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

Orestis Kazasidis1, Jens Jacob2

  • 1Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Plant Protection in Horticulture and Forests / Institute for Epidemiology and Pathogen Diagnostics, Rodent Research, Toppheideweg 88, 48161, Münster, Germany. orestis.kaza@gmail.com.

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Puumala virus (PUUV) infections in humans are linked to bank vole populations. A new model predicts human PUUV infection risk using weather data, achieving 85% sensitivity.

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Area of Science:

  • Environmental epidemiology
  • Infectious disease modeling
  • Mammalian ecology

Background:

  • Human Puumala virus (PUUV) infections exhibit multi-annual fluctuations in Germany, correlating with bank vole population dynamics.
  • Predicting localized human infection risk is crucial for public health interventions.

Purpose of the Study:

  • To develop a robust model for predicting human Puumala virus infection risk at the district level.
  • To introduce and apply a PUUV Outbreak Index for quantifying spatial synchrony of outbreaks.

Main Methods:

  • A machine-learning classification model was developed using transformed annual incidence values.
  • Input features included soil temperature (April, two years prior; September, previous year) and sunshine duration (September, two years prior).
  • The PUUV Outbreak Index was introduced to measure the spatial synchrony of local PUUV outbreaks.

Main Results:

  • The classification model achieved 85% sensitivity and 71% precision in predicting human infection risk.
  • The model effectively utilized only three specific weather parameters from previous years.
  • The PUUV Outbreak Index was estimated with a maximum uncertainty of 20% for the period 2006-2021.

Conclusions:

  • A straightforward, robust model for predicting human Puumala virus infection risk was established using limited weather data.
  • The developed model and PUUV Outbreak Index offer valuable tools for epidemiological surveillance and outbreak management.