The MS in Epidemiology is an innovative 12-month program that provides rigorous training in the foundational concepts, theory, and advanced methods of epidemiology with an emphasis on applying quantitative research skills to address complex public health problems. Our students will acquire skills in hypothesis formation and testing, data collection, statistical computing, research design, and interpretation of results and findings.
As part of the degree, you will complete a thesis or manuscript under the mentorship of a faculty member in an area of your interest. Possible areas include environmental health, cancer, global health, infectious diseases, nutrition, aging, and population health.
Courses are offered Monday- Thursday 4pm- 9pm. The program has a structured curriculum that can be completed in 12 months full time. There are also part time options available. We expect that you will complete 34 credits with a minimum grade point average of 3.0 (B).
The course descriptions below are a sampling of courses offered in the MS in Epidemiology Program:
Epidemiology is the study of the distribution and determinants of health-related states and events in specified populations, and the application of this knowledge to control health problems. This course will introduce students to concepts that guide the design and analysis of various epidemiologic study designs, including counterfactuals, confounding, effect measure modification, measurement error and bias, as well as practical considerations. In parallel with lectures and assigned readings, lab sessions will guide students through applications of these concepts, including constructing causal diagrams and using SAS software for epidemiologic analysis.
Applied Linear Models I
Regression analysis is a widely used set of methods for exploring the relationships between response variables and one or more explanatory variables. This course provides an introduction to regression methods for a single continuous response variable. Both linear and curvilinear regression models are considered. Model assumptions, and regression diagnostics for assessing those assumptions, are explored in detail. Strategies for model selection are presented. The emphasis is on concepts and application rather than on underlying theory. As mathematical results are presented without proof, students are not required to be proficient in calculus or matrix algebra.
Epidemiology of Infectious Disease
Epidemiology of Infectious Diseases builds upon the concepts presented in Introduction to Epidemiology, stressing the importance of considering the host, environment and disease agent in transmission dynamics. The nineteenth and twentieth centuries witnessed advances in prevention, treatment, and study of infectious diseases and the misconception that infectious diseases were disappearing. The study of infectious diseases leads to the continual development of vaccines, antibiotics, and technology, prompting epidemiologists to develop more advanced methods to monitor disease, investigate patterns of disease transmission, and evaluate innovative prevention modalities. The past thirty years have brought to light both new and re-emerging problems in the epidemiology of infectious diseases, including HIV, SARS, avian influenza, arboviruses, antimicrobial resistance, and the threat of bioterrorism.
This course focuses on the fundamentals of epidemiological methods specific to environmental health research. The course will provide students with an insight to appropriate study designs and methodologies to investigate health effects of environmental exposures. These include fundamental concepts involved in generating research hypotheses, as well as environmental health specific issues such as use of exposure biomarkers, models of exposure (e.g. air pollution), study design issues, confounding and other types of bias, and phenotyping issues as they relate to environmental factors. We will also review data analytic strategies unique to environmental health (e.g. mixtures), the nascent field of exposomics, and the interpretation of the study findings and public health implications for environmental epidemiological research. The students will also learn the techniques for critical appraisal of environmental epidemiological studies. These are achieved through lectures with in-depth discussion of current research status on environmental epidemiology, readings, homework assignments, and exams.
Big Data Epidemiology
Omics research is an emerging, multidisciplinary, and rapidly evolving field that has started to impact both clinical practice and public health. Omics encompasses many molecular biology domains such as genomics, epigenomics, transcriptomics, proteomics, metabolomics and exposomics. These molecular domains can offer a more nuanced perspective on how multiple exposures (e.g., environmental, lifestyle, social factors) affect health compared with traditional research approaches. This course provides students with an understanding of omics research methods and applications, and hands-on training in big data analysis with omics biomarkers for epidemiology studies.