Predicting the main pollen season of Broussonetia Papyrifera (paper mulberry) tree

Ahmad Kakakhail, Aimal Rextin, Jeroen Buters, Chun Lin*, Jose M. Maya-Manzano, Mehwish Nasim, José Oteros, Antonio Picornell, Hilary Pinnock, Jürgen Schwarze, Osman Yusuf

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Paper mulberry pollen, declared a pest in several countries including Pakistan, can trigger severe allergies and cause asthma attacks. We aimed to develop an algorithm that could accurately predict high pollen days to underpin an alert system that would allow patients to take timely precautionary measures. We developed and validated two prediction models that take historical Nov 15, 2023 2/18 pollen and weather data as their input to predict the start date and peak date of the pollen season in Islamabad, the capital city of Pakistan. The first model is based on linear regression and the second one is based on phenological modelling. We tested our models on an original and comprehensive dataset from Islamabad. The mean absolute errors (MAEs) for the start day are 2.3 and 3.7 days for the linear and phenological models, respectively, while for the peak day, the MAEs are 3.3 and 4.0 days, respectively. These encouraging results could be used in a website or app to notify patients and healthcare providers to start preparing for the paper mulberry pollen season. Timely action could reduce the burden of symptoms, mitigate the risk of acute attacks and potentially prevent deaths due to acute pollen-induced allergy.
Original languageEnglish
Article numbere0296878
Number of pages16
JournalPLoS ONE
Issue number2
Publication statusPublished - 2 Feb 2024


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