Spatio-Temporal Methods in Environmental Epidemiology
Chapter 8 - Spatial Patterns in Disease
This chapter introduces disease mapping and contains the theory for spatial lattice processes and models for performing
smoothing of risks over space. From this chapter, the reader will have gained an understanding of the following topics:
Disease mapping, where we have seen how to improve estimates of risk by borrowing strength
from adjacent regions which can reduce the instability inherent in risk estimates (SMRs)
based on small expected numbers.
Seen how smoothing can be performed using either the empirical Bayes or fully Bayesian
Been introduced to computational methods for handling areal data.
Learned about Besag’s seminal contributions to the field of spatial statistics including
the very important concept of a Markov random field.
Explored approaches to modelling a real data including the conditional autoregressive models.
Seen how Bayesian spatial models for lattice data use WinBUGS, R and R–INLA.