Speaker "Nita Madhav" Details Back



Big Data and Predictive Analytics for Infectious Disease Epidemics


Around the world, governments and corporations need new and innovative strategies to mitigate and manage epidemic risk. Traditional outbreak reporting data is often incomplete and lagged, and mitigation and response strategies are often reactionary and put into place after an epidemic is identified. This session will describe several ways in which data science, artificial intelligence, and machine learning can help us improve our abilities to mitigate and manage epidemic risk. For example: - Boosted regression trees allow us to develop predictive analytics to detect outbreak emergence hotspots; - Natural language processing allows us to perform analyses of outbreak data, media sentiment data, and travel warnings; and - Large-scale simulation models can be used to perform risk analytics.


Nita Madhav is the Vice President of Data Science at Metabiota, where she oversees the teams responsible for monitoring and modeling infectious disease spread and economic impacts. Ms. Madhav has over 13 years of experience in probabilistic modeling and risk assessment. The majority of her experience has focused on developing infectious disease risk, burden, and costing models to provide actionable insights to commercial and government entities. While at Metabiota, Ms. Madhav established the modeling group and has spearheaded the team's efforts to create the comprehensive library of modeled pathogens. Before joining Metabiota, Ms. Madhav worked as a Principal Scientist at AIR Worldwide, where she led the life and health research and modeling team. Prior to that, Ms. Madhav performed hantavirus research at the Special Pathogens Branch of the US Centers for Disease Control and Prevention. Ms. Madhav holds a BS in Ecology & Evolutionary Biology, with distinction, from Yale University and an MSPH in Epidemiology from the Rollins School of Public Health at Emory University.