Joint disease mapping for bivariate count data with residual correlation due to unknown number of common cases Article Swipe
Édouard Chatignoux
,
Zoé Uhry
,
Laurent Remontet
,
Isabelle Albert
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/biomtc/ujaf119
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/biomtc/ujaf119
The joint spatial distribution of two count outcomes (eg, counts of two diseases) is usually studied using a Poisson shared component model (P-SCM), which uses geographically structured latent variables to model spatial variations that are specific and shared by both outcomes. In this model, the correlation between the outcomes is assumed to be fully accounted for by the latent variables. However, in this article, we show that when the outcomes have an unknown number of cases in common, the bivariate counts exhibit a positive “residual” correlation, which the P-SCM wrongly attributes to the covariance of the latent variables, leading to biased inference and degraded predictive performance. Accordingly, we propose a new SCM based on the Bivariate-Poisson distribution (BP-SCM hereafter) to study such correlated bivariate data. The BP-SCM decomposes each count into counts of common and distinct cases, and then models each of these three counts (two distinct and one common) using Gaussian Markov Random Fields. The model is formulated in a Bayesian framework using Hamiltonian Monte Carlo inference. Simulations and a real-world application showed the good inferential and predictive performances of the BP-SCM and confirm the bias in P-SCM. BP-SCM provides rich epidemiological information, such as the mean levels of the unknown counts of common and distinct cases, and their shared and specific spatial variations.
Related Topics
Concepts
Bivariate analysis
Statistics
Mathematics
Joint probability distribution
Poisson distribution
Count data
Correlation
Latent variable
Markov chain Monte Carlo
Covariance
Multivariate normal distribution
Bayesian inference
Random effects model
Residual
Inference
Bayesian probability
Econometrics
Multivariate statistics
Computer science
Artificial intelligence
Algorithm
Medicine
Internal medicine
Geometry
Meta-analysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/biomtc/ujaf119
- OA Status
- green
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413889532
All OpenAlex metadata
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https://openalex.org/W4413889532Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/biomtc/ujaf119Digital Object Identifier
- Title
-
Joint disease mapping for bivariate count data with residual correlation due to unknown number of common casesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-07-03Full publication date if available
- Authors
-
Édouard Chatignoux, Zoé Uhry, Laurent Remontet, Isabelle AlbertList of authors in order
- Landing page
-
https://doi.org/10.1093/biomtc/ujaf119Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://hal.inrae.fr/hal-05233948Direct OA link when available
- Concepts
-
Bivariate analysis, Statistics, Mathematics, Joint probability distribution, Poisson distribution, Count data, Correlation, Latent variable, Markov chain Monte Carlo, Covariance, Multivariate normal distribution, Bayesian inference, Random effects model, Residual, Inference, Bayesian probability, Econometrics, Multivariate statistics, Computer science, Artificial intelligence, Algorithm, Medicine, Internal medicine, Geometry, Meta-analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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17Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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