RAPID: Socio-technical systems and Big Data Analytics in the Ebola Response

In response to the 2014 Ebola virus outbreak in western Africa, dozens of humanitarian relief organizations as well as the CDC and U.S. military are providing medical assistance or logistics support to the relief effort. At the same time, a diverse range of volunteer technical communities (VTCs) and academics, as well as the humanitarian relief organizations themselves, are attempting to make use of 'big data' to improve the response. These big data analyses are based on diverse data from diverse sources, including call records from mobile phone companies, health worker inventories from ministries, and daily case-data reports aggregated from multiple organizations. The analyses have generated outputs such as visually appealing maps and predictions of outbreak trajectories. However, precisely how, when and where these analyses can be used effectively by response organizations are still open questions. The project will develop knowledge to guide response organizations interested in leveraging existing and emerging big data from a variety of sources (response organizations, firms, government, individuals), which in turn may improve the speed, quality, and efficiency of crisis response.

The research team of computer and social scientists will partner with a consultant with expertise in crisis information management deployed in the Ebola response. They will examine both organizational and technical dimensions of the use of big data analytics in the Ebola response organizations, carrying out a series of interviews to investigate how and where data is used (field, headquarters, or both) and the work involved to make big data analyses usable in the decision making of response organizations. The results will inform the development of a socio-technical systems framework to explain what makes big data analyses useable. The social dimensions of the framework will include the response context as well as decision making processes. The technical dimensions will include data availability, data analyses and output formats. In the process of developing this socio-technical framework, this research will identify mechanisms for matching organizational needs with big data analyses. More importantly, however, by identifying these mechanisms, the research will shed light on the fundamental roles of multi-level governance and articulation work in making effective use of big data analyses. The multi-level approach helps explain and predict the location in an organization?s hierarchy of technical and decision making expertise. Within these levels, a focus on articulation work helps specify the necessary tasks for using data in a highly dynamic environment. The project will extend the scholarship in the crisis informatics sub-field of big data beyond its current focus on social media data and provide clarification of 'last mile' issues in big data aimed at ensuring the usefulness of output.