Estimating the state of epidemics spreading with graph neural networks
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11071_2021_7160_ReferencePDF.pdf
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Author(s)
Tomy, Abhishek
Razzanelli, Matteo
Di Lauro, Francesco
Rus, Daniela
Della Santina, Cosimo
Date Issued
January 21, 2022
Publisher
Springer Netherlands
Citation
Tomy, Abhishek, Razzanelli, Matteo, Di Lauro, Francesco, Rus, Daniela and Della Santina, Cosimo. 2022. "Estimating the state of epidemics spreading with graph neural networks."
Version
Author's final manuscript
Abstract
Abstract
When an epidemic spreads into a population, it is often impractical or impossible to continuously monitor all subjects involved. As an alternative, we propose using algorithmic solutions that can infer the state of the whole population from a limited number of measures. We analyze the capability of deep neural networks to solve this challenging task. We base our proposed architecture on Graph Convolutional Neural Networks. As such, it can reason on the effect of the underlying social network structure, which is recognized as the main component in spreading an epidemic. The proposed architecture can reconstruct the entire state with accuracy above 70%, as proven by two scenarios modeled on the CoVid-19 pandemic. The first is a generic homogeneous population, and the second is a toy model of the Boston metropolitan area. Note that no retraining of the architecture is necessary when changing the model.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/s11071-021-07160-1
