ArticleBiostatistics (Oxford, England)2019
A Bayesian space-time model for clustering areal units based on their disease trends.
Article in Biostatistics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 24 citations in OpenAlex.
- Bayesian tele-connected spatial clustering of multivariate spatial data with applications to disease-mapping.Biometrics · 2026Article
- The impact of modifiable social determinants on tuberculosis incidence: insights from a Bayesian spatiotemporal and counterfactual analysis.BMC public health · 2026Article
- Large-scale importance of bark beetle outbreaks for standing deadwood and woodpeckers.The Journal of animal ecology · 2025Review
- Spatio-temporal monitoring of health facility-level malaria trends in Zambia and adaptive scaling for operational intervention.Communications medicine · 2022Article
- Population-weighted exposure to air pollution and COVID-19 incidence in Germany.Spatial statistics · 2021Article
- Modelling of malaria risk, rates, and trends: A spatiotemporal approach for identifying and targeting sub-national areas of high and low burden.PLoS computational biology · 2021Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Population-level disease risk across a set of non-overlapping areal units varies in space and time, and a large research literature has developed methodology for identifying clusters of areal units exhibiting elevated risks. However, almost no research has extended the clustering paradigm to identify groups of areal units exhibiting similar temporal disease trends. We present a novel Bayesian hierarchical mixture model for achieving this goal, with inference based on a Metropolis-coupled Markov chain Monte Carlo ((MC)$^3$) algorithm. The effectiveness of the (MC)$^3$ algorithm compared to a standard Markov chain Monte Carlo implementation is demonstrated in a simulation study, and the methodology is motivated by two important case studies in the United Kingdom. The first concerns the impact on measles susceptibility of the discredited paper linking the measles, mumps, and rubella vaccination to an increased risk of Autism and investigates whether all areas in the Scotland were equally affected. The second concerns respiratory hospitalizations and investigates over a 10 year period which parts of Glasgow have shown increased, decreased, and no change in risk.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.