Visual analytics of geo-social interaction patterns for epidemic control.
Visual analytics of geo-social interaction patterns for epidemic control.
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DOI：
10.1186/s12942-016-0059-3
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发表时间：
2016-08-10
影响因子：
4.9
通讯作者：
Luo W
中科院分区：
文献类型：
--
作者：
Luo W
人类相互作用和人口流动决定了空气传播疾病传播的时空过程。这项研究将这种传播视为地理-社会互动问题，因为人口流动将病毒传播所在的地理位置上的不同人群联系在一起。以前的研究认为，根据人口流动数据确定的地理-社会互动模式可以为设计有效的大流行缓解提供巨大潜力。然而，很少有人研究考虑到地理-社会相互作用模式的控制策略设计的有效性。为了弥补这一差距，本研究提出了有效疾病控制的新框架；具体地说，该框架提出疾病控制战略应从识别地理-社会互动模式开始，相应地设计有效的控制措施，并评估不同控制措施的效果。该框架被用来构建一个新的可视化分析工具，该工具由三部分组成：地缘社会混合模式的可重排序矩阵、基于代理的流行病模型和组合可视化方法。以法国一所小学的真实人际互动数据作为概念验证，本研究比较了空间-社会互动模式和整个地区疫苗接种策略的有效性。模拟结果表明，局部靶向接种有可能将感染人数控制在较小范围内，并防止传播到其他地区。在很小的概率下，局部控制策略将失效；在这种情况下，将需要其他控制策略。这项研究进一步探索了不同的空间-社会尺度对当地疫苗接种策略成功的影响。结果表明，适当的空间-社会尺度有助于在有限的疫苗数量下达到最佳的防治效果。该案例研究显示了GS-EpiViz如何支持空气传播疾病(例如流感)的先进控制方案的设计和测试。通过探索人类互动数据确定的地理-社会模式可以帮助针对关键个人、地点和地点集群进行疾病控制。不同的空间-社会尺度可以帮助在地理和社会上优先考虑有限的资源(例如疫苗)。
Human interaction and population mobility determine the spatio-temporal course of the spread of an airborne disease. This research views such spreads as geo-social interaction problems, because population mobility connects different groups of people over geographical locations via which the viruses transmit. Previous research argued that geo-social interaction patterns identified from population movement data can provide great potential in designing effective pandemic mitigation. However, little work has been done to examine the effectiveness of designing control strategies taking into account geo-social interaction patterns. To address this gap, this research proposes a new framework for effective disease control; specifically this framework proposes that disease control strategies should start from identifying geo-social interaction patterns, designing effective control measures accordingly, and evaluating the efficacy of different control measures. This framework is used to structure design of a new visual analytic tool that consists of three components: a reorderable matrix for geo-social mixing patterns, agent-based epidemic models, and combined visualization methods. With real world human interaction data in a French primary school as a proof of concept, this research compares the efficacy of vaccination strategies between the spatial–social interaction patterns and the whole areas. The simulation results show that locally targeted vaccination has the potential to keep infection to a small number and prevent spread to other regions. At some small probability, the local control strategies will fail; in these cases other control strategies will be needed. This research further explores the impact of varying spatial–social scales on the success of local vaccination strategies. The results show that a proper spatial–social scale can help achieve the best control efficacy with a limited number of vaccines. The case study shows how GS-EpiViz does support the design and testing of advanced control scenarios in airborne disease (e.g., influenza). The geo-social patterns identified through exploring human interaction data can help target critical individuals, locations, and clusters of locations for disease control purposes. The varying spatial–social scales can help geographically and socially prioritize limited resources (e.g., vaccines).