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

Department of Mathematics and Statistics, Georgetown University, 37th and O streets, Washington, DC 20057, USA
Academic Editors: Igor Burstyn and Gheorghe Luta
Int. J. Environ. Res. Public Health 2015, 12(9), 10536-10548; https://doi.org/10.3390/ijerph120910536
Received: 4 July 2015 / Revised: 19 August 2015 / Accepted: 21 August 2015 / Published: 28 August 2015
(This article belongs to the Special Issue Methodological Innovations and Reflections-1)
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. View Full-Text
Keywords: spatio-temporal models; spatial models; hierarchical modeling; Bayesian analysis; zero-inflated models; hurdle models; Integrated Nested Laplace Approximation (INLA) spatio-temporal models; spatial models; hierarchical modeling; Bayesian analysis; zero-inflated models; hurdle models; Integrated Nested Laplace Approximation (INLA)
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Arab, A. Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros. Int. J. Environ. Res. Public Health 2015, 12, 10536-10548.

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