Machine learning identifies straightforward early warning rules for human Puumala hantavirus outbreaks

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 r...

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Bibliographic Details
Published in:Scientific reports 2023-03, Vol.13 (1), p.3585-3585, Article 3585
Main Authors: Kazasidis, Orestis, Jacob, Jens
Format: Article
Language:English
Subjects:
704/158/1144
704/158/1469
Algorithms
Animals
Arvicolinae
Classification
Disease Outbreaks
Epidemics
Forests
Hantavirus
Health risks
Humanities and Social Sciences
Humans
Infections
Infectious diseases
Learning algorithms
Machine Learning
multidisciplinary
Outbreaks
Population number
Puumala virus
Science
Science (multidisciplinary)
Soil temperature
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-30596-x