利用手机数据揭示HIV的空间流行病学
Unveiling Spatial Epidemiology of HIV with Mobile Phone Data
摘要：
越来越多的地理参考移动电话数据使人们能够识别行为模式、习惯和移动。有了这些数据，我们可以提取对许多应用潜在有用的知识，包括本研究中所解决的问题-了解流行病的空间变化。我们探索了手机服务提供商收集的数据集，并将其与公开调查中估计的空间艾滋病毒流行率联系起来。为此目的，224个功能提取的流动性和连通性的痕迹，并在50个象牙海岸省的艾滋病毒流行的水平。通过回归模型，我们评估了提取的特征的预测能力。几个模型预测的艾滋病毒流行率与实际值高度相关（>0.7）。通过贡献分析，我们确定了影响感染率的关键因素。我们的研究结果表明，夜间连接和活动，用户覆盖的空间区域和整体迁移与艾滋病毒密切相关。通过可视化的通信和流动，我们努力解释流行病的空间结构。我们发现，通信和流动性方面的强有力联系和枢纽与艾滋病毒热点相一致。
英文摘要：
An increasing amount of geo-referenced mobile phone data enables the identification of behavioral patterns, habits and movements of people. With this data, we can extract the knowledge potentially useful for many applications including the one tackled in this study - understanding spatial variation of epidemics. We explored the datasets collected by a cell phone service provider and linked them to spatial HIV prevalence rates estimated from publicly available surveys. For that purpose, 224 features were extracted from mobility and connectivity traces and related to the level of HIV epidemic in 50 Ivory Coast departments. By means of regression models, we evaluated predictive ability of extracted features. Several models predicted HIV prevalence that are highly correlated (>0.7) with actual values. Through contribution analysis we identified key elements that impact the rate of infections. Our findings indicate that night connectivity and activity, spatial area covered by users and overall migrations are strongly linked to HIV. By visualizing the communication and mobility flows, we strived to explain the spatial structure of epidemics. We discovered that strong ties and hubs in communication and mobility align with HIV hot spots.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
