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Estimating the state of epidemics spreading with graph neural networks

Tomy, Abhishek and Razzanelli, Matteo and Di Lauro, Francesco and Rus, Daniela and Della Santina, Cosimo (2022) Estimating the state of epidemics spreading with graph neural networks. Nonlinear Dynamics, 109 (1), pp. 249-263. Springer. doi: 10.1007/s11071-021-07160-1. ISSN 0924-090X.

[img] PDF - Preprint version (submitted draft)

Official URL: https://dx.doi.org/10.1007/s11071-021-07160-1


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.

Item URL in elib:https://elib.dlr.de/193635/
Document Type:Article
Title:Estimating the state of epidemics spreading with graph neural networks
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Tomy, AbhishekInria Grenoble - Rhône-AlpesUNSPECIFIEDUNSPECIFIED
Razzanelli, MatteoProxima Robotics srl, PisaUNSPECIFIEDUNSPECIFIED
Di Lauro, FrancescoUniversity of OxfordUNSPECIFIEDUNSPECIFIED
Della Santina, CosimoUNSPECIFIEDhttps://orcid.org/0000-0003-1067-1134UNSPECIFIED
Date:21 January 2022
Journal or Publication Title:Nonlinear Dynamics
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 249-263
Keywords:graph neural networks
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Robotics
DLR - Research area:Raumfahrt
DLR - Program:R RO - Robotics
DLR - Research theme (Project):R - Basic Technologies [RO]
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Analysis and Control of Advanced Robotic Systems
Institute of Robotics and Mechatronics (since 2013)
Deposited By: Strobl, Dr. Klaus H.
Deposited On:27 Jan 2023 14:49
Last Modified:19 Oct 2023 12:31

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