ArticleBMC medical informatics and decision making2009
Disease surveillance using a hidden Markov model.
Article in BMC medical informatics and decision making, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Article
- Epidemiological cluster identification using multiple data sources: an approach using logistic regression.Microbial genomics · 2023Article
- Hidden variable models reveal the effects of infection from changes in host survival.PLoS computational biology · 2023Article
- Early Warning of Infectious Diseases in Hospitals Based on Multi-Self-Regression Deep Neural Network.Journal of healthcare engineering · 2022Article
- Supervised learning using routine surveillance data improves outbreak detection of Salmonella and Campylobacter infections in Germany.PloS one · 2022Article
- Article
- Bayesian spatiotemporal modeling with sliding windows to correct reporting delays for real-time dengue surveillance in Thailand.International journal of health geographics · 2020Article
- Spatiotemporal signal detection using continuous shrinkage priors.Statistics in medicine · 2020Article
- Visual tools to assess the plausibility of algorithm-identified infectious disease clusters: an application to mumps data from the Netherlands dating from January 2009 to June 2016.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2019Article
- Improving regional influenza surveillance through a combination of automated outbreak detection methods: the 2015/16 season in France.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2017Article
- A method for detecting and characterizing outbreaks of infectious disease from clinical reports.Journal of biomedical informatics · 2015Article
- A case-association cluster detection and visualisation tool with an application to Legionnaires' disease.Statistics in medicine · 2013Article
- Finding evidence for local transmission of contagious disease in molecular epidemiological datasets.PloS one · 2013Article
- Prospective surveillance of multivariate spatial disease data.Statistical methods in medical research · 2012Article
- Epidemic features affecting the performance of outbreak detection algorithms.BMC public health · 2012Article
- Tuberculosis surveillance using a hidden markov model.Iranian journal of public health · 2012Article
- Conditional predictive inference for online surveillance of spatial disease incidence.Statistics in medicine · 2011Article
- Attempted early detection of influenza A (H1N1) pandemic with surveillance data of influenza-like illness and unexplained pneumonia.Influenza and other respiratory viruses · 2011Article
- A hidden Markov model for analysis of frontline veterinary data for emerging zoonotic disease surveillance.PloS one · 2011Article
- Probabilistic, Decision-theoretic Disease Surveillance and Control.Online journal of public health informatics · 2011Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRoutine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs) have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable for use with sparse small area count data and requires little baseline data.
methodsA Bayesian HMM was designed to monitor routinely collected notifiable disease data that are aggregated by residential postcode. Semi-synthetic data were used to evaluate the algorithm and compare outbreak detection performance with the established Early Aberration Reporting System (EARS) algorithms and a negative binomial cusum.
resultsAlgorithm performance varied according to the desired false alarm rate for surveillance. At false alarm rates around 0.05, the cusum-based algorithms provided the best overall outbreak detection performance, having similar sensitivity to the HMMs and a shorter average time to detection. At false alarm rates around 0.01, the HMM algorithms provided the best overall outbreak detection performance, having higher sensitivity than the cusum-based Methods and a generally shorter time to detection for larger outbreaks. Overall, the 14-day HMM had a significantly greater area under the receiver operator characteristic curve than the EARS C3 and 7-day negative binomial cusum algorithms.
conclusionOur findings suggest that the HMM provides an effective method for the surveillance of sparse small area notifiable disease data at low false alarm rates. Further investigations are required to evaluation algorithm performance across other diseases and surveillance contexts.
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