In some areas of the United States, the spread of HIV/AIDS is increasing as a result of the opioid epidemic. However, identifying and suppressing these outbreaks is difficult—transmission between people with acute HIV infection is extraordinarily difficult to detect via routine investigations. The Microbial Transmission Network Team (MTNT), a sector of the Centers for Disease Control (CDC), is using data science to tailor prevention and intervention efforts according to the needs of a population at risk. One of the analytic tools the MTNT team used to investigate the HIV outbreak was Collaborative Advanced Analytics and Data Sharing (CAADS), managed by Leidos, which incorporates self-service data preparation technology from Trifacta, along with Alpine Data, Tableau, Arcadia Data, and Centrifuge Systems.
Ells Campbell, Connor Carreras, and Ryan Weil explain how the MTNT is leveraging new techniques in data collection, preparation, and visualization to advance the understanding of the spread of HIV/AIDS. Ells outlines the CDC’s approach to the data inference, characterization, and visualization processes that are being pioneered by MTNT. Ryan explains how the CDC has leveraged the CAADS platform including Trifacta to execute their analysis. Connor then offers a demonstration of how MTNT uses Trifacta’s data wrangling solution to perform deeper exploration and more efficient transformation of complex epidemiologic data.
This session is sponsored by Trifacta.
Ellsworth (Ells) Campbell is a health scientist in the Laboratory Branch of the Division of HIV/AIDS Prevention at the CDC. Ells began working at CDC as a PhD student and Oak Ridge Institute for Science Education (ORISE) fellow and recently transitioned to a full-time associate service fellowship. Ells holds bachelor’s and master’s degrees in biology from UC San Diego and is currently pursuing a PhD in biology at Penn State University.
Connor Carreras is Trifacta’s manager for customer success in the Americas, where she helps customers use cutting-edge data wrangling techniques in support of their big data initiatives. Connor brings her prior experience in the data integration space to help customers understand how to adopt self-service data preparation as part of an analytics process. She is a coauthor of the O’Reilly book Principles of Data Wrangling.
Ryan Weil is chief scientist in the Health Products and Solutions Group at Leidos. Ryan has nearly 20 years of experience in analytics and bioinformatics. Previously, he served as the program manager in support of the CDC Office of Infectious Disease’s bioinformatics and data analytics effort. Ryan holds a BS in microbiology from Texas A&M College Station and a PhD in molecular biophysics from UT Southwestern Medical Center in Dallas.
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