Cluster-based Epidemic Control Through Smartphone-based Body Area Networks.
Cluster-based Epidemic Control Through Smartphone-based Body Area Networks.
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DOI：
10.1109/tpds.2014.2313331
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发表时间：
2015-02-09
期刊：
影响因子：
--
通讯作者：
Fang H
中科院分区：
文献类型：
--
作者：
Zhang Z;Wang H;Wang C;Fang H
人口密度增加，社会接触和互动更加密切，使疫情控制变得困难。传统的离线流行病控制方法（例如，使用医疗调查或医疗记录）或基于模型的方法是无效的，这分别是由于其不能同时收集健康数据和社会联系信息或者关于社会联系网络的动态的不切实际的统计假设。此外，由于高计算复杂性，找到待隔离的最佳人群集合以遏制大人群的流行病传播是具有挑战性的。与这些方法不同的是，本文提出了一种新的基于智能手机的体域网基于集群的流行病控制方案。该方案将人口划分为多个集群的基础上，他们的物理位置和社会联系信息。所提出的控制方案适用于集群内或集群之间。此外，我们开发了一种称为UGP的计算高效方法，以使用图论为大规模网络（即，人口）。通过仿真和真实的社会联系网络的实验，证明了所提出的方法的有效性。
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 quarantined 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 Smartphone-based 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.