Mapping, bayesian geostatistical analysis and spatial prediction of lymphatic filariasis prevalence in Africa.

ABSTRACT: There is increasing interest to control or eradicate the major neglected tropical diseases. Accurate modelling of the geographic distributions of parasitic infections will be crucial to this endeavour. We used 664 community level infection prevalence data collated from the published literature in conjunction with eight environmental variables, altitude and population density, and a multivariate Bayesian generalized linear spatial model that allows explicit accounting for spatial autocorrelation and incorporation of uncertainty in input data and model parameters, to construct the first spatially-explicit map describing LF prevalence distribution in Africa. We also ran the best-fit model against predictions made by the HADCM3 and CCCMA climate models for 2050 to predict the likely distributi

SUBMITTER: Slater H 

PROVIDER: S-EPMC3741112 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature