Anne G Hoen, PhD
Associate Professor of Epidemiology
Associate Professor of Biomedical Data Science
Associate Professor of Microbiology and Immunology
Biomedical Data Science
Microbiology and Immunology
Molecular and Cellular Biology Graduate Programs
Quantitative Biomedical Sciences
Dartmouth-Hitchcock Medical Center
One Medical Center Drive
Lebanon NH 03756
Dr. Hoen's research focus is on the development of the microbiome in infants and children, and the associations between environmental and dietary exposures, the microbiome, and risk for infectious and other diseases. She has a broad interest in the predictors of disease risk and microbial colonization dynamics in human populations and the environment as reflected in microbial genetic sequence variation and health informatics streams over space and time. Interdisciplinary approaches used in her research include spatial and time-series statistics, population genetics and novel disease surveillance methodologies along with tools from the fields of microbial ecology, molecular epidemiology and landscape epidemiology.
Rotations and Thesis Projects:
Development of novel computational methods for studying the human microbiota
Investigating the environmental drivers of infant intestinal microbiome development
Associations between patterns of infant intestinal microbiome establishment and health outcomes in children
Functional characterization of the developing infant gut microbiota using metabolomics
K01LM011985: Bioinformatics strategies for early life microbiomics
R01LM012723: Multi-omic functional integration using networks
QBS 136 Applied Epidemiologic Methods I
QBS 137 Applied Epidemiologic Methods II
Dr. Hoen completed her PhD in epidemiology and public health at Yale University and received post-doctoral training at Children's Hospital Boston, Harvard Medical School and McGill University. She has broad training in biostatistics, epidemiology and public health with research experience in the evaluation of interventions, understanding infectious disease emergence and spread, and evaluating associations between the microbiome and human health. Her research uses advanced statistical methods as well as informatics and complex systems approaches. Past studies include modeling the timing and spread of influenza to estimate the effects of interventions targeting children, modeling the evolutionary ecology of the Lyme disease agent, Borrelia burgdorferi and characterizing the initial development of the human microbiome in early life and its associations with common exposures and infectious outcomes.
Information enhanced model selection for Gaussian graphical model with application to metabolomic data.
Retail store customer flow and COVID-19 transmission.
Prediction of an outcome using NETwork Clusters (NET-C).
Identification of microbial interaction network: zero-inflated latent Ising model based approach.
Reliability of stool microbiome methods for DNA yields and sequencing among infants and young children.
Maternal determinants of infant immunity: Implications for effective immunization and maternal-child health.
Microbial Communities in Human Milk Relate to Measures of Maternal Weight.
An Integrated Gaussian Graphical Model to evaluate the impact of exposures on metabolic networks.
Specific class of intrapartum antibiotics relates to maturation of the infant gut microbiota: a prospective cohort study.
Conditional Regression Based on a Multivariate Zero-Inflated Logistic-Normal Model for Microbiome Relative Abundance Data.