Skip to main content

Free Content Mapping malaria transmission in West and Central Africa

Download Article:

You have access to the full text article on a website external to Ingenta Connect.

Please click here to view this article on Wiley Online Library.

You may be required to register and activate access on Wiley Online Library before you can obtain the full text. If you have any queries please visit Wiley Online Library


We have produced maps of Plasmodium falciparum malaria transmission in West and Central Africa using the Mapping Malaria Risk in Africa (MARA) database comprising all malaria prevalence surveys in these regions that could be geolocated. The 1846 malaria surveys analysed were carried out during different seasons, and were reported using different age groupings of the human population. To allow comparison between these, we used the Garki malaria transmission model to convert the malaria prevalence data at each of the 976 locations sampled to a single estimate of transmission intensity E, making use of a seasonality model based on Normalized Difference Vegetation Index (NDVI), temperature and rainfall data. We fitted a Bayesian geostatistical model to E using further environmental covariates and applied Bayesian kriging to obtain smooth maps of E and hence of age-specific prevalence. The product is the first detailed empirical map of variations in malaria transmission intensity that includes Central Africa. It has been validated by expert opinion and in general confirms known patterns of malaria transmission, providing a baseline against which interventions such as insecticide-treated nets programmes and trends in drug resistance can be evaluated. There is considerable geographical variation in the precision of the model estimates and, in some parts of West Africa, the predictions differ substantially from those of other risk maps. The consequent uncertainties indicate zones where further survey data are needed most urgently. Malaria risk maps based on compilations of heterogeneous survey data are highly sensitive to the analytical methodology.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: Markov chain Monte Carlo; entomological inoculation rate; kriging; malaria; parasite prevalence; vectorial capacity

Document Type: Research Article

Affiliations: 1:  Swiss Tropical Institute, Basel, Switzerland 2:  Organisation de Coordination pour la lutte contre les Endémies en Afrique Centrale, Yaoundé, Cameroon 3:  World Health Organization Regional Office for Africa, Libreville, Gabon 4:  Biological & Environmental Engineering, Cornell University, Ithaca, NY, USA

Publication date: 01 July 2006

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more