RAPID: III: Data Collection and Risk Evaluation Learning in Identifying High Risk Ebola Subpopulations for the Intervention and Prevention of Large-scale Ebola Virus Spreading

RAPID：III：识别高风险埃博拉亚群的数据收集和风险评估学习，以干预和预防大规模埃博拉病毒传播

• 批准号: 1513324
• 负责人: Fengjun Li
• 金额: $ 18.87万
• 依托单位: University of Kansas Center for Research Inc
• 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 2016-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1513324&HistoricalAwards=false
• 关键词: RAPID; III; Data; Collection; Risk

The 2014 Ebola epidemic is the largest in history, affecting multiple countries in West Africa, and now impacting the US and other countries worldwide. The US Center for Disease Control and Prevention (CDC) and partners are taking precautions to prevent the further spread of Ebola within the United States. There is a lack of public understanding of the risks associated with Ebola; witness the inconsistently applied local responses (such as quarantines) that do not match CDC recommendations. This project will develop technology to enable individuals to evaluate risks associated with their own past and planned activities and travel. This will both enable those at risk to take appropriate action, and reduce unwarranted demand on the healthcare system by reassuring those whose activities have not placed them at risk. This project will use data gathered from the CDC and other public sources to develop risk models, and develop a mobile app that will use this data along with the user's own location and activity history and plans to report individual risk to the user. An individual's data never leaves their own device, ensuring personal privacy. The resulting lessons learned will ease the process of developing similar individualized risk assessment tools for future epidemics, providing long-term benefits beyond the Ebola virus epidemic.The research will address three main issues. The first is focused crawling of structured (CDC Contact Tracing reports) and unstructured (social media, web blogs) information on time, location, and activities of Ebola patients. A second research challenge is patient activity modeling: Given the returned information, developing a time/space/activity model determining the risk of the patient acting as a transmission agent. Finally, the project will develop a mobile app that tracks time, location, and activities of the mobile device user, and retrieves the patient activity models developed from public data to determine if the user is at risk of infection. This is a complex problem, as the data may be non-specific and require inferential techniques to estimate risk (e.g., being in the same time/location as a transmission agent poses very different risk if the location is a sports stadium as opposed to a restaurant); the project will develop ontologies for activities to use in estimating risk. The project will use expert opinion to seed regression models for risk assessment. Lessons learned from this project will also identify challenges for future research in information integration, risk analysis, machine learning, and privacy preserving technologies.

2014年的埃博拉疫情是历史上规模最大的一次，影响了西非多个国家，目前正在影响美国和世界其他国家。美国疾病控制和预防中心（CDC）及其合作伙伴正在采取预防措施，防止埃博拉病毒在美国进一步传播。公众对与埃博拉相关的风险缺乏了解；目睹不符合CDC建议的不一致应用的本地响应（例如隔离）。该项目将开发技术，使个人能够评估与自己过去和计划的活动和旅行相关的风险。这将使那些有风险的人能够采取适当的行动，并通过让那些活动没有使他们处于危险中的人放心，减少对医疗保健系统的不必要需求。该项目将使用从疾病预防控制中心和其他公共来源收集的数据来开发风险模型，并开发一个移动应用程序，该应用程序将使用这些数据以及用户自己的位置、活动历史和计划，向用户报告个人风险。个人数据永远不会离开自己的设备，确保个人隐私。由此获得的经验教训将有助于为今后的流行病制定类似的个性化风险评估工具，提供超越埃博拉病毒流行的长期效益。这项研究将解决三个主要问题。第一种是集中抓取结构化（CDC接触者追踪报告）和非结构化（社交媒体、网络博客）埃博拉患者的时间、地点和活动信息。第二个研究挑战是患者活动建模：根据返回的信息，开发一个时间/空间/活动模型，确定患者作为传播媒介的风险。最后，该项目将开发一个移动应用程序，跟踪移动设备用户的时间、地点和活动，并检索从公共数据中开发的患者活动模型，以确定用户是否有感染的风险。这是一个复杂的问题，因为数据可能是非特异性的，需要推断技术来估计风险（例如，与传播媒介在同一时间/地点所造成的风险非常不同，如果地点是体育场，而不是餐馆）；该项目将开发用于评估风险的活动本体。该项目将利用专家意见为风险评估建立回归模型。从该项目中吸取的经验教训还将确定未来在信息集成、风险分析、机器学习和隐私保护技术方面研究的挑战。