ArticleInternational journal of environmental research and public health2019
Bayesian Spatial Joint Model for Disease Mapping of Zero-Inflated Data with R-INLA: A Simulation Study and an Application to Male Breast Cancer in Iran.
Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research.International journal of environmental research and public health · 2023Pooled it
- Exploring the impacts of wildlife trade on zoonotic spillover and disease outbreaks: A multiple-perspective study.One health (Amsterdam, Netherlands) · 2026Article
- Spatial Analysis of Lung Cancer Mortality and Its Potential Risk Factors: A Regional Study in Kansas.Preventive oncology & epidemiology · 2026Article
- Spatial and temporal modeling of breast cancer mortality in Kansas: An R-INLA approach.PloS one · 2026Article
- Spatiotemporal Epidemiology of Female Thyroid Cancer: A Retrospective Population-Based Registry Study in the Hamadan Province, Iran (2010-2019).Archives of Iranian medicine · 2025Article
- Spatial bayesian modeling of diabetes mellitus (DM) risk in the United States.BMC public health · 2025Article
- Distribution and Risk Factors of Scrub Typhus in South Korea, From 2013 to 2019: Bayesian Spatiotemporal Analysis.JMIR public health and surveillance · 2025Article
- Spatiotemporal Mapping of Colorectal and Gastric Cancer Incidence in Hamadan Province, Western Iran (2010-2019).Journal of research in health sciences · 2025Article
- The association between living environment and out-of-hospital cardiac arrest risk in adults: the perspective of daily-life contexts.Archives of public health = Archives belges de sante publique · 2025Article
- Historical Redlining and Community-Reported Housing Quality: A Spatial Analysis.Journal of urban health : bulletin of the New York Academy of Medicine · 2025Article
- Bayesian spatial modelling of localised SARS-CoV-2 transmission through mobility networks across England.PLoS computational biology · 2023Article
- A time-series approach to mapping livestock density using household survey data.Scientific reports · 2022Article
- Effectiveness of controlling COVID-19 epidemic by implementing soft lockdown policy and extensive community screening in Taiwan.Scientific reports · 2022Article
- Geospatial modeling of pre-intervention nodule prevalence of Onchocerca volvulus in Ethiopia as an aid to onchocerciasis elimination.PLoS neglected tropical diseases · 2022Article
- Accessibility to rabies centers and human rabies post-exposure prophylaxis rates in Cambodia: A Bayesian spatio-temporal analysis to identify optimal locations for future centers.PLoS neglected tropical diseases · 2022Article
- Invited Commentary: Predicting Incidence Rates of Rare Cancers-Adding Epidemiologic and Spatial Contexts.American journal of epidemiology · 2022Article
- Small area disease mapping of cancer incidence in British Columbia using Bayesian spatial models and the smallareamapp R Package.Frontiers in oncology · 2022Article
- Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina.International journal of environmental research and public health · 2021Article
- Monitoring the COVID-19 epidemic with nationwide telecommunication data.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Population Mobility Trends, Deprivation Index and the Spatio-Temporal Spread of Coronavirus Disease 2019 in Ireland.International journal of environmental research and public health · 2021Article
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4 authors.
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Abstract
Hierarchical Bayesian log-linear models for Poisson-distributed response data, especially Besag, York and Mollié (BYM) model, are widely used for disease mapping. In some cases, due to the high proportion of zero, Bayesian zero-inflated Poisson models are applied for disease mapping. This study proposes a Bayesian spatial joint model of Bernoulli distribution and Poisson distribution to map disease count data with excessive zeros. Here, the spatial random effect is simultaneously considered into both logistic and log-linear models in a Bayesian hierarchical framework. In addition, we focus on the BYM2 model, a re-parameterization of the common BYM model, with penalized complexity priors for the latent level modeling in the joint model and zero-inflated Poisson models with different type of zeros. To avoid model fitting and convergence issues, Bayesian inferences are implemented using the integrated nested Laplace approximation (INLA) method. The models are compared according to the deviance information criterion and the logarithmic scoring. A simulation study with different proportions of zero exhibits INLA ability in running the models and also shows slight differences between the popular BYM and BYM2 models in terms of model choice criteria. In an application, we apply the fitting models on male breast cancer data in Iran at county level in 2014.
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