Applied Spatial Statistics for Public Health Data
Course Description
This course provides a comprehensive introduction to spatial and spatiotemporal methods used in public health studies. It is designed as two related parts: (1) spatial and spatiotemporal statistics and (2) geostatistical regression models.
In Part 1, students will gain a solid understanding of spatial and spatiotemporal data structure, statistical methods and techniques utilized in disease mapping and pattern analyses. Part 2 introduces geostatistic data structure and regression models used for disease modeling and health risk prediction.
This course includes lectures and tutorials, using case studies to illustrate concepts, theory, and methodologies. R programming will also be provided to enhance practical knowledge and real-word data applications.
Course Objectives
The course aims to:
- Introduce spatial and spatiotemporal statistical data and analysis methods available for public health studies;
- Foster spatial and spatiotemporal thinking in the context of epidemiological and public health studies;
- Enhance students’ knowledge and skills in spatial and spatiotemporal data analyses for disease surveillance and health risk modeling.
By the course’s end, students will have a comprehensive understanding of spatial and spatiotemporal data utilized in public health research. They will also have gained valuable skills in spatial data analysis and modeling, enabling them to address real-word challenges in the field.
Methods of Assessment
| Participation | 10% |
| Assignments (x 2) | 25% each |
| Final Exam | 40% |