ArticleStatistical methods in medical research2012
Prospective surveillance of multivariate spatial disease data.
Article in Statistical methods in medical research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 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
- JSTMapp: A web-based joint spatiotemporal modelling and mapping application for epidemiologists.PloS one · 2025Article
- Identification of common spatial and temporal trends in the epidemiology of cattle bovine tuberculosis and human extrapulmonary and drug-resistant tuberculosis in Malawi.One health (Amsterdam, Netherlands) · 2024Article
- Multichart Schemes for Detecting Changes in Disease Incidence.Computational and mathematical methods in medicine · 2020Article
- Bayesian adaptive algorithms for locating HIV mobile testing services.BMC medicine · 2018Article
- Spatiotemporal multivariate mixture models for Bayesian model selection in disease mapping.Environmetrics · 2017Article
- A simulation study on the statistical monitoring of condemnation rates from slaughterhouses for syndromic surveillance: an evaluation based on Swiss data.Epidemiology and infection · 2015Article
- Temporal and spatial monitoring and prediction of epidemic outbreaks.IEEE journal of biomedical and health informatics · 2015Article
- A Practitioner-Driven Research Agenda for Syndromic Surveillance.Public health reports (Washington, D.C. : 1974)Article
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Abstract
Surveillance systems are often focused on more than one disease within a predefined area. On those occasions when outbreaks of disease are likely to be correlated, the use of multivariate surveillance techniques integrating information from multiple diseases allows us to improve the sensitivity and timeliness of outbreak detection. In this article, we present an extension of the surveillance conditional predictive ordinate to monitor multivariate spatial disease data. The proposed surveillance technique, which is defined for each small area and time period as the conditional predictive distribution of those counts of disease higher than expected given the data observed up to the previous time period, alerts us to both small areas of increased disease incidence and the diseases causing the alarm within each area. We investigate its performance within the framework of Bayesian hierarchical Poisson models using a simulation study. An application to diseases of the respiratory system in South Carolina is finally presented.
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