Bayesian disease mapping: Past, present, and future

• Published in: Spatial statistics Vol. 50; p. 100593
• Main Author: MacNab, Ying C.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.08.2022
• Subjects: (Adaptive) Conditional autoregressive models; (Multidimensional) Gaussian Markov random fields; Bayesian disease mapping; Bayesian hierarchical models; Empirical Bayes; Linear coregionalization; Empirical Bayes; (Adaptive) Conditional autoregressive models; Bayesian hierarchical models; Bayesian disease mapping; Linear coregionalization; (Multidimensional) Gaussian Markov random fields
• ISSN: 2211-6753; 2211-6753
• DOI: 10.1016/j.spasta.2022.100593

On the occasion of the Spatial Statistics’ 10th Anniversary, I reflect on the past and present of Bayesian disease mapping and look into its future. I focus on some key developments of models, and on recent evolution of multivariate and adaptive Gaussian Markov random fields and their impact and importance in disease mapping. I reflect on Bayesian disease mapping as a subject of spatial statistics that has advanced to date, and continues to grow, in scope and complexity alongside increasing needs of analytic tools for contemporary health science research, such as spatial epidemiology, population and public health, and medicine. I illustrate (potential) utility and impact of some of the disease mapping models and methods for analysing and monitoring communicable disease such as the COVID-19 infection risks during an ongoing pandemic.