ArticleJMIR public health and surveillance2024
A Bayesian System to Detect and Track Outbreaks of Influenza-Like Illnesses Including Novel Diseases: Algorithm Development and Validation.
Article in JMIR public health and surveillance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2024Pooled it
- An evaluation of a Bayesian method to track outbreaks of known and novel influenza-like illnesses.Scientific reports · 2026Article
- Integrating Surveillance and Stakeholder Insights to Predict Influenza Epidemics: A Bayesian Network Study in Queensland, Australia.International journal of environmental research and public health · 2026Article
- Standardization of case definition and development of early-warning model for acute respiratory infection syndromes based on Yinzhou Regional Health Information Platform.Frontiers in public health · 2025Article
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6 authors.
Funding
Abstract
backgroundThe early identification of outbreaks of both known and novel influenza-like illnesses (ILIs) is an important public health problem.
objectiveThis study aimed to describe the design and testing of a tool that detects and tracks outbreaks of both known and novel ILIs, such as the SARS-CoV-2 worldwide pandemic, accurately and early.
methodsThis paper describes the ILI Tracker algorithm that first models the daily occurrence of a set of known ILIs in hospital emergency departments in a monitored region using findings extracted from patient care reports using natural language processing. We then show how the algorithm can be extended to detect and track the presence of an unmodeled disease that may represent a novel disease outbreak.
resultsWe include results based on modeling diseases like influenza, respiratory syncytial virus, human metapneumovirus, and parainfluenza for 5 emergency departments in Allegheny County, Pennsylvania, from June 1, 2014, to May 31, 2015. We also include the results of detecting the outbreak of an unmodeled disease, which in retrospect was very likely an outbreak of the enterovirus D68 (EV-D68).
conclusionsThe results reported in this paper provide support that ILI Tracker was able to track well the incidence of 4 modeled influenza-like diseases over a 1-year period, relative to laboratory-confirmed cases, and it was computationally efficient in doing so. The system was also able to detect a likely novel outbreak of EV-D68 early in an outbreak that occurred in Allegheny County in 2014 as well as clinically characterize that outbreak disease accurately.
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