Identification

Author

Patil AP, Gething PW, Piel FB, Hay SI

Title

Bayesian geostatistics in healthcartography: the perspectiveof malaria

Year

2011

Publication type

Article

Journal

Trends in Parasitology

Created

2013-08-02 16:23:55+00:00

Modified

2016-08-04 17:42:57.432035+00:00

Details

Volume

27

Number

6

Access

Language

English

URL http://www.map.ox.ac.uk/publications/
DOI

10.1016/j.pt.2011.01.003

Accessed

2016-06-13

Extended information

Abstract

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.