News Release

Pollen counts can be predicted by machine learning models using meteorological data with more than 80% accuracy even a week ahead, for both grass and birch tree pollen, which could be key in effectively treating hayfever

Peer-Reviewed Publication

PLOS

Comparison of machine learning methods in forecasting and characterizing the birch and grass pollen season

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Credit: Bulanda et al., 2026, PLOS One, CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)

Pollen counts can be predicted by machine learning models using meteorological data with more than 80% accuracy even a week ahead, for both grass and birch tree pollen, which could be key in effectively treating hayfever

Article URL: https://plos.io/3NW0x7X

Article title: Comparison of machine learning methods in forecasting and characterizing the birch and grass pollen season

Author countries: Poland

Funding: The study was supported by the statutory project of the Ministry of Science and Higher Education in Poland N41/DBS/001323. Initials of the authors who received the award: MB. URL of the funder: https://www.gov.pl/web/science. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


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