Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/46506
Title: Assessing the impact of neighborhood structures in Bayesian disease mapping
Authors: NGUYEN, Minh Hanh 
NEYENS, Thomas 
LAWSON, Andrew 
FAES, Christel 
Issue Date: 2025
Publisher: TAYLOR & FRANCIS LTD
Source: Journal of applied statistics,
Status: Early view
Abstract: In Bayesian disease mapping, defining the neighborhood structure is crucial when fitting the conditional auto-regressive model. Yet, there has been little assessment of how different structures affect the model performance in case of fine-scale data. This paper explores this gap. In a case study examining COVID-19 pandemic effects, 2020 mortality is contrasted with pre-pandemic rates in small areas in Limburg (Belgium). Data are modeled using BYM and BYM2, with three broadening queen-neighborhood structures up to the fifth-order neighbors and two weight schemes. A simulation study assesses model performance in reproducing the pairwise spatial correlation at different neighbor orders. Models are compared regarding WAIC, goodness-of-fit, parameter estimates, and computation time. Results show that the order-based weight matrix performs better than the binary matrix. The simple first-order neighborhood structure shows comparable performance to larger higher-order structures while requiring much less computation time. The BYM model is more impacted by the choice of the neighborhood as compared to the BYM2 model. Our findings suggest minimal advantages in employing higher-order neighborhood matrices. In conclusion, our study indicates that opting for a simple first-order neighborhood structure is a pragmatic and suitable choice when applying a conditional auto-regressive model to fine-scale data in Bayesian disease mapping.
Notes: Nguyen, MH (corresponding author), Hasselt Univ, Data Sci Inst, I BioStat, Hasselt, Belgium.; Nguyen, MH (corresponding author), Ind Univ Ho Chi Minh City, Fac Informat Technol, Ho Chi Minh City, Vietnam.
minhhanh.nguyen@uhasselt.be
Keywords: Disease mapping;conditional auto-regressive model;neighborhood structure;fine-scale data
Document URI: http://hdl.handle.net/1942/46506
ISSN: 0266-4763
e-ISSN: 1360-0532
DOI: 10.1080/02664763.2025.2533479
ISI #: 001533290100001
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

Show full item record

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.