PocketCare：使用部分接近度和症状观察值跟踪手机流感
PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms
摘要：
移动电话提供了一个强大的传感平台，研究人员可以采用它来了解人与人之间的接近互动以及通过这些互动传播疾病，行为和意见。然而，它仍然是一个挑战，跟踪整个社区的基于邻近的互动，然后从一小部分志愿者群体的观察开始，对疾病和行为的社会传播进行建模。在本文中，我们提出了一种新的方法，试图连接在一起，这些稀疏的意见，使用一个模型的个人如何相互作用，以及如何发生社会互动的一系列接近的相互作用。我们应用我们的方法来跟踪流感在3000人的大学校园的空间邻近网络中的传播，动员300名志愿者通过蓝牙扫描监控附近的手机，并每天报告流感症状。我们的目标是根据一个人的日常生活与志愿者的日常生活交叉的频率来预测他/她患流感的可能性。因此，我们使用志愿者的日常工作来建立志愿者和非志愿者的模型。我们的研究结果表明，我们可以提前两周预测流感感染，根据信息量的不同，平均精度从0.24到0.35不等。这种精度比随机猜测模型高6到9倍。在人口水平上，我们可以预测两周内的传染性人口，r平方值为0.95（随机猜测模型的r平方值为0.2）。这些结果指出了一种创新的方法，用于跟踪与出现症状的人互动的个人，使我们能够警告那些有感染危险的人，并告知健康研究人员接触性疾病的进展。
英文摘要：
Mobile phones provide a powerful sensing platform that researchers may adopt to understand proximity interactions among people and the diffusion, through these interactions, of diseases, behaviors, and opinions. However, it remains a challenge to track the proximity-based interactions of a whole community and then model the social diffusion of diseases and behaviors starting from the observations of a small fraction of the volunteer population. In this paper, we propose a novel approach that tries to connect together these sparse observations using a model of how individuals interact with each other and how social interactions happen in terms of a sequence of proximity interactions. We apply our approach to track the spreading of flu in the spatial-proximity network of a 3000-people university campus by mobilizing 300 volunteers from this population to monitor nearby mobile phones through Bluetooth scanning and to daily report flu symptoms about and around them. Our aim is to predict the likelihood for an individual to get flu based on how often her/his daily routine intersects with those of the volunteers. Thus, we use the daily routines of the volunteers to build a model of the volunteers as well as of the non-volunteers. Our results show that we can predict flu infection two weeks ahead of time with an average precision from 0.24 to 0.35 depending on the amount of information. This precision is six to nine times higher than with a random guess model. At the population level, we can predict infectious population in a two-week window with an r-squared value of 0.95 (a random-guess model obtains an r-squared value of 0.2). These results point to an innovative approach for tracking individuals who have interacted with people showing symptoms, allowing us to warn those in danger of infection and to inform health researchers about the progression of contact-induced diseases.
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。
