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dc.contributor.author
Osei, Frank Badu
dc.contributor.author
Stein, Alfred
dc.contributor.author
Andreo, Verónica Carolina
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dc.date.available
2023-02-13T11:10:47Z
dc.date.issued
2022-04
dc.identifier.citation
Osei, Frank Badu; Stein, Alfred; Andreo, Verónica Carolina; A zero-inflated mixture spatially varying coefficient modeling of cholera incidences; Elsevier; Spatial Statistics; 48; 4-2022; 1-19
dc.identifier.issn
2211-6753
dc.identifier.uri
http://hdl.handle.net/11336/187707
dc.description.abstract
Spatial disease modeling remains an important public health tool. For cholera, the presence of zero counts is common. The Poisson model is inadequate to (1) capture over-dispersion, and (2) distinguish between excess zeros arising from non-susceptible and susceptible populations. In this study, we develop zero-inflated (ZI) mixture spatially varying coefficient (SVC) models to (1) distinguish between the sources of the excess zeros and (2) uncover the spatially varying effects of precipitation and temperature (LST) on cholera. We demonstrate the potential of the models using cholera data from Ghana. A striking observation is that the Poisson model outperformed the ZI mixture models in terms of fit. The ZI Negative Binomial (ZINB) outperformed the ZI Poisson (ZIP) model. Subject to our objectives, we make inferences using the ZINB model. The proportion of zeros estimated with the ZINB model is 0.41 and exceeded what would have been estimated using a Poisson model which is 0.35. We observed the spatial trends of the effects of precipitation and LST to have both increasing and decreasing gradients; an observation implying that the use of only the global coefficients would lead to wrong inferences. We conclude that (1) the use of ZI mixture models has epidemiological significance. Therefore, its choice over the Poisson model should be based on an epidemiological concept rather than model fit and, (2) the extension of ZI mixture models to accommodate spatially varying coefficients uncovered remarkable varying effects of the covariates. These findings have significant implications for public health monitoring of cholera.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
BAYESIAN
dc.subject
CHOLERA
dc.subject
POISSON
dc.subject
SPATIALLY VARYING COEFFICIENTS
dc.subject
ZERO-INFLATED NEGATIVE BINOMIAL
dc.subject
ZERO-INFLATED POISSON
dc.subject.classification
Estadística y Probabilidad
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dc.subject.classification
Matemáticas
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.subject.classification
Epidemiología
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dc.subject.classification
Ciencias de la Salud
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dc.subject.classification
CIENCIAS MÉDICAS Y DE LA SALUD
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dc.title
A zero-inflated mixture spatially varying coefficient modeling of cholera incidences
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-02-09T15:32:33Z
dc.journal.volume
48
dc.journal.pagination
1-19
dc.journal.pais
Países Bajos
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dc.description.fil
Fil: Osei, Frank Badu. University Of Twente; Países Bajos
dc.description.fil
Fil: Stein, Alfred. University Of Twente; Países Bajos
dc.description.fil
Fil: Andreo, Verónica Carolina. Comision Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales "Mario Gulich"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba; Argentina
dc.journal.title
Spatial Statistics
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S2211675322000240
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.spasta.2022.100635
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