Bivariate binomial spatial modeling of Loa loa prevalence in tropical Africa

Ciprian M. Crainiceanu, Peter J. Diggle, Barry Rowlingson

Research output: Contribution to journalArticlepeer-review


We present a state-of-the-art application of smoothing for dependent bivariate binomial spatial data to Loa loa prevalence mapping in West Africa. This application starts with the nonspatial calibration of survey instruments, continues with the spatial model building and assessment, and ends with robust, tested software intended for use by field workers for online prevalence map updating. From a statistical perspective, we address several important methodological issues: building spatial models that are sufficiently complex to capture the structure of the data but remain computationally usable, reducing the computational burden in the handling of very large covariate data sets, and devising methods for comparing spatial prediction methods for a given exceedance policy threshold.

Original languageEnglish (US)
Pages (from-to)21-37
Number of pages17
JournalJournal of the American Statistical Association
Issue number481
StatePublished - Mar 2008


  • Geostatistics
  • Low rank
  • Thin-plate splines

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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