title: Bayesian geostatistics in health cartography: the perspective of malaria.
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.
authors.affiliation: Spatial Ecology and Epidemiology Group, Department of Zoology, The Tinbergen Building, South Parks Road, University of Oxford, Oxford, UK OX1 3PS. anand.patil@zoo.ox.ac.uk
authors.forename: Anand P
authors.initials: AP
authors.lastname: Patil
authors.forename: Peter W
authors.initials: PW
authors.lastname: Gething
authors.forename: Frédéric B
authors.initials: FB
authors.lastname: Piel
authors.forename: Simon I
authors.initials: SI
authors.lastname: Hay
mesh_terms: D000349:Africa / Q000453:epidemiology; D001499:Bayes Theorem; D016013:Likelihood Functions; D016778:Malaria, Falciparum / Q000453:epidemiology* / Q000635:transmission; D011156:Population Density; D015995:Prevalence; D011634:Public Health / Q000706:statistics & numerical data