ArticlePloS one2022
Supervised learning using routine surveillance data improves outbreak detection of Salmonella and Campylobacter infections in Germany.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- Artificial Intelligence in Population-Level Gastroenterology and Hepatology: A Comprehensive Review of Public Health Applications and Quantitative Impact.Digestive diseases and sciences · 2026Review
- Mortality surveillance as an early warning system for respiratory infection outbreaks: lessons from the COVID-19 pandemic in Alexandria, Egypt.The Journal of the Egyptian Public Health Association · 2026Article
- Integrating Statistical and Machine-Learning Approaches forMicroorganisms · 2025Article
- Article
- An openEHR based infection control system to support monitoring of nosocomial bacterial clusters and contacts.NPJ digital medicine · 2025Article
- The application of machine learning in clinical microbiology and infectious diseases.Frontiers in cellular and infection microbiology · 2025Review
- From GeoSentinel data to epidemiological insights: a multidisciplinary effort towards artificial intelligence-supported detection of infectious disease outbreaks.Journal of travel medicine · 2024Article
- Building a pathway to One Health surveillance and response in Asian countries.Science in One Health · 2024Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The early detection of infectious disease outbreaks is a crucial task to protect population health. To this end, public health surveillance systems have been established to systematically collect and analyse infectious disease data. A variety of statistical tools are available, which detect potential outbreaks as abberations from an expected endemic level using these data. Here, we present supervised hidden Markov models for disease outbreak detection, which use reported outbreaks that are routinely collected in the German infectious disease surveillance system and have not been leveraged so far. This allows to directly integrate labeled outbreak data in a statistical time series model for outbreak detection. We evaluate our model using real Salmonella and Campylobacter data, as well as simulations. The proposed supervised learning approach performs substantially better than unsupervised learning and on par with or better than a state-of-the-art approach, which is applied in multiple European countries including Germany.
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Registered trials
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