Mapping malaria risk in West Africa using a Bayesian nonparametric non-stationary model
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| Title: | Mapping malaria risk in West Africa using a Bayesian nonparametric non-stationary model |
|---|---|
| Authors: | Gosoniu, L.1 laura.gosoniu@unibas.ch, Vounatsou, P.1, Sogoba, N.2, Maire, N.1, Smith, T.1 |
| Source: | Computational Statistics & Data Analysis. Jul2009, Vol. 53 Issue 9, p3358-3371. 14p. |
| Subject Terms: | Risk of malaria, Disease mapping, Mathematical models in medicine, Bayesian analysis, Nonparametric statistics, Stationary processes |
| Geographic Terms: | West Africa |
| Abstract: | 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. [Copyright &y& Elsevier] |
| Copyright of Computational Statistics & Data Analysis is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
| FullText | Text: Availability: 0 |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 38317437 AccessLevel: 6 PubType: Periodical PubTypeId: serialPeriodical PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Mapping malaria risk in West Africa using a Bayesian nonparametric non-stationary model – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gosoniu%2C+L%2E%22">Gosoniu, L.</searchLink><relatesTo>1</relatesTo><i> laura.gosoniu@unibas.ch</i><br /><searchLink fieldCode="AR" term="%22Vounatsou%2C+P%2E%22">Vounatsou, P.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Sogoba%2C+N%2E%22">Sogoba, N.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Maire%2C+N%2E%22">Maire, N.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Smith%2C+T%2E%22">Smith, T.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: Computational Statistics & Data Analysis. Jul2009, Vol. 53 Issue 9, p3358-3371. 14p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Risk+of+malaria%22">Risk of malaria</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+mapping%22">Disease mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models+in+medicine%22">Mathematical models in medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Nonparametric+statistics%22">Nonparametric statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Stationary+processes%22">Stationary processes</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22West+Africa%22">West Africa</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computational Statistics & Data Analysis is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.csda.2009.02.022 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 3358 Subjects: – SubjectFull: Risk of malaria Type: general – SubjectFull: Disease mapping Type: general – SubjectFull: Mathematical models in medicine Type: general – SubjectFull: Bayesian analysis Type: general – SubjectFull: Nonparametric statistics Type: general – SubjectFull: Stationary processes Type: general – SubjectFull: West Africa Type: general Titles: – TitleFull: Mapping malaria risk in West Africa using a Bayesian nonparametric non-stationary model Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gosoniu, L. – PersonEntity: Name: NameFull: Vounatsou, P. – PersonEntity: Name: NameFull: Sogoba, N. – PersonEntity: Name: NameFull: Maire, N. – PersonEntity: Name: NameFull: Smith, T. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2009 Type: published Y: 2009 Identifiers: – Type: issn-print Value: 01679473 Numbering: – Type: volume Value: 53 – Type: issue Value: 9 Titles: – TitleFull: Computational Statistics & Data Analysis Type: main |
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