Bayesian joint spatio-temporal analysis of multiple diseases
In this paper we propose a Bayesian hierarchical spatio-temporal model for the joint analysis of multiple diseases which includes specific and shared spatial and temporal effects. Dependence on shared terms is controlled by disease-specific weights so that their posterior distribution can be used to...
| Authors: | , , , , |
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| Format: | article |
| Publication Date: | 2019 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/178513 |
| Online Access: | https://hdl.handle.net/2117/178513 |
| Access Level: | Open access |
| Keyword: | Bayesian modelling Joint modelling Multivariate disease mapping Shared components. Spatio-temporal epidemiology Classificació AMS::62 Statistics::62F Parametric inference Classificació AMS::62 Statistics::62H Multivariate analysis Classificació AMS::62 Statistics::62M Inference from stochastic processes Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
| Summary: | In this paper we propose a Bayesian hierarchical spatio-temporal model for the joint analysis of multiple diseases which includes specific and shared spatial and temporal effects. Dependence on shared terms is controlled by disease-specific weights so that their posterior distribution can be used to identify diseases with similar spatial and temporal patterns. The model proposed here has been used to study three different causes of death (oral cavity, esophagus and stomach cancer) in Spain at the province level. Shared and specific spatial and temporal effects have been estimated and mapped in order to study similarities and differences among these causes. Furthermore, estimates using Markov chain Monte Carlo and the integrated nested Laplace approximation are compared. |
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