Genomics, Big Data and Data Science in Public Health

Big Data encompasses the ever increasing amounts of health-related information from disparate sources that can provide more precision by place, time, and persons than previously available. Although genomic and other molecular technologies helped launch Big Data, the field now offers emerging opportunities for public health science and practice beyond genomics, promising to enhance public health surveillance, epidemiologic investigations, and policy and program evaluations. Big Data will accelerate a shift from historical data analysis using sparse information to predictive data science that could forecast health outcomes in populations. Machine learning using algorithms to automate detection of outcomes, exposures, and susceptibility could usher a new era of precision public health. However, analyzing and integrating Big Data present unique challenges and a paradigm shift in the way public health analyses are performed. This seminar will provide an introduction to Big Data and machine learning and potential public health applications, including examples from large scale analyses using NHANES data to look at gene-environment interactions. Presenters will discuss how new data science strategies are needed to address public health challenges in the 21^st century.