About this Class

Traditional methods of epidemic modeling continue to be used fruitfully for characterizing outbreaks and predicting the spread of disease in populations. However, these methods, typically rely on what are known as “compartment models”, requiring assumptions that are not necessarily sensitive to the ever-changing environmental, behavioral, temporospatial, and social phenomena that influence disease spread. Compartment models can be enriched by the judicious use of robust methods drawn from the field of artificial intelligence that allow us to model more accurately and more quickly the population and disease dynamics that are central to developing policies for prevention, detection, and treatment. We will explore these approaches, including some that are currently in use as well as a proposal for novel, next-generation machine learning tools for epidemiologic investigation. John H. Holmes, PhD, is Professor of Medical Informatics in Epidemiology at the University of Pennsylvania Perelman School of Medicine. He is the Associate Director of the Penn Institute for Biomedical Informatics and is Past-Chair of the Graduate Group in Epidemiology and Biostatistics. Dr. Holmes has been recognized nationally and internationally for his work on developing and applying new artificial intelligence approaches to mining epidemiologic surveillance data. Dr. Holmes’ research interests are focused on the intersection of medical informatics and clinical research, specifically evolutionary computation and machine learning approaches to knowledge discovery in clinical databases, deep electronic phenotyping, interoperable information systems infrastructures for epidemiologic surveillance, and their application to a broad array of clinical domains, including cardiology and pulmonary medicine. He has served as the co-lead of the Governance Core for the SPAN project, a scalable distributed research network, and participates in the FDA Sentinel Initiative.