Mobile Phone Enabled Social Community Extraction for Controlling of Disease Propagation in Healthcare

New mobile phones equipped with multiple sensors provide users with the ability to sense the world at a microscopic level. The collected mobile sensing data can be comprehensive enough to be mined not only for the understanding of human behaviors but also for supporting multiple applications ranging from monitoring/tracking, to medical, emergency and military applications. In this work, we investigate the feasibility and effectiveness of using human contact traces collected from mobile phones to derive social community information to control the disease propagation rate in the healthcare domain. Specifically, we design a community-based framework that extracts the dynamic social community information from human contact based traces to make decisions on who will receive disease alert messages and take vaccination. We have experimentally evaluated our framework via a trace-driven approach by using data sets collected from mobile phones. The results confirmed that our approach of utilizing mobile phone enabled dynamic community information is more effective than existing methods, without utilizing social community information or merely using static community information, at reducing the propagation rate of an infectious disease. This strongly indicates the feasibility of exploiting the social community information derived from mobile sensing data for supporting healthcare related applications.