ArticleSpatial statistics2022
Bayesian disease mapping: Past, present, and future.
Article in Spatial statistics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Spatial heterogeneity in area-level environmental context of hospital-based congenital structural anomaly burden in Southwest China: a retrospective study from a provincial pediatric referral center.World journal of pediatrics : WJP · 2026Article
- Spatiotemporal Approaches to Assess the Association of Environmental Risk Factors With Cardiovascular Diseases: A Scoping Review.GeoHealth · 2026Review
- The effect of recurrent mobility on air pollution exposure and mortality burden in Catalonia.International journal of health geographics · 2025Article
- High-risk human papillomavirus cervical infection prevalence: a nationwide retrospective study comparing opportunistic and organised screening, France, 2020 to 2023.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2025Article
- Identifying Rural Hotspots for Head and Neck Cancer Using the Bayesian Mapping Approach.Cancers · 2025Article
- Detecting outbreaks using a spatial latent field.PloS one · 2025Article
- Bayesian spatiotemporal evaluation of bovine anaplasmosis seroprevalence in Missouri (2010-2021).Frontiers in veterinary science · 2025Article
- A municipality-specific analysis to investigate persistent increased incidence rates of childhood leukaemia near the nuclear power plant of Krümmel in Germany.European journal of epidemiology · 2024Article
- Pesticides and risk of pancreatic adenocarcinoma in France: a nationwide spatiotemporal ecological study between 2011 and 2021.European journal of epidemiology · 2024Article
- Spatial Epidemiology and Its Role in Prevention and Control of Swine Viral Disease.Animals : an open access journal from MDPI · 2024Review
- Factors associated with the spatiotemporal distribution of dog rabies in Tunisia.PLoS neglected tropical diseases · 2024Article
- The epidemiologic case for urban health: conceptualizing and measuring the magnitude of challenges and potential benefits.F1000Research · 2024Review
- Adaptive Gaussian Markov random field spatiotemporal models for infectious disease mapping and forecasting.Spatial statistics · 2023Article
- Revisiting Gaussian Markov random fields and Bayesian disease mapping.Statistical methods in medical research · 2023Review
- Near real time monitoring and forecasting for COVID-19 situational awareness.Applied geography (Sevenoaks, England) · 2022Article
- A Bayesian modelling framework to quantify multiple sources of spatial variation for disease mapping.Journal of the Royal Society, Interface · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
On the occasion of the Spatial Statistics' 10th Anniversary, I reflect on the past and present of Bayesian disease mapping and look into its future. I focus on some key developments of models, and on recent evolution of multivariate and adaptive Gaussian Markov random fields and their impact and importance in disease mapping. I reflect on Bayesian disease mapping as a subject of spatial statistics that has advanced to date, and continues to grow, in scope and complexity alongside increasing needs of analytic tools for contemporary health science research, such as spatial epidemiology, population and public health, and medicine. I illustrate (potential) utility and impact of some of the disease mapping models and methods for analysing and monitoring communicable disease such as the COVID-19 infection risks during an ongoing pandemic.
Indexed as
Identifiers
What OpenQuestion holds
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.