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

在西非国家，手机普及率从40%到80%不等，仅在塞拉利昂，几内亚和利比里亚就有超过200万台Android设备。研究人员建议提供一种智能手机应用程序，通过监测平民的位置模式、习惯（如走路、睡觉）和资源需求（如洗手液或手套），改善就医、流行病学和预防。该项目解决了三个关键问题。首先，与随机流行病学模型相比，智能手机传感器能在多大程度上支持实时监测和预测传染病（在这种情况下是埃博拉病毒）的规模？第二，如何使用智能手机通信来教育人群预防行为并改变这些行为？第三，移动保健（移动的保健）在西非是否可持续，或者基础设施是否太不发达，无法长期支持这种创新？我们的项目是新颖的，因为它使用被动获得的位置信息，传感器数据和动态调查，以了解健康和潜在的感染人口在真实的时间，以补充疾病预防控制中心，世界卫生组织和联合国的努力。这个快速提出寻求使用机器学习来分类从手机传感器获得的数据（例如，在发病率高的村庄中的位置、减少的运动、皮疹的自拍），沿着调查问题，以确定用户是否出现可能与埃博拉相似的症状。该项目还将研究与随机流行病学模型相比的人类流动模式，以确定实时手机跟踪的有效性。这些传感器模态的耦合将用于将用户建模为噪声传感器，并比较该信息如何同意或预测历史趋势。最后，该项目将使用行为科学的功能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。

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.