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; Haque, Rashidul

doi:10.4269/ajtmh.2010.10-0154

Cited by 43 publications

(40 citation statements)

References 43 publications

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“…1). Following baseline entomological surveys to describe the distribution of the vector Anopheles farauti (The PacMI Survey Group on behalf of the Ministries of Health of Vanuatu and Solomon Islands, 2010) and geo-statistical analysis of malaria survey data on the main island of Tanna in 2008 (Reid et al, 2010), focal IRS operational areas on Tanna was defined as all settlements located within a 2 km boundary of the coastline. As part of the Tafea province elimination strategy, three annual rounds of IRS were planned for the Tanna focal IRS zone between 2009 and 2011.…”

Section: Study Areamentioning

confidence: 99%

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A spatial decision support system for guiding focal indoor residual spraying interventions in a malaria elimination zone

Kelly

Seng

Donald³

et al. 2011

Geospat Health

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Abstract.A customized geographical information system (GIS) has been developed to support focal indoor residual spraying (IRS) operations as part of a scaled-up campaign to progressively eliminate malaria in Vanuatu. The aims of the GISbased spatial decision support system (SDSS) were to guide the planning, implementation and assessment of IRS at the household level. Additional aims of this study were to evaluate the user acceptability of a SDSS guiding IRS interventions. IRS was conducted on Tanna Island, Republic of Vanuatu between 26 October and 5 December 2009. Geo-referenced household information provided a baseline within the SDSS. An interactive mapping interface was used to delineate operation areas, extract relevant data to support IRS field teams. In addition, it was used as a monitoring tool to assess overall intervention coverage. Surveys and group discussions were conducted during the operations to ascertain user acceptability. Twenty-one operation areas, comprising a total of 187 settlements and 3,422 households were identified and mapped. A total of 3,230 households and 12,156 household structures were sprayed, covering a population of 13,512 individuals, achieving coverage of 94.4% of the households and 95.7% of the population. Village status maps were produced to visualize the distribution of IRS at the sub-village level. One hundred percent of survey respondents declared the SDSS a useful and effective tool to support IRS. The GIS-based SDSS adopted in Tanna empowered programme managers at the provincial level to implement and asses the IRS intervention with the degree of detail required for malaria elimination. Since completion, SDSS applications have expanded to additional provinces in Vanuatu and the neighbouring Solomon Islands supporting not only specific malaria elimination and control interventions, but also the broader public health sector in general.

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Section: Study Areamentioning

confidence: 99%

“…Lozano-Fuentes et al, 2008;Clements et al, 2009;Daash et al, 2009;Reid et al, 2010). In the context of malaria elimination, the need for modernized, high-resolution mapping to support the operational management of scaled-up interventions is also recognized (The malEra Consultative Group on Monitoring Evaluation and Surveillance, 2011).…”

Section: Introductionmentioning

confidence: 99%

A spatial decision support system for guiding focal indoor residual spraying interventions in a malaria elimination zone

Kelly

Seng

Donald³

et al. 2011

Geospat Health

Self Cite

View full text Add to dashboard Cite

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“…In addition, both timely and accurate diagnosis as well as the benefits of testing before commencement of malaria treatment should be emphasized. Regularly updated malaria risk maps (Haque et al, 2010b;Reid et al, 2010Reid et al, , 2012Haque et al, 2011;Wang et al, 2014) can help the NMCC to monitor progress and mobilize resources. Distance, population density, treatment seeking behavior and other health facilities should be considered (Ahmed et al, 2009;Haque et al, 2012;Simon et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Diagnostic approaches to malaria in Zambia, 2009-2014

et al. 2015

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Malaria is an important health burden in Zambia with proper diagnosis remaining as one of the biggest challenges. The need for reliable diagnostics is being addressed through the introduction of rapid diagnostic tests (RDTs). However, without sufficient laboratory amenities in many parts of the country, diagnosis often still relies on non-specific, clinical symptoms. In this study, geographical information systems were used to both visualize and analyze the spatial distribution and the risk factors related to the diagnosis of malaria. The monthly reported, district-level number of malaria cases from January 2009 to December 2014 were collected from the National Malaria Control Center (NMCC). Spatial statistics were used to reveal cluster tendencies that were subsequently linked to possible risk factors, using a non-spatial regression model. Significant, spatio-temporal clusters of malaria were spotted while the introduction of RDTs made the number of clinically diagnosed malaria cases decrease by 33% from 2009 to 2014. The limited access to road network(s) was found to be associated with higher levels of malaria, which can be traced by the expansion of health promotion interventions by the NMCC, indicating enhanced diagnostic capability. The capacity of health facilities has been strengthened with the increased availability of proper diagnostic tools and through retraining of community health workers. To further enhance spatial decision support systems, a multifaceted approach is required to ensure mobilization and availability of human, infrastructural and technological resources. Surveillance based on standardized geospatial or other analytical methods should be used by program managers to design, target, monitor and assess the spatio-temporal dynamics of malaria diagnostic resources country-wide.

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“…In another paper 8 , the authors concluded that the dynamics and seasonal abundance of malaria vectors in the Kintampo area of Ghana were influenced by microecology, rainfall, and temperature patterns. Reid et al 9 considered the use of Bayesian geostatistical models in mapping the malaria risk in Bangladesh. In all these studies, it is important to point out that the presence of different socioeconomic factors could have had great impact on the spread of malaria during that time.…”

Section: Methodsmentioning

confidence: 99%

Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

Achcar

Martinez

Souza

et al. 2011

Rev. Soc. Bras. Med. Trop.

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Introduction: Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities. The objective of this article was to investigate the association between environmental variables and the yearly registers of malaria in the Amazon region using Bayesian spatiotemporal methods. Methods: We used Poisson spatiotemporal regression models to analyze the Brazilian Amazon forest malaria count for the period from 1999 to 2008. In this study, we included some covariates that could be important in the yearly prediction of malaria, such as deforestation rate. We obtained the inferences using a Bayesian approach and Markov Chain Monte Carlo (MCMC) methods to simulate samples for the joint posterior distribution of interest. The discrimination of different models was also discussed. Results: The model proposed here suggests that deforestation rate, the number of inhabitants per km 2 , and the human development index (HDI) are important in the prediction of malaria cases. Conclusions: It is possible to conclude that human development, population growth, deforestation, and their associated ecological alterations are conducive to increasing malaria risk. We conclude that the use of Poisson regression models that capture the spatial and temporal effects under the Bayesian paradigm is a good strategy for modeling malaria counts.

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Mapping Malaria Risk in Bangladesh Using Bayesian Geostatistical Models

Cited by 43 publications

References 43 publications

A spatial decision support system for guiding focal indoor residual spraying interventions in a malaria elimination zone

A spatial decision support system for guiding focal indoor residual spraying interventions in a malaria elimination zone

Diagnostic approaches to malaria in Zambia, 2009-2014

Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

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