This graduate course presents a practical and comprehensive overview of geographical epidemiology, and is targeted at students and faculty from disciplines such as veterinary epidemiology, biostatistics and medical geography, as well as public health practitioners and researchers. The notions of spatial data, epidemiology and map-making are presented. The emphasis of the course is on spatial statistical methods and their application to public health data. Computational aspect will focus on the use of the open and free R software and R-Studio. There is also great interest to link the location of disease occurrence to spatially varying risk factors. This course presents a practical and comprehensive overview of geographical epidemiology. Participants are introduced to the notions of spatial data, epidemiology and map-making. The emphasis of the course will be on spatial statistical methods and their application in public health, epidemiology, veterinary science and medical geography.
The course is designed for public health practitioners and researchers, students and faculty from disciplines such as veterinary epidemiology, biostatistics and medical geography.
At the end of the course, participants should be able to;
- Explain the uses of geographic epidemiology
- Identify types of spatial data in public health
- Associate types of statistical methods and mapping methods associated with data types
- Apply R software to analyze and map public health data
- Apply SaTScan software to identify geographic clusters in public health data
- Apply R and WinBugs/OpenBugs software to investigate geographic risk factors of disease
- Identify areas of recent research avenues in geographical epidemiology
In addition to specific learning outcomes stated above, the course aims to instill a general attitude towards critical thinking in statistical and epidemiological science.
This graduate equivalent course normally requires that learners have a Bachelor's or Master's degree with course work in public health, epidemiology, statistics or equivalent.
All course materials will be posted on the Geo Epi CourseLink website. Access information and pre-course activities will be sent to you in advance of the course.
A laptop computer is required for the course. Students are required to bring their own laptop with R/RStudio installed - instructions will be provided prior to the course. Electrical plugs are available in the classroom.
Open Learning participants will be assessed by completing a quiz on the last day. Students taking the course for credit will have additional assignments.
Note: Open Learning students may complete the assignment if they wish to receive course credit. Assignment information and schedule will be included in the course manual and/or made available online through CourseLink.
- Dr. Julie Horrocks (Mathematics and Statistics)
- Dr. Lorna Deeth (Mathematics and Statistics)
- Department of Mathematics and Statistics
- Ontario Veterinary College