  The application of machine learning (ML) techniques span a vast spectrum
ranging from speech, face and character recognition, medical diagnosis, anomaly
detection in data to the general classification, prediction, and regression
problems. In the present work, we solve the problem of predicting R_0 for
disease spreading on complex networks using the regression-based state-of-art
ML techniques. R_0 is a metric that determines whether the disease-free
epidemic or an endemic state is asymptotically stable and hence indicates the
controllability of the disease spread. We predict R_0 , based on training the
ML models with structural properties of complex networks, irrespective of the
network type. The prediction is possible because: (a) The structure of complex
networks plays an essential role in the spreading processes on networks (b) The
regression techniques such as Support Vector Regression and Artificial Neural
Network Model can be very efficiently used for prediction problems, even for
non-linear data. We obtained good accuracy in the prediction of R_0 for the
simulated networks as well as real-world networks using these techniques.
Moreover, the ML model training is a one-time investment cost in terms of
training time and memory, and the trained model can be used for predicting R_0
on unseen/new examples of networks.
