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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Franklin Open |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773186325000611 |
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