Dalla Lana School of Public Health

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:

1. Introduce spatial and spatiotemporal statistical data and analysis methods available for public health studies;
2. Foster spatial and spatiotemporal thinking in the context of epidemiological and public health studies;
3. 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%