Next Article in Journal
Effective Strategies for Monitoring and Regulating Chemical Mixtures and Contaminants Sharing Pathways of Toxicity
Next Article in Special Issue
A Bayesian Approach to Account for Misclassification and Overdispersion in Count Data
Previous Article in Journal
The Experiences of Tobacco Use among South-Western Taiwanese Adolescent Males
Previous Article in Special Issue
A Simulation Study of Categorizing Continuous Exposure Variables Measured with Error in Autism Research: Small Changes with Large Effects
Article Menu

Export Article

Open AccessReview
Int. J. Environ. Res. Public Health 2015, 12(9), 10536-10548; doi:10.3390/ijerph120910536

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
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)
View Full-Text   |   Download PDF [1694 KB, uploaded 28 August 2015]   |  

Abstract

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)
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Arab, A. Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros. Int. J. Environ. Res. Public Health 2015, 12, 10536-10548.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Int. J. Environ. Res. Public Health EISSN 1660-4601 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top