Estimating the state of epidemics spreading with graph neural networks

Journal article (2022)

Authors

Abhishek Tomy Inria Grenoble Rhône-Alpes
Matteo Razzanelli Proxima Robotics srl
Francesco Di Lauro University of Oxford
Daniela Rus Massachusetts Institute of Technology
C. Della Santina Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 Abhishek Tomy, Matteo Razzanelli, Francesco Di Lauro, Daniela Rus, C. Della Santina
DOI
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https://doi.org/10.1007/s11071-021-07160-1
Epidemics State estimation Network dynamics CoVid-19 Nonlinear inference
More Info
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Published Date

2022

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

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.