The University of Southampton
University of Southampton Institutional Repository

Mapping malaria risk in Bangladesh using Bayesian geostatistical models

Mapping malaria risk in Bangladesh using Bayesian geostatistical models
Mapping malaria risk in Bangladesh using Bayesian geostatistical models
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 (PfPR(2-10)) 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 PfPR(2-10) 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 PfPR(2-10) ranging from 0.5% to 50%; 3.1 million people were estimated to be living in areas with a PfPR(2-10) 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.
bangladesh, epidemiology, bayes theorem, child, preschool, geographic information systems, humans, malaria, falciparum, epidemiology models, biological models, statistical prevalence, risk factors
0002-9637
861-867
Reid, H.
4f221910-f3f2-41bc-ab4b-66ab4b88744f
Haque, U.
eb54c58c-e50a-4e43-b812-77d96f64cc72
Clements, A.C.
26bb7239-e58c-4f3e-a5ce-97817fb85962
Tatem, A.J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Vallely, A.
46020b1f-b530-470a-981f-660f3c7462a7
Ahmed, S.M.
c70ec574-ba7b-4358-aba5-e8eed71d91d7
Islam, A.
b61a9289-c189-455d-bb7b-a1b9994541d6
Haque, R.
2ad4a18a-ac00-43d4-b81a-14004994bc3d
Reid, H.
4f221910-f3f2-41bc-ab4b-66ab4b88744f
Haque, U.
eb54c58c-e50a-4e43-b812-77d96f64cc72
Clements, A.C.
26bb7239-e58c-4f3e-a5ce-97817fb85962
Tatem, A.J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Vallely, A.
46020b1f-b530-470a-981f-660f3c7462a7
Ahmed, S.M.
c70ec574-ba7b-4358-aba5-e8eed71d91d7
Islam, A.
b61a9289-c189-455d-bb7b-a1b9994541d6
Haque, R.
2ad4a18a-ac00-43d4-b81a-14004994bc3d

Reid, H., Haque, U., Clements, A.C., Tatem, A.J., Vallely, A., Ahmed, S.M., Islam, A. and Haque, R. (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). (PMID:20889880)

Record type: Article

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 (PfPR(2-10)) 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 PfPR(2-10) 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 PfPR(2-10) ranging from 0.5% to 50%; 3.1 million people were estimated to be living in areas with a PfPR(2-10) 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.

This record has no associated files available for download.

More information

Published date: 5 October 2010
Keywords: bangladesh, epidemiology, bayes theorem, child, preschool, geographic information systems, humans, malaria, falciparum, epidemiology models, biological models, statistical prevalence, risk factors
Organisations: Geography & Environment, PHEW – P (Population Health)

Identifiers

Local EPrints ID: 344445
URI: http://eprints.soton.ac.uk/id/eprint/344445
ISSN: 0002-9637
PURE UUID: 0d6a4015-072a-4cc0-a703-931e1ef93750
ORCID for A.J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 05 Nov 2012 10:19
Last modified: 15 Mar 2024 03:43

Export record

Altmetrics

Contributors

Author: H. Reid
Author: U. Haque
Author: A.C. Clements
Author: A.J. Tatem ORCID iD
Author: A. Vallely
Author: S.M. Ahmed
Author: A. Islam
Author: R. Haque

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×