Co-Lead, Research Project 3, MISM
Professor in Global Health, Duke University
Charles Nunn, PhD, Project Co-Lead, is a Professor of Evolutionary Anthropology, Research Professor of Global Health, and the Gosnell Family Professor in Global Health at Duke University. He also serves as Director of the Triangle Center for Evolutionary Medicine. Dr. Nunn’s research focuses on the ecology of infectious diseases and the links among ecology, evolution, and global health. Dr. Nunn has investigated these topics using phylogenetic methods, mathematical modeling, and fieldwork worldwide to address a wide range of questions at the intersections of disease ecology, evolutionary medicine, and global health. In Madagascar, he investigates how land use change and market integration influence infectious disease dynamics in humans and other animals. This research focuses on rural areas and addresses global health challenges involving infectious diseases, food security, hypertension, and sleep. In ecological research, Dr. Nunn investigates zoonotic disease emergence in humans and the predictors of cross-species transmission. These projects involve large-scale informatics datasets, including phylogenetic comparative studies and meta-analyses. Across these research areas, Dr. Nunn takes an interdisciplinary approach to advance evolutionary perspectives on societally important questions, including One Health and pandemic prevention. His research is funded by the NIH, NSF, and other funding agencies.
Dr. Nunn’s strong interest in global health and pandemic preparedness has led to the formation of a scholarly community around Predictive Intelligence for Pandemic Prevention in Research Triangle Park, NC, funded by the NSF. He is the author of Infectious Diseases of Primates: Behavior, Ecology and Evolution and The Comparative Approach in Evolutionary Anthropology and Biology, as well as nearly 200 scientific articles and book chapters.
As Lead for MISM Research Project 3, Dr. Nunn will oversee the development of more traditional agent-based models of epidemics that incorporate immune heterogeneity among the agents.