Application of knowledge-driven spatial modelling approaches and uncertainty management to a study of Rift Valley fever in Africa
2006

Mapping Rift Valley Fever Risk in Africa

publication Evidence: moderate

Author Information

Author(s): Archie C. A. Clements, Dirk U. Pfeiffer, Vincent Martin

Primary Institution: Royal Veterinary College, UK

Hypothesis

Can decision science methods improve understanding of the geographical distribution of Rift Valley fever in Africa?

Conclusion

The study shows that decision science methods can enhance understanding of uncertainties in the geographical distribution of animal diseases like Rift Valley fever.

Supporting Evidence

  • Most of sub-Saharan Africa was found suitable for endemic circulation of RVF.
  • Areas near rivers and lakes in semi-arid regions were highly suitable for RVF epidemics.
  • Variations in model structure significantly impacted suitability estimates.

Takeaway

This study helps us understand where Rift Valley fever might spread in Africa, using smart methods to make sense of limited data.

Methodology

The study used multiple criteria decision making and Dempster-Shafer theory within a geographical information system to assess RVF suitability.

Potential Biases

Subjectivity in defining weights and the potential for publication bias may influence the results.

Limitations

The study relies on limited data and subjective assessments, which may affect the accuracy of the suitability maps.

Digital Object Identifier (DOI)

10.1186/1476-072X-5-57

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