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

这项研究使得能够编制新的量化危机图，以接近实时地估计传染性埃博拉病毒在当地区域的传播率，从而加强对最近开发的有限疫苗的有效分配，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察进行比较，验证和改进疾控中心的业务预测模型。它可能允许第三世界国家进行疾病监测和早期发现，这反过来将对全球产生影响，最近埃博拉疫情在全球的快速传播就是明证。这项工作应该可以推广到其他传染性病毒情况。此外，这项研究将能够利用来自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，将来自Twitter的可量化的社交媒体数据用于流行病学传染病病毒传播模型，目的是验证模型预测，并提供传统来源无法获得的疑似埃博拉病例的额外渠道。这项工作是基于收集与撒哈拉以南非洲三个国家感染埃博拉病毒的区域人口有关的实时量化媒体数据的需要。虽然因特网网站上存在公开提供的部分数据，但只有从管理和控制这些网站的组织购买，才能获得公共数据的全部来源，如Twitter和Instagram。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，以期在未来改进传染病传播和预防的预测和预防。这项工作与各自的快速提案协调并与各自的快速提案并行提交，与FAU、FIU和UMN的研究人员合作，旨在共同解决具有国家和全球意义的公共卫生问题，特别是缓解突发公共卫生事件的大数据问题。具体地说，提出者将与FAU和FIU合作，通过使用创新的大数据分析技术，整合来自Twitter的可量化社交媒体数据，对埃博拉传播进行建模。除了目前的埃博拉问题，拟议的方法和联盟建设努力的目的是在广泛的公共卫生问题上提供支持解决方案。

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.