Estimating the state of epidemics spreading with graph neural networks

Abhishek Tomy, Matteo Razzanelli, Francesco Di Lauro, Daniela Rus, Cosimo Della Santina

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish
Pages (from-to)249-263
Number of pages15
JournalNonlinear Dynamics
Issue number1
StatePublished - Jul 2022
Externally publishedYes


  • CoVid-19
  • Epidemics
  • Network dynamics
  • Nonlinear inference
  • State estimation


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