ArticleScientific reports2023
Machine learning identifies straightforward early warning rules for human Puumala hantavirus outbreaks.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Omics Approaches in Hantavirus Research: Current Advances, Challenges, and Future Perspectives.Biotech (Basel (Switzerland)) · 2026Review
- Climate change, rodent ecology, and the rising risk of hantavirus outbreaks: an alarming public health concern.Frontiers in microbiology · 2026Article
- High-resolution early warning system for human Puumala hantavirus infection risk in Germany.Scientific reports · 2024Article
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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
Human Puumala virus (PUUV) infections in Germany fluctuate multi-annually, following fluctuations of the bank vole population size. We applied a transformation to the annual incidence values and established a heuristic method to develop a straightforward robust model for the binary human infection risk at the district level. The classification model was powered by a machine-learning algorithm and achieved 85% sensitivity and 71% precision, despite using only three weather parameters from the previous years as inputs, namely the soil temperature in April of two years before and in September of the previous year, and the sunshine duration in September of two years before. Moreover, we introduced the PUUV Outbreak Index that quantifies the spatial synchrony of local PUUV-outbreaks, and applied it to the seven reported outbreaks in the period 2006-2021. Finally, we used the classification model to estimate the PUUV Outbreak Index, achieving 20% maximum uncertainty.
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