DIGITAL HEALTH SOLUTIONS FOR COVID-19: COVID-19 ONGOING MONITORING (COMMUNITY)

本课题的目标是开发一种基于自述调查数据和可穿戴传感器数据的COVID-19检测算法。来自25K COVID-19体验参与者和25K大规模流感监测（2020年3月新增的COVID-19问题）的数据将与现有的机器学习模型一起使用，以开发这种新的检测算法，该算法将在大规模试点人群中进行验证，以识别未确诊的COVID-19个体。Evidation将把该模型整合到一个名为Achieve的已建立的网络和多平台（Android、iOS）智能手机平台中，该平台允许用户分享日常生活中个人生成的健康数据（PGD）。本项目收集的数据将被识别并安全地传输到NIH数据中心。

The goal of this proposal is to develop a COVID-19 detection algorithm based on self-report survey data and wearable sensor data. Data from 25K COVID-19 Experiences participants and 25K Large-scale Flu Surveillance (COVID-19 Questions added March 2020) will be used with an existing machine learning model to develop this new detection algorithm, which will be validated in a large-scale pilot population to identify individuals with undiagnosed COVID-19. Evidation will incorporate the model into an established web and multi-platform (Android, iOS) smartphone platform called Achieve which allows users to share person-generated health data (PGD) from their everyday lives. Data collected under this project will be deidentified and securely transmitted to an NIH data hub.