London School of Hygiene & Tropical Medicine

Spatial epidemiology includes description and analysis of geographic variation in health outcomes with respect to multiple contextual factors (including environmental, demographic, socio-economic and behavioural). This very practical course provides an overview of how to approach spatial problems in epidemiology, with a specific focus on public health applications.

The course is structured sequentially to move from sourcing spatial health and covariate data, to visualisation and spatial exploration, quantifying spatial patterns, and finally to methods for spatial prediction. Broadly, the course focuses on:

• Assessing and visualising data through the use of a geographical information system (GIS);
• Sourcing important spatial health and covariate data, including population and remotely sensed data;
• Quantifying spatial patterns using a range of exploratory statistical approaches;
• Introducing approaches to modelling spatial data (i.e. predicting health outcomes in space). 

Spatial epidemiology is a very rapidly advancing field, pushing our abilities to map, monitor and model health outcomes at increasingly fine spatial resolution. Although we do introduce these new advances, the course primarily focuses on fundamental principles.

Nb The first two days of this module mirror the stand-alone GIS training provided by LSHTM. If your interests primarily concern visualising data using a GIS, you may find that the stand-alone training is sufficient for your needs.

The overall module aim is to introduce students to methods for analysing and predicting spatial patterns of infectious diseases, and to develop a critical appreciation of their application to disease control.

Intended learning outcomes

Upon successful completion of the module a student will be able to:

1. Collect and organise spatial data on disease and its ecological determinants (e.g. climate, land-use and poverty) using appropriate tools, including Global Positioning Systems, Geographic Information Systems platforms (qGIS) and R statistical software;
2. Apply basic statistical techniques to analyse the spatial patterns of infection and disease;
3. Appreciate the relative merits of alternative spatial statistical approaches for exploring and predicting spatial distributions of infection and disease;
4. Demonstrate an understanding of how the output of these analyses can be integrated into a rational disease control programme and be able to relate your knowledge to published/peer-reviewed studies.

Mode of delivery

This module is delivered predominantly face-to-face. Where specific teaching methods (lectures, seminars, discussion groups) are noted in this module specification these will be delivered by predominantly face-to-face sessions. There will be a combination of live and interactive activities (synchronous learning) as well as self-directed study (asynchronous learning).

Assessment

The assessment for this module has been designed to measure student learning against the module intended learning outcomes (ILOs) as listed above. Formative assessment methods may be used to measure students’ progress. The grade for summative assessment(s) only will go towards the overall award GPA.

The assessment for this module will be online. Students will be provided with an epidemiological dataset and asked to analyse these data using appropriate spatial analytical approaches covered in the module. This assessment is written up as a report (word limit: 2,000 words).

Credits

• CATS: 15
• ECTS: 7.5

Module specification

For full information regarding this module please see the module specification.