Programme Overview
Overview
Geostatistical models are increasingly important in public health research due to the rise in spatial health data. This intensive course suits statisticians and public health professionals wanting to enhance their skills in geospatial analysis and modelling. Participants learn about spatial data structures and geostatistics through real-life examples, progressing from basic to complex models using Bayesian inference with the INLA approach.
The focus is on spatial patterns of malaria and other health outcomes, with an emphasis on skills that continue to develop after the course.
Format
Duration
Typically a three-day intensive training.
Teaching Method
A mix of lectures and hands-on practicals, using real-life examples drawn from malaria research.
Software
R (version 4.0 and above).
Language
English.
Cost
Training is free of charge. Participants are responsible for their own travel and accommodation costs.
What You’ll Learn
Participants learn Bayesian geostatistical modelling approaches — such as INLA and INLAbru — applied to malaria and real-world data. The exact tools and special topics (e.g. spatial disaggregation) vary by cohort; see this year’s Edition page for the current curriculum.
Lecture topics include:
- Introduction to Geostatistics
- Spatial Data in R
- Areal Data and Models
- Geostatistical Models in INLA
- Basics of Point Process Models
Who Should Apply
This course is designed for statisticians, public health professionals, National Malaria Control Programme (NMCP) staff, and government researchers who want to build their geospatial analysis and modelling skills. Candidates should have:
- Basic experience with R
- An understanding of statistical inference
- Introductory knowledge of Bayesian statistical methods
Next Steps
Ready to apply? Review the eligibility criteria and application details on the Call for Applications page.
Curious what previous cohorts covered? See past cohorts.