Epihack Analytics: A Collaborative Approach for Evaluation & Adoption of Novel Data Sources for Disease Surveillance

Tuesday, June 21, 2016: 4:06 PM
Tubughnenq' 3, Dena'ina Convention Center
Adam Wade Crawley , Skoll Global Threats Fund, San Francisco, CA
Samuel V Scarpino , Santa Fe Institute, Santa Fe, NM
Key Objectives: EpiHack Analytics is a collaborative, cross-sectoral approach to evaluation and integration of novel data sources for disease surveillance. The objectives of this session are to (1) describe the EpiHack Analytics workshop approach, (2) share outcomes from the September 2015 EpiHack Analytics session focused on influenza surveillance, and (3) obtain feedback to improve the process and ensure relevancy for the applied epidemiology sector in the United States. 

Brief Summary:In September 2015 the Skoll Global Threats Fund (SGTF), in partnership the Food Protection and Defense Institute at the University of Minnesota and Dr. Samuel Scarpino of the Santa Fe Institute, convened an EpiHack Analytics (EA) workshop. The 30 attendees included state and local epidemiologists, the CDC, research universities, and the private sector. The two-day event aimed to enable a sample of U.S. health departments to acquire, analyze, and interpret data from the Flu Near You (FNY) participatory surveillance system to complement existing influenza surveillance efforts. Specifically, we focused on constructing open-source data integration and visualization tools using a variety of software environments.

The EA approach evolved from a series of EpiHacks (epidemiology hack-a-thons) supported by SGTF in Cambodia, Thailand, Tanzania, and Brazil from 2013 through 2015. These events convened software developers and health officials to design and prototype data collection tools that addressed gaps in disease surveillance. As these efforts have spawned successful data capture systems, we have begun to address the question of how to analyze and interpret a novel data source for routine disease surveillance.

The EA approach endeavors to create a collaborative atmosphere that provides intellectual space for determining how to incorporate a new dataset into an epidemiologists’ workflow and ensure it’s effective use for public health decision-making. Given the limited bandwidth of most health agencies, an EA approach can result in beneficial outputs that include visualization prototypes, analytic code, and guidance on benefits and limitations of new data sources. In this round table, we will present lessons learned from both the 2015 workshop and from epidemiologists who used FNY data during the 2015-16 influenza season. From this discussion, we hope to gain valuable insight from the audience members on how to scale-up this model to a larger number of public health settings.