Estimating the state of epidemics spreading with graph neural networks Estimating the state of epidemics

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...

Full description

Saved in:
Bibliographic Details
Published inNonlinear dynamics Vol. 109; no. 1; pp. 249 - 263
Main Authors Tomy, Abhishek, Razzanelli, Matteo, Di Lauro, Francesco, Rus, Daniela, Della Santina, Cosimo
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.07.2022
Subjects
Automotive Engineering
Classical Mechanics
Control
Dynamical Systems
Engineering
Mechanical Engineering
Original Paper
Vibration
Epidemics
CoVid-19
Network dynamics
Nonlinear inference
State estimation
Online AccessGet full text
ISSN0924-090X
1573-269X
DOI10.1007/s11071-021-07160-1

Cover

More Information
Summary: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.
ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-021-07160-1