Bayesian Nonparametric Modeling for Disease Incidence Data

Authored by: Athanasios Kottas

Handbook of Spatial Epidemiology

Print publication date:  April  2016
Online publication date:  April  2016

Print ISBN: 9781482253016
eBook ISBN: 9781482253023
Adobe ISBN:

10.1201/b19470-25

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Abstract

Disease incidence or mortality data are routinely recorded as summary counts for contiguous geographical regions (e.g., census tracts, zip codes, districts, or counties) and collected over discrete time periods. The count responses are typically accompanied by covariate information associated with the region (e.g., median family income or percent with a specific type of education), and occasionally, by covariate information associated with each incidence case (e.g., sex, race, or age), even though we only know the region into which the case falls. A key inferential objective in the analysis of disease incidence data is identification and explanation of spatial and spatiotemporal patterns of disease risk (disease mapping). Also of interest is forecasting of disease risk.

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