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Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros

Epidemiological data often include excess zeros. This is particularly the case for data on rare conditions, diseases that are not common in specific areas or specific time periods, and conditions and diseases that are hard to detect or on the rise. In this paper, we provide a review of methods for m...

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Detalles Bibliográficos
Autor principal: Arab, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4586626/
https://www.ncbi.nlm.nih.gov/pubmed/26343696
http://dx.doi.org/10.3390/ijerph120910536
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author Arab, Ali
author_facet Arab, Ali
author_sort Arab, Ali
collection PubMed
description Epidemiological data often include excess zeros. This is particularly the case for data on rare conditions, diseases that are not common in specific areas or specific time periods, and conditions and diseases that are hard to detect or on the rise. In this paper, we provide a review of methods for modeling data with excess zeros with focus on count data, namely hurdle and zero-inflated models, and discuss extensions of these models to data with spatial and spatio-temporal dependence structures. We consider a Bayesian hierarchical framework to implement spatial and spatio-temporal models for data with excess zeros. We further review current implementation methods and computational tools. Finally, we provide a case study on five-year counts of confirmed cases of Lyme disease in Illinois at the county level.
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spelling pubmed-45866262015-10-06 Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros Arab, Ali Int J Environ Res Public Health Review Epidemiological data often include excess zeros. This is particularly the case for data on rare conditions, diseases that are not common in specific areas or specific time periods, and conditions and diseases that are hard to detect or on the rise. In this paper, we provide a review of methods for modeling data with excess zeros with focus on count data, namely hurdle and zero-inflated models, and discuss extensions of these models to data with spatial and spatio-temporal dependence structures. We consider a Bayesian hierarchical framework to implement spatial and spatio-temporal models for data with excess zeros. We further review current implementation methods and computational tools. Finally, we provide a case study on five-year counts of confirmed cases of Lyme disease in Illinois at the county level. MDPI 2015-08-28 2015-09 /pmc/articles/PMC4586626/ /pubmed/26343696 http://dx.doi.org/10.3390/ijerph120910536 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Arab, Ali
Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros
title Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros
title_full Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros
title_fullStr Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros
title_full_unstemmed Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros
title_short Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros
title_sort spatial and spatio-temporal models for modeling epidemiological data with excess zeros
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4586626/
https://www.ncbi.nlm.nih.gov/pubmed/26343696
http://dx.doi.org/10.3390/ijerph120910536
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