Comparison of classical, xgboost and neural network methods for parameter estimation in epidemic processes on random graphs

The main goal of this paper is to quantitatively compare the performance of classical methods to XGBoost and convolutional neural networks in a parameter estimation problem for SIR epidemic spread. Since we model the underlying social network by flexible two-layer random graphs, we can also study ho...

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Bibliographic Details
Main Authors:	Ágnes Backhausz, Edit Bognár, Villő Csiszár, Damján Tárkányi, András Zempléni
Format:	Article
Language:	English
Published:	Elsevier 2025-06-01
Series:	Franklin Open
Subjects:	Convolutional neural network Epidemic spread Two-layer random graph Xgboost
Online Access:	http://www.sciencedirect.com/science/article/pii/S2773186325000611
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Comparison of classical, xgboost and neural network methods for parameter estimation in epidemic processes on random graphs

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