ArticleBioMed research international2015
A Bayesian Outbreak Detection Method for Influenza-Like Illness.
Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Evaluation of the Automated and Early Detection of Seasonal Epidemic Onset and Burden Levels (AEDSEO) method for respiratory surveillance using data from 21 European countries.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2026Article
- Synthetic data in health care: A narrative review.PLOS digital health · 2023Article
- [Early alert of COVID-19 outbreaks to plan the response in the IMSS].Revista medica del Instituto Mexicano del Seguro Social · 2022Article
- A novel data-driven methodology for influenza outbreak detection and prediction.Scientific reports · 2021Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epidemic outbreak detection is an important problem in public health and the development of reliable methods for outbreak detection remains an active research area. In this paper we introduce a Bayesian method to detect outbreaks of influenza-like illness from surveillance data. The rationale is that, during the early phase of the outbreak, surveillance data changes from autoregressive dynamics to a regime of exponential growth. Our method uses Bayesian model selection and Bayesian regression to identify the breakpoint. No free parameters need to be tuned. However, historical information regarding influenza-like illnesses needs to be incorporated into the model. In order to show and discuss the performance of our method we analyze synthetic, seasonal, and pandemic outbreak data.
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