RAPID: Near Real-Time Quantifiable Social Media Data for Improved Modeling, Tracking and Mitigating the Spread of the Ebola Virus

RAPID：近乎实时的可量化社交媒体数据，用于改进建模、跟踪和减轻埃博拉病毒的传播

• 批准号: 1508613
• 负责人: Milton Halem
• 金额: $ 10万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2017-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1508613&HistoricalAwards=false
• 关键词: RAPID; Near; Real; Time; Quantifiable

The research enables the development of new quantitative crisis maps to approximate local regional rates of infectious Ebola virus spreading in near real time thereby enhancing the effective distribution of limited availability of recently developed vaccines to mitigate the spread of Ebola. It will also provide a mechanism for validating and improving operational forecast models of the CDC by comparisons with regional social media observations. It may allow for disease surveillance and early detection among third-world countries, which will in turn have impacts globally as evidenced by the rapid global spread of the recent Ebola outbreak. The work should be generalizable to other infectious virus conditions. Furthermore the research will be capable of exploiting social media health data from future hand held devices.More specifically, this project is developing methodology for utilization and augmentation of quantifiable social media data from Twitter into epidemiological infectious virus spread model, with the aim to validate the model forecasts and provide additional channels of suspected EVD cases not available through traditional sources. The work is based on a need to collect real-time quantitative media data related to Ebola-exposed regional populations in the three sub-Saharan African countries. Although publicly available partial data exists on Internet sites, the complete source of public data is available only by purchase from organizations that manage and control such sites as Twitter and Instagram. The developed methods would contribute in evaluating the effectiveness of model forecast based on the social media data for future improvements in the prediction of infectious disease spread and prevention.This work, coordinated with and submitted in parallel with respective RAPID proposals, in coalition with researchers at FAU, FIU, and UMN, aims to jointly address public health problems of national and global significance, particularly the problems of big data for mitigation of public health emergencies. Specifically, the proposers will work with FAU and FIU on modeling Ebola spread through use of innovative big data analytics techniques, integrating quantifiable social media data from Twitter. Beyond the present issue of Ebola, the proposed methodology and the coalition-building effort aims to enable support solutions in a wide range of public health issues.

这项研究使新的定量危机地图的发展，以近似当地的传染性埃博拉病毒的传播速度在真实的时间，从而加强有效分配有限的最近开发的疫苗，以减轻埃博拉病毒的传播。它还将提供一种机制，通过与区域社交媒体观测结果进行比较，验证和改进CDC的业务预测模型。它可以使第三世界国家进行疾病监测和早期发现，这反过来又会对全球产生影响，最近埃博拉疫情在全球迅速蔓延就是证明。这项工作应推广到其他感染性病毒的条件。此外，该研究将能够利用未来手持设备的社交媒体健康数据，更具体地说，该项目正在开发方法，以利用和增强来自Twitter的可量化社交媒体数据到流行病学传染性病毒传播模型，旨在验证模型预测，并提供通过传统来源无法获得的疑似EVD病例的额外渠道。这项工作的基础是需要收集与三个撒哈拉以南非洲国家的埃博拉暴露区域人口有关的实时定量媒体数据。虽然互联网网站上存在公开的部分数据，但只有从管理和控制Twitter和Instagram等网站的组织购买才能获得完整的公共数据来源。所开发的方法将有助于评估基于社交媒体数据的模型预测的有效性，以进一步改进传染病传播和预防的预测。这项工作与FAU，FIU和UMN的研究人员合作，与各自的RAPID提案协调并同时提交，旨在共同解决国家和全球重要的公共卫生问题，特别是大数据在缓解突发公共卫生事件方面的问题。具体而言，提案者将与FAU和FIU合作，通过使用创新的大数据分析技术，整合Twitter的可量化社交媒体数据，对埃博拉病毒的传播进行建模。除了目前的埃博拉问题外，拟议的方法和建立联盟的努力旨在支持解决广泛的公共卫生问题。