Pick almost any topic in public health: influenza epidemics, rising cancer rates, healthcare disparities. To understand how any of these problems are distributed across populations—much less, how to design an intervention—requires an epidemiologic mindset.
A cornerstone of public health, epidemiology focuses on the distribution and causes of disease, and on developing and testing ways to prevent and control it. From health departments to academia to the private sector, epidemiologists investigate public health challenges, including communicable and non-communicable diseases, aging, determinants of health, and mental health.
The Certificate in Advanced Epidemiology provides graduates with in-depth expertise in epidemiologic methods across different topics like infectious and chronic disease and for diverse populations. The program fosters understanding of the role of epidemiology within the broader fields of public health, medicine, and social and behavioral sciences—areas in which epidemiologists often collaborate.
This certificate enables graduates to identify potential ethical problems in research studies and evaluate alternative approaches. The intensive curriculum prepares MPH graduates with the critical interdisciplinary thinking and methodological skill set needed to address today’s complex public health challenges.
Advanced Epidemiology is open to Columbia MPH students in:
Applicants should have taken one semester of calculus and scored in the 75th percentile or higher on the Quantitative Reasoning Section of the GRE.
Students who do not meet the calculus, and GRE requirements can be considered contingent upon receiving an A- grade in the REMA-Quantitative module of the Core during the Fall semester.
Visit the Certificates Database to learn more about core and credit requirements.
Epi Modeling for Infectious DiseasesThis course is an intro to intermediate level infectious disease mathematical modeling methodological class. It will introduce the fundamental principles of infectious disease modeling. Emphasis will be given to compartmental metapopulation models. Over the course, we will learn a variety of mathematical models for infectious diseases, starting from simple compartmental models to more complex compartmental models with various structures, including multiple risk groups, age groups, spatial network, and multiple hosts. In addition to these models, we will also discuss how key epidemiological parameters (e.g. the basic reproductive number) can be estimated from real disease data. Other topics will include vaccination/antiviral efficacy assessment, survival analysis, and agent based models. Half of the course will be devoted to hands-on computer lab excises, using the R Studio open source program (if students are familiar with Python or Matlab, these programs can be used instead).
Application of Epi Research Methods II
This course will introduce students to the basic programming skills necessary to adapt R statistical computing system to their needs by presenting material spanning a spectrum from basic epidemiological measures of disease outcomes and association to more advanced applications such as mapping, spatial analysis and Bayesian methods using additional open-source tools like GRASS and WinBUGS. The course is structured in three parts: (1) Introduction to R and object based programming, (2) How to apply and extend R for epidemiological methods, and (3) Using R for advanced methods such as spatial and Bayesian analysis. Practice material and data are grounded on actual research questions, often based on the instructor’s recent work, and are intended to illustrate the kinds of issues that often arise when practicing epidemiology.
Applied Regression II
This course introduces the statistical methods for analyzing censored data, non-normally distributed response data, and repeated measurements data that are commonly encountered in medical and public health research. Topics include estimation and comparison of survival curves, regression models for survival data, logit models, log-linear models, and generalized estimating equations. Examples are drawn from the health sciences.