Michael E. Emch
Ph.D., W.R. Kenan, Jr. Distinguished Professor, Geography, Epidemiology
emch@unc.edu
Curriculum Vitae
Google Scholar Profile
PubMed Publications
CPC Publications
ORCID iD
Michael Emch’s expertise is in spatial epidemiology, disease ecology, and geographic information science applications of public health mostly on infectious diseases. He studies spatial patterns of diseases by investigating the role of social, natural, and built environments in disease occurrence in different places and populations, leading efforts to incorporate spatial sciences into different areas of inquiry in the health sciences.
Emch's expertise is in infectious disease ecology, neighborhood determinants of health, and geographic information science applications of public health. He leads the Spatial Health Research Group which conducts research that explores spatio-temporal patterns of disease, primarily infectious diseases of the developing world. Disease patterns are studied using a holistic approach by investigating the role of natural, social, and built environments in disease occurrence in different places and populations. Diverse statistical and spatial analytical methods are informed by theory from the fields of medical geography, epidemiology, ecology, and others. These theories and methods are used to examine diverse topics such as the role of population-environment drivers in viral evolution, how social connectivity contributes to disease incidence, and using environmental indicators to predict disease outbreaks. Two present studies the Group is working on now include: (1) Comparative Evolution and Ecology of Swine Influenza Viruses in China and the United States and (2) Impacts of Environment, Host Genetics and Antigen Diversity on Malaria Vaccine Efficacy . For more information about these studies and other Group research activities see the Spatial Health Research Group website at Spatial Health Research Group
Associated Projects
- Impacts of Environment, Host Genetics and Antigen Diversity on Malaria Vaccine Efficacy
- Modeling the Pandemic Lifecycle for Disease Control