Maps of parasite prevalences and other aspects of infectious diseases that vary in space are widely used in parasitology. However, spatial parasitological datasets rarely, if ever, have sufficient coverage to allow exact determination of such maps. Bayesian geostatistics (BG) is a method for finding a large sample of maps that can explain a dataset, in which maps that do a better job of explaining the data are more likely to be represented. This sample represents the knowledge that the analyst has gained from the data about the unknown true map. BG provides a conceptually simple way to convert these samples to predictions of features of the unknown map, for example regional averages. These predictions account for each map in the sample, yielding an appropriate level of predictive precision.

Original publication

DOI

10.1016/j.pt.2011.01.003

Type

Journal article

Journal

Trends in Parasitology

Publisher

Elsevier (Cell Press)

Publication Date

06/2011

Volume

27

Pages

246 - 253

Keywords

Africa, Bayes Theorem, Likelihood Functions, Malaria, Falciparum, Population Density, Prevalence, Public Health