University of Washington School of Public Health

Winter Course 2018: BIOST 555/EPI 555/GH 534 - Statistical Methods for Spatial Epidemiology


Winter 2018 Course Offering: BIOST 555/EPI 555/GH 534 Statistical Methods for Spatial Epidemiology

Quarter: Winter 2018

Time and Location: T Th 9:00-10:20am, Health Sciences T739

Grading: Graded, 3 credits

Instructor: Jon Wakefield, Professor of Statistics and Biostatistics (

SLN: 11682

Prerequisites: Previous exposure to regression modeling, some familiarity with log-linear or logistic modeling is desirable.

This course motivates the need for, and describes methods for, the analysis of spatially indexed epidemiological data. Major topics to be covered include disease mapping, clustering and cluster detection, spatial regression, methods for infectious disease data, small area estimation and an introduction to geographical information systems.

Learning Objectives: At the end of the course the student will be able to describe the need for specialized methods for the analysis of spatial data, distinguish between different types of spatial data, and choose an appropriate analysis method.

Motivating data:

- Disease mapping area-level cancer incidence and mortality data

- Cluster investigation close to a pollution source

- Assessment

- Small area estimation in a developing world context

- Analysis of space-time measles data

- Geostatistical models for exposure mapping

Both point-references and spatially aggregated data will be considered. The use of R packages for analysis will be described.

The availability of geographically indexed health, population and exposure data, and advances in computing, geographic information systems, and statistical methodology, have enabled the realistic investigation of spatial variation in disease risk. Each of the population, exposure and health data may have associated exact spatial and temporal information (point data), or be available as aggregated summaries (count data).  The following specific topics, with analysis methods listed for each, will be covered:

Disease mapping:

- Geostatistical smoothing models for point-level data, including prevalence data

- Kernel density estimation

- Models for area-level (aggregate) data

- Extensions to space-time modeling

Clustering and cluster detection:

- Autocorrelation statistics, including Moran’s I

- K-functions

- Scan statistics including SatScan

Spatial regression:

- Problems with conventional analyses

- Methods for acknowledgement of residual spatial dependence

- The ecological fallacy

Methods for infectious disease data:

- Discrete-time Susceptible-Infectious-Recovered (SIR) and related models

Small area estimation (domain estimation):

- Bayesian model-based approaches

- Incorporation of design weights


- Background to GIS

- How to visualize spatial data in R


Evaluation: Via biweekly homework's (40%) and a course project (60%). The final version of the latter is to be completed by the end of the quarter, with an outline to be handed in approximately halfway through the quarter.

Reading list:

Elliott, P., Wakefield, J., Best, N. and Briggs, D. (2000). Spatial Epidemiology: Methods and Applications, Oxford University Press.

Waller, L.A. and Gotway, C.A. (2004). Applied Spatial Statistics for Public Health Data, Wiley, New York.