Elena Naumova is a director of the Tufts Initiative for Forecasting and Modeling of Infectious Diseases (InForMID). She applies her theoretical work to studies of infections sensitive to climate variations and extreme weather events. Her research activities span a broad range of research programs in emerging and re-emerging diseases, environmental epidemiology, molecular biology, and immunogenetics, nutrition, and growth. Naumova participates in a number of international projects, collaborating with epidemiologists, immunologists, and public health professionals in India, Kenya, Ecuador, Japan, Canada, the United Kingdom, and Russia.
Adjunct Professor, Department of Civil and Environmental Engineering, Tufts School of Engineering
Adjunct Professor, Christian Medical College
Visiting Professor, DIMACS, Rutgers University
Professor, Friedman School of Nutritional Science and Policy, Tufts University
Director, Tufts University Initiative for the Forecasting and Modeling of Infectious Diseases (InForMID), Tufts University
Department of Public Health and Family Medicine, Tufts University School of Medicine
- 2010-present: Adjunct Professor
- 2008-2010: Professor
- 2003-2008: Associate Professor
- 1997-2003: Assistant Professor
Research Assistant, Center for Environmental Epidemiology, Medical College of Wisconsin
Senior Investigator, Department of Information and Analysis, Institute of Pathology of Blood Circulation, Russian Ministry of Public Health
Research Assistant, Mathematical Modeling and Statistical Processing Laboratory, Institute of Clinical Immunology, Siberian Division of the USSR Academy of Medical Science
Elena Naumova's area of expertise is in methodology development for modeling of transient processes with application in environmental epidemiology, infectious diseases, and public health. Her research on developing innovative analytical and computational tools for monitoring environmentally-driven infections and longitudinal studies of growth is funded by NIAID, NIEHS, and EPA. She 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.