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Auteur Jiangzhuo Chen |
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Online flu epidemiological deep modeling on disease contact network / Liang Zhao in Geoinformatica, vol 24 n° 2 (April 2020)
[article]
Titre : Online flu epidemiological deep modeling on disease contact network Type de document : Article/Communication Auteurs : Liang Zhao, Auteur ; Jiangzhuo Chen, Auteur ; Feng Chen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 443 – 475 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification semi-dirigée
[Termes IGN] données localisées des bénévoles
[Termes IGN] épidémie
[Termes IGN] itération
[Termes IGN] maladie infectieuse
[Termes IGN] maladie virale
[Termes IGN] modélisation
[Termes IGN] optimisation (mathématiques)
[Termes IGN] réseau social
[Termes IGN] surveillance sanitaireRésumé : (auteur) The surveillance and preventions of infectious disease epidemics such as influenza and Ebola are important and challenging issues. It is therefore crucial to characterize the disease progress and epidemics process efficiently and accurately. Computational epidemiology can model the progression of the disease and its underlying contact network, but as yet lacks the ability to process of real-time and fine-grained surveillance data. Social media, on the other hand, provides timely and detailed disease surveillance but is insensible to the underlying contact network and disease model. To address these challenges simultaneously, this paper proposes a novel semi-supervised neural network framework that integrates the strengths of computational epidemiology and social media mining techniques for influenza epidemiological modeling. Specifically, this framework learns social media users’ health states and intervention actions in real time, regularized by the underlying disease model and contact network. The learned knowledge from social media can then be fed into the computational epidemic model to improve the efficiency and accuracy of disease diffusion modeling. We propose an online optimization algorithm that iteratively processes the above interactive learning process. he extensive experimental results provided demonstrated that our approach can not only outperform competing methods by a substantial margin in forecasting disease outbreaks, but also characterize the individual-level disease progress and diffusion effectively and efficiently. Numéro de notice : A2020-359 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-019-00376-9 Date de publication en ligne : 25/07/2019 En ligne : https://doi.org/10.1007/s10707-019-00376-9 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95264
in Geoinformatica > vol 24 n° 2 (April 2020) . - pp 443 – 475[article]