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.
