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Spatial modelling and the prediction of Loa loa risk: Decision making under uncertainty

  • Peter Diggle
  • , M. C. Thomson
  • , O. F. Christensen
  • , B. Rowlingson
  • , V. Obsomer
  • , J. Gardon
  • , S. Wanji
  • , I. Takougang
  • , P. Enyong
  • , J. Kamgno
  • , J. H. Remme
  • , M. Boussinesq
  • , David Molyneux
  • Lancaster University
  • Columbia University
  • Aarhus University
  • Institute of Tropical Medicine Antwerp
  • Centre Pasteur du Cameroun
  • Institut de recherche pour le développement
  • University of Buea
  • Université de Yaoundé I
  • Tropical Medicine Research Station
  • World Health Organization

Research output: Contribution to journalArticlepeer-review

91 Citations (Scopus)

Abstract

Health decision-makers working in Africa often need to act for millions of people over large geographical areas on little and uncertain information. Spatial statistical modelling and Bayesian inference have now been used to quantify the uncertainty in the predictions of a regional, environmental risk map for Loa loa ( a map that is currently being used as an essential decision tool by the African Programme for Onchocerciasis Control). The methodology allows the expression of the probability that, given the data, a particular location does or does not exceed a predefined high-risk threshold for which a change in strategy for the delivery of the antihelmintic ivermectin is required.

Original languageEnglish
Pages (from-to)499-509
Number of pages11
JournalPathogens and Global Health
Volume101
Issue number6
DOIs
Publication statusPublished - 1 Sept 2007

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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