SCIENTISTS using mathematical models to predict the spread of disease across populations must consider evolutionary changes, such as mutations, to accurately estimate the clinical impact. This is according to a study conducted by Dr Osman Yagan and his team at the Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
The models are typically imputed with data on the initial rate of virus spreading, and this is extrapolated to forecast the expected dispersion of disease. However, the possibility of pathogenic mutation is not recognised and is a factor that has potential to alter the speed at which the virus spreads; Dr Yagan warns that “if you don’t consider the potential changes over time, you will be wrong in predicting the number of people that will get sick.”
In this study, the researchers tested their mathematical theory by using thousands of computer-simulated epidemics in real-world networks, which included contact networks between students, teachers, and staff at a school in the USA and staff and patients in a hospital in France. They also looked at how information spreads online via platforms such as Twitter, which can be classed as an epidemic when information is tweaked and goes ‘viral.’
After proving that their theory matched the observations seen in the simulations, scientist Rashad Eletreby stated that: “Traditional models that don’t consider evolutionary adaptations fail at predicting the probability of the emergence of an epidemic.” The authors recognise that although their model is not as accurate as real-time data tracking of virus evolution, they are “one step closer to reality,” says Eletreby.