The April issue of the online open-access journal PLoS Biology reports that an international team of scientists has developed an innovative tool in form a game to teach the basics of epidemiology, i.e. the science of how infectious diseases spread amongst the population.

Each year, the team holds a workshop in South Africa, which assists epidemiologists in improving their mathematical models for studying outbreaks of diseases, such as AIDS, malaria and cholera. Under the lead of Steve Bellan from the University of California at Berkeley, the team developed a new game as a teaching aid, which has proven to be very effective in demonstrating various epidemiology concepts. Players simulate a real-life epidemic by passing around pieces of paper that read, "You have been infected," together with instructions for spreading the disease.

Study co-author, Juliet Pulliam, a biologist at the University of Florida, explains:

"Infectious disease modeling is an established field of
study in bio-mathematics."

She continues explaining that because mathematicians tend to work independently from practitioners in tracking spreading diseases, the game is meant to demonstrate that collaborating can achieve much better results.

Pulliam says:

"Not knowing how data about an outbreak was collected can
lead to misinterpretations."

For instance, a change in procedures of how infected individuals are counted could create a sudden rise in the data, which gives a false interpretation of how the disease is actually being spread. Once this false interpretation is implemented in the model, it could alter projections and interfere with efforts on the ground to prevent further outbreaks.

According to Bellan, an ecologist who specializes in epidemiology of wildlife diseases, collaborations between bio-mathematicians and classical epidemiologists have already proven that researchers can gain new insight of tracking the spread of diseases.

HIV interventions for instance, as well as efforts to eradicate trachoma, a bacterial infection causing blindness, have successfully used the tag-team approach. Studies have provided evidence in both cases that there is a higher chance of an epidemic being interrupted when practitioners employ the power of mathematical modeling to improve their intervention strategies.

Bellan states: "This is about the importance of collaboration. No one can be an expert in everything. We want to see more scientists working together from the start," which is why every year Bellan, Pulliam and six other Canadian, U.S. and South African scientists offer two-week clinics at the African Institute for Mathematical Sciences. The clinics enable epidemiological mathematicians to gain a more complete insight into the human aspects of how disease spreads, and the new game has substantially changed the approach in which this is achieved.

The concept of the game is to notify participants that they have been exposed to disease by receiving an 'infectious' piece of paper, which they are asked to email to Bellan, informing him of their fate. To determine the number of people who should already be infected, they use a random number generator, and then pass this number of "infections" to other participants. The rules are designed to spread the disease and to also build a set of data of who infected whom and when. Pulliam explains: "The drill produced an outbreak with data that looks like a real epidemic."

Course participants usually spend the first week discussing where the data sets have originated, who collects them, and what the numbers refer to.

Pulliam comments:

"Using the game as a way to demonstrate those issues instead of talking about them is
instructive on its own."

The second week of the course allows participants to experience real benefits by conducting group experiments with different epidemiological models that use actual data sets, which are usually from HIV studies or other ongoing projects.

Pullliam says:

"Many opted to work with data sets from the game." She continued that becoming familiar with the process for collecting data greatly improved the participants' ability to customize mathematical models, which meant that the models accurately represented the spread of disease across population. She concludes: "And that's exactly what we wanted them to get out of the workshop."

Written by Petra Rattue