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

International Journal of Environmental Research and Public Health
Ali Arab

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.

References

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Citations

Mar 8, 2018·PLoS Computational Biology·Elizabeth C LeeShweta Bansal
Jan 31, 2019·International Journal of Environmental Health Research·Jahidur Rahman Khan, K Shuvo Bakar
May 29, 2018·Statistics in Medicine·Francisca Corpas-BurgosMiguel A Martinez-Beneito
Mar 14, 2019·Frontiers in Veterinary Science·Gianluca BooAndreas Pospischil
Jan 3, 2020·Animal Health Research Reviews·Samira Yousefi NaghaniShayan Sharif
Mar 1, 2017·Frontiers in Veterinary Science·Guillaume Le Loc'hMathilde C Paul
Sep 17, 2020·Scientific Reports·Thomas SpoonerMatthew Baylis
Nov 27, 2019·International Journal of Environmental Research and Public Health·Naeimehossadat AsmarianNajaf Zare
May 18, 2021·Frontiers in Public Health·Julien GraveleauGuillaume Constantin De Magny
Jul 3, 2021·International Journal of Environmental Research and Public Health·Jamie M MaddenGuy McGrath

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Software Mentioned

Windows
MATLAB
JAGS
GeoBUGS
WinBUGS
R
INLA
BUGS
OpenBUGS
SPDE

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