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

University Of Maryland Baltimore County, Baltimore MD

Investigators

Abstract

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.