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

International Journal of Environmental Research and Public Health

Ali Arab¹  Igor Burstyn¹ 

[1] Department of Mathematics and Statistics, Georgetown University, 37th and O streets, Washington, DC 20057, USA; E-Mail

关键词: spatio-temporal models;    spatial models;    hierarchical modeling;    Bayesian analysis;    zero-inflated models;    hurdle models;    Integrated Nested Laplace Approximation (INLA);   

DOI  :  10.3390/ijerph120910536

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.