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American Journal of Epidemiology Advance Access originally published online on December 15, 2005
American Journal of Epidemiology 2006 163(3):289-297; doi:10.1093/aje/kwj026
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American Journal of Epidemiology Copyright © 2005 by the Johns Hopkins Bloomberg School of Public Health All rights reserved; printed in U.S.A.

Practice of Epidemiology

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

A. Gemperli1, P. Vounatsou1, N. Sogoba2 and T. Smith1

1 Biostatistics and Basic Epidemiology Group, Department of Public Health and Epidemiology, Swiss Tropical Institute, Basel, Switzerland
2 Epidemiology and GIS Unit, Department of Medical Entomology and Vector Ecology, Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Odonto-Stomatology, University of Bamako, Bamako, Mali

Correspondence to Dr. Armin Gemperli, Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, 615 North Wolfe Street, Baltimore, MD 21205 (e-mail: agemperl{at}jhsph.edu).

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.

disease transmission; kriging; malaria; Markov chain Monte Carlo

Abbreviations: EIR, entomological inoculation rate; MARA, Mapping Malaria Risk in Africa; NDVI, Normalized Difference Vegetation Index


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