This course introduces students to the concepts, principles, and methods for the visualization and analysis of spatially referenced health data. Lectures, discussion and assignments will highlight spatial data analysis techniques with applications in malaria and other infectious and non-infectious diseases prevalent in international settings. Hands-on skills-based exercises will be emphasized over lecture-based presentation of theory, so students should be prepared to be fully engaged.
At the end of this course, scholars will be able to:
- Demonstrate proficiency in spatial data analysis techniques using open-source software including QGIS, R, SaTScan and WinBUGS (or other software for Apple brand computer users);
- Understand the theories and assumptions of different spatial data analysis methods;
- Conduct exploratory spatial data analysis and identify and apply the correct analytical tools for a given problem; and
- Perform spatial regression analyses and produce basic smoothed disease maps.
Students should have a good basic understanding of the software program R as this will be heavily used. A good understanding of mixed effect regression modeling is also required.
|Course Directors:||Adam Bennett, PhD, MA
|Hugh Sturrock, PhD, MSc
Course content will be delivered through weekly interactive lectures. These will take place on Wednesdays from 12:30 PM to 3:30 PM, Sept. 18 to Dec. 4 (no class on Nov. 27).
Readings will be provided and accessible through the course's online syllabus.
Grading will be based on:
- Oral Presentation (30%)
- Written Reports (50%)
- Weekly Written Assignments (20%)
To enroll in this course, please fill out and submit the application below. Course fees are covered by the Department of Epidemiology and Biostatistics, and hence there is no charge for this course.
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