected chimpanzee is infectious for, how many simulations you want to run, which chimpanzees you want to vaccinate, how many days you want the simulation to cover, and much more!
Once you enter in the different inputs, and hit “Submit”, then the results of your simulation will be displayed. You will see a visualization of the chimpanzee social network (based on the data collected by Dr. Potts and Anika Clark) and a graphic that displays the proportion of chimpanzees that were infected at different time points (median with 25th and 75th percentiles). This can give you an idea of what proportion of chimpanzees will be infected under many different conditions.
So go ahead. Check out the app!
If you wish you knew more about how these simulations work, you are in luck! We are in the process of writing a paper about this model. I will be sure to post an update on this blog once the paper is out.