Mapping malaria risk in Bangladesh using Bayesian geostatistical models

Reid, Heidi, Haque, Ubydul, Clements, Archie C. A., Tatem, Andrew J., Vallely, Andrew, Ahmed, Syed Masud, Islam, Akramul and Haque, Rashidul (2010) Mapping malaria risk in Bangladesh using Bayesian geostatistical models. American Journal of Tropical Medicine and Hygiene, 83 4: 861-867. doi:10.4269/ajtmh.2010.10-0154

Author Reid, Heidi
Haque, Ubydul
Clements, Archie C. A.
Tatem, Andrew J.
Vallely, Andrew
Ahmed, Syed Masud
Islam, Akramul
Haque, Rashidul
Title Mapping malaria risk in Bangladesh using Bayesian geostatistical models
Journal name American Journal of Tropical Medicine and Hygiene   Check publisher's open access policy
ISSN 0002-9637
Publication date 2010-10
Sub-type Article (original research)
DOI 10.4269/ajtmh.2010.10-0154
Volume 83
Issue 4
Start page 861
End page 867
Total pages 7
Editor James W. Kazura
Cathi B. Siegel
Place of publication Deerfield, IL, U.S.A.
Publisher American Society of Tropical Medicine and Hygiene
Collection year 2011
Language eng
Formatted abstract
Background malaria-control programs are increasingly dependent on accurate risk maps to effectively guide the allocation of interventions and resources. Advances in model-based geostatistics and geographical information systems (GIS) have enabled researchers to better understand factors affecting malaria transmission and thus, more accurately determine the limits of malaria transmission globally and nationally. Here, we construct Plasmodium falciparum risk maps for Bangladesh for 2007 at a scale enabling the malaria-control bodies to more accurately define the needs of the program. A comprehensive malaria-prevalence survey (N = 9,750 individuals; N = 354 communities) was carried out in 2007 across the regions of Bangladesh known to be endemic for malaria. Data were corrected to a standard age range of 2 to less than 10 years. Bayesian geostatistical logistic regression models with environmental covariates were used to predict P. falciparum prevalence for 2- to 10-year-old children (PfPR2–10) across the endemic areas of Bangladesh. The predictions were combined with gridded population data to estimate the number of individuals living in different endemicity classes. Across the endemic areas, the average PfPR2–10 was 3.8%. Environmental variables selected for prediction were vegetation cover, minimum temperature, and elevation. Model validation statistics revealed that the final Bayesian geostatistical model had good predictive ability. Risk maps generated from the model showed a heterogeneous distribution of PfPR2–10 ranging from 0.5% to 50%; 3.1 million people were estimated to be living in areas with a PfPR2–10 greater than 1%. Contemporary GIS and model-based geostatistics can be used to interpolate malaria risk in Bangladesh. Importantly, malaria risk was found to be highly varied across the endemic regions, necessitating the targeting of resources to reduce the burden in these areas.
Copyright © 2010 by The American Society of Tropical Medicine and Hygiene
Keyword Sub-saharan Africa
Plasmodium-falciparum prevalence
Forest malaria
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Official 2011 Collection
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Created: Thu, 24 Feb 2011, 10:05:20 EST by Geraldine Fitzgerald on behalf of School of Public Health