Journal Article

Malaria Mapping Using Transmission Models: Application to Survey Data from Mali

A. Gemperli, P. Vounatsou, N. Sogoba and T. Smith

in American Journal of Epidemiology

Published on behalf of Johns Hopkins Bloomberg School of Public Health

Volume 163, issue 3, pages 289-297
Published in print February 2006 | ISSN: 0002-9262
Published online December 2005 | e-ISSN: 1476-6256 | DOI: http://dx.doi.org/10.1093/aje/kwj026
Malaria Mapping Using Transmission Models: Application to Survey Data from Mali

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Geographic mapping of the distribution of malaria is complicated by the limitations of the available data. The most widely available data are from prevalence surveys, but these surveys are generally carried out at arbitrary locations and include nonstandardized and overlapping age groups. To achieve comparability between different surveys, the authors propose the use of transmission models, particularly the Garki model, to convert heterogeneous age prevalence data to a common scale of estimated entomological inoculation rates, vectorial capacity, or force of infection. They apply this approach to the analysis of survey data from Mali, collected in 1965–1998, extracted from the Mapping Malaria Risk in Africa database. They use Bayesian geostatistical models to produce smooth maps of estimates of the entomological inoculation rates obtained from the Garki model, allowing for the effect of environmental covariates. Again using the Garki model, they convert kriged entomological inoculation rates values to age-specific malaria prevalence. The approach makes more efficient use of the available data than do previous malaria mapping methods, and it produces highly plausible maps of malaria distribution.

Keywords: disease transmission; kriging; malaria; Markov chain Monte Carlo; EIR, entomological inoculation rate; MARA, Mapping Malaria Risk in Africa; NDVI, Normalized Difference Vegetation Index

Journal Article.  5467 words.  Illustrated.

Subjects: Public Health and Epidemiology

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