• May 15, 2019 News!Vol.7, No.5- Vol.8, No.4 has been indexed by EI (Inspec).   [Click]
  • Aug 01, 2018 News! [CFP] 2020 the annual meeting of IJMO Editorial Board, ECDMO 2020, will be held in Athens, Greece, February 15-17, 2020.   [Click]
  • Aug 05, 2019 News!Vol 9, No 5 has been published with online version. 10 original aritcles from 6 countries are published in this issue.    [Click]
General Information
    • ISSN: 2010-3697  (Online)
    • Abbreviated Title: Int. J. Model. Optim.
    • Frequency: Bimonthly
    • DOI: 10.7763/IJMO
    • Editor-in-Chief: Prof. Adrian Olaru
    • Executive Editor: Ms.Yoyo Y. Zhou
    • Abstracting/ Indexing: ProQuest, Crossref, Electronic Journals Library, Google Scholar, EI (INSPEC, IET), EBSCO, etc.
    • E-mail ijmo@iacsitp.com
Editor-in-chief
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2019 Vol.9(1): 30-33 ISSN: 2010-3697
DOI: 10.7763/IJMO.2019.V9.679

A Framework for Social Network-Based Dynamic Modeling and Prediction of Communicable Diseases

Samy Ghoniemy and Noha Gamal
Abstract—It was published lately in 2016 that there are approximately 3.7 million of deaths caused by communicable diseases annually. Unfortunately, currently there is no automated method for the detection and tracking of communicable diseases progression. In this paper, a framework is proposed, that is based on social network analysis, different biological sensors, and big data analytics as for predicting and analyzing communicable disease and to facilitate the process of managing, preventing and predicting risks of communicable disease progression. The proposed framework is largely based on graph theory and social network analysis algorithms to model and dynamically predict communicable disease risk for diagnosed and non-diagnosed patients. In this research, a global graph structure that maps a whole friendship network is proposed, and the suitable algorithms to identify and continuously monitor a certain communicable disease progression rate. This research can potentially be useful for forming a methodology for early intervention and prevention policies targeted at patients that can potentially divert them from the disease pathway. The interpretation and dynamic utilities offered by the framework and its predictive capability are considered a remarkable and promising broad model highlighting potential pathways linking social support, biological sensors and data sciences to physical health.

Index Terms—Social network analysis, graph theory, communicable disease progression, healthcare, big data analytics.

Samy Ghoniemy and Noha Gamal are with the British University in Egypt (e-mail: samy.ghoniemy@Bue.edu.eg, noha.gamal@acu.edu.eg).

[PDF]

Cite: Samy Ghoniemy and Noha Gamal, "A Framework for Social Network-Based Dynamic Modeling and Prediction of Communicable Diseases," International Journal of Modeling and Optimization vol. 9, no. 1, pp. 页, 2019.

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