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 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.
Department of Mathematics and Statistics, Georgetown University, 37th and O streets, Washington, DC 20057, USA. ali.arab@georgetown.edu.
Ali
A
Arab
D001499:Bayes Theorem; D004812:Epidemiologic Methods; D006801:Humans; D007087:Illinois / Q000453:epidemiology; D008193:Lyme Disease / Q000453:epidemiology* / Q000382:microbiology; D008962:Models, Theoretical; D062206:Spatial Analysis; D013997:Time Factors
Bayesian analysis; Integrated Nested Laplace Approximation (INLA); hierarchical modeling; hurdle models; spatial models; spatio-temporal models; zero-inflated models