TY - JOUR
T1 - Predicting the main pollen season of Broussonetia Papyrifera (paper mulberry) tree
AU - Kakakhail, Ahmad
AU - Rextin, Aimal
AU - Buters, Jeroen
AU - Lin, Chun
AU - Maya-Manzano, José M.
AU - Nasim, Mehwish
AU - Oteros, Jose
AU - Picornell, Antonio
AU - Pinnock, Hillary
AU - Schwarze, Jurgen
AU - Yusuf, Osman
N1 - Publisher Copyright:
© 2024 Kakakhail et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2024/2
Y1 - 2024/2
N2 - 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 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.
AB - 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 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.
UR - http://www.scopus.com/inward/record.url?scp=85183818059&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0296878
DO - 10.1371/journal.pone.0296878
M3 - Article
C2 - 38306347
AN - SCOPUS:85183818059
SN - 1932-6203
VL - 19
JO - PLoS One
JF - PLoS One
IS - 2 February
M1 - e0296878
ER -