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02 November 2024 |
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Article overview
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Spatiotemporal models for Poisson areal data with an application to the AIDS epidemic in Rio de Janeiro | Marco A. R. Ferreira
; Juan C. Vivar
; | Date: |
2 Jun 2022 | Abstract: | We present a class of spatiotemporal models for Poisson areal data suitable
for the analysis of emerging infectious diseases. These models assume Poisson
observations related through a link equation to a latent random field process.
This latent random field process evolves through time with proper Gaussian
Markov random field convolutions. Our approach naturally accommodates flexible
structures such as distinct but interacting temporal trends for each region and
across-time contamination among neighboring regions. We develop a Bayesian
analysis approach with a simulation-based procedure: specifically, we construct
a Markov chain Monte Carlo algorithm based on the generalized extended Kalman
filter to obtain samples from an approximate posterior distribution. Finally,
for the comparison of Poisson spatiotemporal models, we develop a
simulation-based conditional Bayes factor. We illustrate the utility and
flexibility of our Poisson spatiotemporal framework with an application to the
number of acquired immunodeficiency syndrome (AIDS) cases during the period
1982-2007 in Rio de Janeiro. | Source: | arXiv, 2206.00869 | Services: | Forum | Review | PDF | Favorites |
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