School of Public Health


Highlighted Courses


outbreak investigation course


In EPI 201, students step into the shoes of an outbreak investigator. They learn how disease outbreaks—such as Ebola, food-borne illnesses, and pandemic flu—start, spread, and are detected and investigated. Students explore:

  • the social and environmental factors that influence outbreaks
  • the agencies and systems in place to manage outbreak response, and
  • the role of communications, law, and government.

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Exploring HIV/AIDS courseEPI 360: Exploring the HIV/AIDS Pandemic

EPI 360 presents a comprehensive picture of the historical, public health, clinical, and biological aspects of HIV infection. Both public health and pre-med students discuss the impact of AIDS on community and global health care and analyze prospects for prevention and control. Students explore:

  • risk factors and behavioral interventions for prevention of HIV infection
  • the basics of HIV/AIDS treatment, and
  • future issues and trends concerning HIV/AIDS

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methods courseEpi 515 & 516: Advanced Epidemiologic Methods I & II

Epi 515 is an epidemiology methods course aimed at continuing the Department’s long tradition of providing excellent training for the next generation of epidemiologists. Master’s and PhD level students take this first in a two-course series about advanced epidemiology methods. Topics include:

  • Causal inference in the potential outcomes framework
  • Measurement error and bias correction methods
  • Survey design and methodology
  • Missing data using single and multiple imputation approaches

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Epi 516 is the second of in a two-course series on advanced epidemiologic methods. The primary objective of this course is to deepen students’ knowledge of epidemiological and biostatistical principles by introducing methodological approaches to handling common problems in epidemiologic research that extend beyond the scope of traditional methods.  Topics include:

  • Mediation analysis
  • Multiple comparisons adjustment
  • Propensity scores
  • Inverse probability weighting
  • Alternative study designs
  • Marginal structural models

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