Courses Taught by Douglas Wiebe
EPID604: Applications Of Epidemiology
- Graduate level
- Residential
- Fall, Winter, Spring, Spring-Summer, Summer term(s) for residential students;
- 1-6 credit hour(s) for residential students;
- Instructor(s): Ella August, James Buskiewicz, Sara Adar, Matthew Boulton, Andrew Brouwer, Melissa Beck, Kelly Bakulski, Miatta Buxton, Joseph Eisenberg, Marisa Eisenberg, Nancy Fleischer, Betsy Foxman, Aubree Gordon, Alexis Handal, Jennifer Head, Jihyoun Jeon, Spruha Joshi, Sharon Kardia, Carrie Karvonen-Gutierrez, Lindsay Kobayashi, Peter Larson, Aleda Leis, Elizabeth Levin-Sparenberg, Lynda Lisabeth, Juan Marquez, Emily Martin, Briana Mezuk, Alison Mondul, Lewis Morgenstern, Belinda Needham, Marie O'Neill, Sung Kyun Park, C. Leigh Pearce, Laura Power, Alex Rickard, Jennifer Smith, Eduardo Villamor, Abram Wagner, Xin Wang, Douglas Wiebe, Zhenhua Yang, Jonathan Zelner, (Residential);
- Prerequisites: Instructor Permission
- Description: Application of epidemiological methods and concepts to analysis of data from epidemiological, clinical or laboratory studies. Introduction to independent research and scientific writing under faculty guidance.
- This course is cross-listed with .
- Syllabus for EPID604
EPID685: Measurement And Modeling In Space-time Epidemiology
- Graduate level
- Residential
- Winter term(s) for residential students;
- 3 credit hour(s) for residential students;
- Instructor(s): Douglas Wiebe (Residential);
- Prerequisites: None
- Advisory Prerequisites: None
- Description: Focused on measuring exposure based on where and how people spend time to understand health effects, including intermittent and abrupt-onset events, common in injury epidemiology. Emphasis on the temporal-spatial scale relevant to a given exposure-outcome question. Teaches coding to analyze data to test hypotheses while avoiding pitfalls including autocorrelation.
- Learning Objectives: Recall the features and strategy of Rothman's sufficient cause model. Differentiate early and late occurring risk factors for a given injury or disease. Classify the induction period for an exposure as it related to occurrence of an injury or disease. Classify data collection methods according to their suitability for a given exposure-outcome relation. Recognize which data collection methods (eg, realtime monitoring via wearable devices; responses to prompts received on smartphone in ecologic momentary assessment (EMA)) are feasible for a given research question and epidemiologic study design. Learn basic steps to collect data and learning coding to clean and analyze data that have temporal and spatial components (in R, SAS, or Stata). List the dimensions of Haddon's Matrix. Populate Haddon's Matrix for a given injury or disease prevention effort.