Disease Mapping
Disease mapping is the set of model-based methods for estimating and displaying the geographic distribution of disease risk across small areas. Its central problem is that raw area-level rates, especially standardized mortality or incidence ratios, are statistically unstable where populations are small: a handful of cases can produce wildly high or low rates that reflect chance rather than true risk. Clayton and Kaldor's 1987 empirical-Bayes paper showed how to stabilize these estimates by shrinking each area's rate toward an overall mean using a Poisson-gamma (or log-normal) hierarchical model, and the approach was developed into the fully Bayesian, spatially smoothed hierarchical framework synthesized in Lawson's textbook. As a pipeline, disease mapping computes expected counts, places the counts in a hierarchical risk model, borrows strength globally and across neighbors to smooth the estimates, and produces a risk map with quantified uncertainty, including probabilities that risk exceeds a threshold.
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- Clayton, D., & Kaldor, J. (1987). Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics, 43(3), 671-681. · DOI 10.2307/2532003
- Lawson, A. B. (2018). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology (3rd ed.). Chapman & Hall/CRC. · ISBN 9781138575424
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