Department of Quantitative Health Sciences
Digital Communications and Networking | Epidemiology | Health Information Technology | Public Health | Systems and Communications | Theory and Algorithms
Increasing population density, closer social contact, and interactions make epidemic control difficult. Traditional offline epidemic control methods (e.g., using medical survey or medical records) or model-based approach are not effective due to its inability to gather health data and social contact information simultaneously or impractical statistical assumption about the dynamics of social contact networks, respectively. In addition, it is challenging to find optimal sets of people to be isolated to contain the spread of epidemics for large populations due to high computational complexity. Unlike these approaches, in this paper, a novel cluster-based epidemic control scheme is proposed based on Smartphonebased body area networks. The proposed scheme divides the populations into multiple clusters based on their physical location and social contact information. The proposed control schemes are applied within the cluster or between clusters. Further, we develop a computational efficient approach called UGP to enable an effective cluster-based quarantine strategy using graph theory for large scale networks (i.e., populations). The effectiveness of the proposed methods is demonstrated through both simulations and experiments on real social contact networks.
IEEE Transactions on Parallel and Distributed Systems
Zhang, Zhaoyang; Wang, Honggang; Wang, Chonggang; and Fang, Hua (Julia), "Cluster-based Epidemic Control Through Smartphone-based Body Area Networks" (2015). Quantitative Health Sciences Publications and Presentations. 1127.