ArticleStatistics in medicine2011
Conditional predictive inference for online surveillance of spatial disease incidence.
Article in Statistics in medicine, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A scalable variational method for estimating the latent infection-rate field of an outbreak.PloS one · 2026Article
- Detecting outbreaks using a spatial latent field.PloS one · 2025Article
- A Novel Bayesian Spatio-Temporal Surveillance Metric to Predict Emerging Infectious Disease Areas of High Disease Risk.Statistics in medicine · 2024Article
- Evaluation of Bayesian spatiotemporal infectious disease models for prospective surveillance analysis.BMC medical research methodology · 2023Article
- Bayesian spatiotemporal modeling with sliding windows to correct reporting delays for real-time dengue surveillance in Thailand.International journal of health geographics · 2020Article
- Article
- A case-association cluster detection and visualisation tool with an application to Legionnaires' disease.Statistics in medicine · 2013Article
- Prospective surveillance of multivariate spatial disease data.Statistical methods in medical research · 2012Article
- International society for disease surveillance conference 2011: building the future of public health surveillance.Emerging health threats journal · 2011Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
This paper deals with the development of statistical methodology for timely detection of incident disease clusters in space and time. The increasing availability of data on both the time and the location of events enables the construction of multivariate surveillance techniques, which may enhance the ability to detect localized clusters of disease relative to the surveillance of the overall count of disease cases across the entire study region. We introduce the surveillance conditional predictive ordinate as a general Bayesian model-based surveillance technique that allows us to detect small areas of increased disease incidence when spatial data are available. To address the problem of multiple comparisons, we incorporate a common probability that each small area signals an alarm when no change in the risk pattern of disease takes place into the analysis. We investigate the performance of the proposed surveillance technique within the framework of Bayesian hierarchical Poisson models using a simulation study. Finally, we present a case study of salmonellosis in South Carolina.
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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.