RAPID: SCH: NODE: A Real-Time Smartphone Epidemiological Tool

RAPID：SCH：NODE：实时智能手机流行病学工具

• 批准号: 1516340
• 负责人: Henry Kautz
• 金额: $ 13.25万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-12-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1516340&HistoricalAwards=false
• 关键词: RAPID; SCH; NODE; Real; Time

In West African countries, cell phone penetration can range from 40 to 80% and upwards, with over 2 million Android devices in Sierra Leone, Guinea, and Liberia alone. The investigators propose the delivery of a smartphone application that will improve care-seeking, epidemiology, and prevention by monitoring civilian location patterns, habits (e.g. walking, sleeping), and resource needs (e.g. hand sanitizers or gloves). This project addresses three key problems. First, how well can smartphones sensors support real-time monitoring and prediction of the scale of an infectious disease (in this case Ebola) compared to stochastic epidemiology models? Second, how can smartphone communications be used to educate a population about prophylactic behaviors and change such behaviors? Third, is mHealth (mobile health) sustainable in West Africa, or is the infrastructure too underdeveloped to support such innovations over the long term? Our project is novel in that it uses passively obtained location information, sensor data, and dynamic surveys to understand the health and potential for infection among the population in real time to supplement CDC, WHO and UN efforts.This RAPID proposes to seek to use machine learning to classify data obtained from phone sensors (e.g. location in a village with high disease rate, reduced movement, self-photo of rashes) along with survey questions to determine if users are developing symptoms that may be similar to Ebola. The project will also look at human mobility patterns compared to stochastic epidemiological models to determine the efficacy of real-time cell phone tracking. The coupling of these sensor modalities will be used to model users as a noisy sensor and compare how this information agrees or predicts historical trends. Finally, the project will use features of behavioral science to explore what feedback does to affect user behavior, such as, knowing about their risk for Ebola.

在西非国家，手机普及率从40%到80%甚至更高，仅在塞拉利昂、几内亚和利比里亚就有200多万台Android设备。调查人员建议交付一款智能手机应用程序，通过监控平民的位置模式、习惯(如走路、睡觉)和资源需求(如洗手液或手套)，改善求医、流行病学和预防。这个项目解决了三个关键问题。首先，与随机流行病学模型相比，智能手机传感器对传染病(在这种情况下是埃博拉病毒)规模的实时监测和预测能力如何？其次，如何利用智能手机通信来教育民众有关预防行为的知识并改变这种行为？第三，移动健康(移动医疗)在西非是可持续的，还是基础设施太不发达，无法长期支持这种创新？我们的项目很新颖，因为它使用被动获得的位置信息、传感器数据和动态调查来实时了解人口中的健康和感染潜力，以补充疾控中心、世卫组织和联合国的努力。这一快速建议寻求使用机器学习来分类从电话传感器获得的数据(例如，在发病率较高的村庄的位置、减少的活动、皮疹自拍)以及调查问题，以确定用户是否出现了可能类似埃博拉病毒的症状。该项目还将与随机流行病学模型进行比较，研究人类的流动模式，以确定实时手机跟踪的效果。这些传感器模式的耦合将被用来将用户建模为噪声传感器，并比较这些信息如何符合或预测历史趋势。最后，该项目将使用行为科学的特征来探索反馈对用户行为的影响，例如，了解他们感染埃博拉病毒的风险。