Tomy, Abhishek und Razzanelli, Matteo und Di Lauro, Francesco und Rus, Daniela und Della Santina, Cosimo (2022) Estimating the state of epidemics spreading with graph neural networks. Nonlinear Dynamics, 109 (1), Seiten 249-263. Springer. doi: 10.1007/s11071-021-07160-1. ISSN 0924-090X.
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Offizielle URL: https://dx.doi.org/10.1007/s11071-021-07160-1
Kurzfassung
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.
elib-URL des Eintrags: | https://elib.dlr.de/193635/ | ||||||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||
Titel: | Estimating the state of epidemics spreading with graph neural networks | ||||||||||||||||||||||||
Autoren: |
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Datum: | 21 Januar 2022 | ||||||||||||||||||||||||
Erschienen in: | Nonlinear Dynamics | ||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||
Band: | 109 | ||||||||||||||||||||||||
DOI: | 10.1007/s11071-021-07160-1 | ||||||||||||||||||||||||
Seitenbereich: | Seiten 249-263 | ||||||||||||||||||||||||
Verlag: | Springer | ||||||||||||||||||||||||
ISSN: | 0924-090X | ||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||
Stichwörter: | graph neural networks | ||||||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||||||
HGF - Programmthema: | Robotik | ||||||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||||||
DLR - Forschungsgebiet: | R RO - Robotik | ||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Basistechnologien [RO] | ||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Robotik und Mechatronik (ab 2013) > Analyse und Regelung komplexer Robotersysteme Institut für Robotik und Mechatronik (ab 2013) | ||||||||||||||||||||||||
Hinterlegt von: | Strobl, Dr. Klaus H. | ||||||||||||||||||||||||
Hinterlegt am: | 27 Jan 2023 14:49 | ||||||||||||||||||||||||
Letzte Änderung: | 19 Okt 2023 12:31 |
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