Deep Graph Machine Learning Models for Epidemic Spread Prediction and Prevention

Katia Papakonstantinopoulou, Charalampos Salis

Abstract: Epidemic spread prediction and prevention have been of paramount significance for safeguarding the public health and quality of life. However, the adoption of the appropriate safety measures and actions should also take into account other societal challenges, such as the impact on the local economy or the psychological strain on its inhabitants. Recent approaches for preventing an epidemic spread have led to the adoption of rather aggressive strategies with significantly negative side-effects. In this work we address the aforementioned issue by developing a dynamic and data-driven prevention strategy, using modern graph machine learning predictive models. This strategy imposes more realistic assumptions about the pandemic spread and the underlying network structure, while also minimizing the negative side-effects. Finally, the experimental evaluation of our novel architecture for the predictive model demonstrates that we significantly outperform existing methods.