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Mapping malaria risk in West Africa using a Bayesian nonparametric non-stationary model

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  • Gosoniu, L.
  • Vounatsou, P.
  • Sogoba, N.
  • Maire, N.
  • Smith, T.

Abstract

Malaria transmission is highly influenced by environmental and climatic conditions but their effects are often not linear. The climate-malaria relation is unlikely to be the same over large areas covered by different agro-ecological zones. Similarly, spatial correlation in malaria transmission arisen mainly due to spatially structured covariates (environmental and human made factors), could vary across the agro-ecological zones, introducing non-stationarity. Malaria prevalence data from West Africa extracted from the "Mapping Malaria Risk in Africa" database were analyzed to produce regional parasitaemia risk maps. A non-stationary geostatistical model was developed assuming that the underlying spatial process is a mixture of separate stationary processes within each zone. Non-linearity in the environmental effects was modeled by separate P-splines in each agro-ecological zone. The model allows smoothing at the borders between the zones. The P-splines approach has better predictive ability than categorizing the covariates as an alternative of modeling non-linearity. Model fit and prediction was handled within a Bayesian framework, using Markov chain Monte Carlo (MCMC) simulations.

Suggested Citation

  • Gosoniu, L. & Vounatsou, P. & Sogoba, N. & Maire, N. & Smith, T., 2009. "Mapping malaria risk in West Africa using a Bayesian nonparametric non-stationary model," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3358-3371, July.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:9:p:3358-3371
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    1. Corentin M Barbu & Andrew Hong & Jennifer M Manne & Dylan S Small & Javier E Quintanilla Calderón & Karthik Sethuraman & Víctor Quispe-Machaca & Jenny Ancca-Juárez & Juan G Cornejo del Carpio & Fernan, 2013. "The Effects of City Streets on an Urban Disease Vector," PLOS Computational Biology, Public Library of Science, vol. 9(1), pages 1-9, January.
    2. Peter W Gething & Anand P Patil & Simon I Hay, 2010. "Quantifying Aggregated Uncertainty in Plasmodium falciparum Malaria Prevalence and Populations at Risk via Efficient Space-Time Geostatistical Joint Simulation," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-12, April.
    3. Jithitikulchai, Theepakorn, 2023. "The effect of climate change and agricultural diversification on the total value of agricultural output of farm households in Sub-Saharan Africa," African Journal of Agricultural and Resource Economics, African Association of Agricultural Economists, vol. 18(2), October.
    4. Osadolor Ebhuoma & Michael Gebreslasie, 2016. "Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa," IJERPH, MDPI, vol. 13(6), pages 1-29, June.

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