Parameter estimation of epidemic spread in two-layer random graphs by classical and machine learning methods

TL;DR

This paper compares classical and machine learning methods for estimating epidemic spread parameters on two-layer random graphs, analyzing the impact of graph structure and additional information on estimation accuracy.

Contribution

It provides a comprehensive comparison of classical, XGBoost, and CNN methods for epidemic parameter estimation on flexible two-layer graphs, including the influence of graph structure and extra data.

Findings

Machine learning methods outperform classical ones in certain phases.

Graph structure variability affects estimation accuracy.

Additional information like average degree improves results.

Abstract

Our main goal in this paper is to quantitatively compare the performance of classical methods to XGBoost and convolutional neural networks in a parameter estimation problem for epidemic spread. As we use flexible two-layer random graphs as the underlying network, we can also study how much the structure of the graphs in the training set and the test set can differ while to get a reasonably good estimate. In addition, we also examine whether additional information (such as the average degree of infected vertices) can help improving the results, compared to the case when we only know the time series consisting of the number of susceptible and infected individuals. Our simulation results also show which methods are most accurate in the different phases of the epidemic.