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

2014年的埃博拉疫情是历史上规模最大的一次，影响了西非的多个国家，现在又影响了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，以防止埃博拉病毒在美国境内进一步传播。 公众缺乏对埃博拉相关风险的了解;见证了不符合CDC建议的不一致的地方应对措施（如疫苗）。 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有关的风险。 这将使那些处于风险中的人能够采取适当的行动，并通过向那些其活动没有使他们处于风险中的人保证来减少对医疗保健系统的不必要的需求。 该项目将使用从CDC和其他公共来源收集的数据来开发风险模型，并开发一个移动的应用程序，该应用程序将使用这些数据沿着用户自己的位置和活动历史，并计划向用户报告个人风险。 个人的数据永远不会离开自己的设备，确保个人隐私。 由此得出的经验教训将有助于为未来的流行病开发类似的个性化风险评估工具，从而提供埃博拉病毒流行病以外的长期利益。 第一个是集中抓取埃博拉患者的时间、地点和活动的结构化（CDC接触者追踪报告）和非结构化（社交媒体、网络博客）信息。 第二个研究挑战是患者活动建模：给定返回的信息，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的应用程序，用于跟踪移动终端用户的时间、位置和活动，并检索从公共数据开发的患者活动模型，以确定用户是否有感染风险。 这是一个复杂的问题，因为数据可能是非特异性的，需要推理技术来估计风险（例如，如果地点是体育场而不是餐馆，则与传播媒介处于同一时间/地点会造成非常不同的风险）;该项目将开发用于估计风险的活动本体。 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的研究所面临的挑战。

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.