Elena N. Naumova
- Chair of the Division of Nutrition Data Sciences
- Adjunct Professor, Department of Civil and Environmental Engineering, Tufts University School of Engineering
- Adjunct Professor, Department of Public Health & Family Medicine, Tufts University School of Medicine
- Adjunct Professor, Department of Mathematics, Tufts University School of Arts and Sciences
- Adjunct Professor, Department of Gastrointestinal Sciences, Christian Medical College, Vellore, India
Elena N. Naumova is the Chair of the Division of Nutrition Data Science, as well as a Professor at the Friedman School. Her research activities span a broad range of research programs in emerging and re-emerging diseases, environmental epidemiology, molecular biology, nutrition, and growth. Her primary expertise is in development of analytical tools for spatio-temporal and longitudinal data analysis applied to disease surveillance, exposure assessment, and studies of growth; creation and application of statistical tools to evaluate the influence of an extreme and/or intermediate event on spatial and temporal patterns.
Dr. Naumova participates in international projects collaborating with epidemiologists, immunologists, and public health professionals in India, Kenya, Ghana, Ecuador, Japan, Canada, UK, and Russia. She applies theoretical work to studies of infections sensitive to climate variations and extreme weather events and facilitates utilization of novel data sources, including remote sensing data and satellite imagery for better understanding the nature and etiology of diseases on local and global scales. She is involved in a number of observational studies, meta-analyses, and clinical trials with complex schemes of recruitments, including birth cohorts studies with staggered enrollment and randomization at a household level. She utilizes multi-sourced environmetal databases, climate data repositories, and vital and hospitalization records, including Centers for Medicare and Medicaid Services and U.S. Census databases.
Naumova serves on research review panels and editorial boards of scientific journals with the goal to shape and implement institutional policies on data sharing and management, data quality assurance and information security. As a Director of the NIH-sponsored Tufts Initiative for Forecasting and Modeling of Infectious Diseases (InForMID), she has set up workshops and training programs to support field research and analytical assessment of research data, advisied over 60 PhD/MS/MPH students at Tufts and co-directed the Tufts Institute of the Environment, an outstanding supporter for research projects for Tufts graduate students.
- Ph.D., 1988, Applied Mathematics/Statistics, Novosibirsk State Technical University
- M.S., 1982, Statistics, Novosibirsk State Technical University
Precision Dynamic Mapping. We design animated maps that compress decades of health records and terabites of environmental data into short movies to allow detection of trends and persistent clusters on a national scale. We are developing visualization tools and approaches to help researchers to depict complex data. Combined with the dynamic mapping, the proposed models help to identify nuanced spatio-temporal behaviors on global, regional and local scales. Read more at Tufts Now.
Seasonality. We designed statistical methods and models to describe seasonality, periodic fluctuations occured on an annual basis, and to estimate a distributed and delayed effects of exposure on health outcomes. These methods are gaining popularity and have triggered a widespread interest to spatio-temporal models in epidemiological studies. Read more at Tufts Now.
Extreme Weather and Health. We explore short- and long-term adverse health effects of extreme weather in vulnerable populations. We demnstate such effects using passive surveillance systems and medical claims and applied the proposed models to describe changes associated with implementation of large scale and local policies. We design Decision Support Framework for Modeling Adverse Health Effects of Extreme Weather Using Multi-sourced Data (PI) with the goal is to develop methodology for modeling adverse health effects of extreme weather using data from many disparate sources. Read more at Tufts Now.
Big Data. We use large data repositories containing over 600M records of hospitalizations maintained by the Centers for Medicare and Medicaid Services. We demonstrate values and challenges of utilizing these rich, complex, and “big” data for environmental and public health research and advocate for using novel approaches in data collection and analysis. Read more.
Health, Water, and Climate Change. In the project we examined multiple channels of exposure from multiple sources for enteric infections in a study Environmental Predictors of Water Safety and Enteric Infections in Vulnerable Populations (PI) in collaboration with CMC researchers. We aslo explore how geographic, environmental and possibly cultural factors interact to maintain unsafe water and continued transmission of endemic enteric infections. Read more at Tufts Now.
Tufts-CMC Framework Program for Global Health Innovation (co-PI). The goal of this project is to strengthen the educational capacity of two academic institutions. Read more at Tufts Daily.