Weed Pollen Grading Prediction in Summer and Autumn in Beijing -- A Modeling Based on Patients with Weed Pollen Allergy

Yuhui Ouyang; Zhaoyin Yin; Jun Yang; Luo Zhang

doi:doi:10.11648/j.sd.20231102.17

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Weed Pollen Grading Prediction in Summer and Autumn in Beijing -- A Modeling Based on Patients with Weed Pollen Allergy

Yuhui Ouyang, Zhaoyin Yin, Jun Yang, Luo Zhang

Published in Science Discovery (Volume 11, Issue 2)

Received: 21 April 2023 Accepted: 19 April 2023 Published: 23 April 2023

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Abstract

Background: Pollinosis is an allergic disease caused by pollen allergens, which has a high incidence in Northern China. Weed pollen allergy in summer and autumn is the main reason for the seasonal increase in hospital visits in many cities. Objective: To develop a grading model of weed pollen deposition based on the data of allergic patients to predict development in patient with pollen allergy. Methods: Weed pollen data from four pollen monitoring stations in Beijing and the number of weed pollen allergen positive cases detected by serum specific immunoglobulin E (sIgE) in Beijing Tongren Hospital from 2013 to 2016 were used to develop a statistical model of pollen deposition and provide optimized threshold values. Results: There was a logarithmic correlation between the number of patients with weed pollen allergy and weed pollen deposition, and the average pollen deposition for three consecutive days was most correlated with the number of allergic patients. Based on the threshold of the number of patients and the characteristics of weed pollen, a five-stage pollen deposition grading model was developed to predict the degree of pollen allergy. Conclusions: Graded prediction of weed pollen deposition provide guidance for allergen protection of people with pollen allergy, and also provide a time window for intervention treatment before pollen stage and allergy-related clinical research.

Published in	Science Discovery (Volume 11, Issue 2)
DOI	10.11648/j.sd.20231102.17
Page(s)	68-73
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Pollinosis, Weed Pollen, Pollen Deposition, Graded Prediction

References

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APA Style

Yuhui Ouyang, Zhaoyin Yin, Jun Yang, Luo Zhang. (2023). Weed Pollen Grading Prediction in Summer and Autumn in Beijing -- A Modeling Based on Patients with Weed Pollen Allergy. Science Discovery, 11(2), 68-73. https://doi.org/10.11648/j.sd.20231102.17

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ACS Style

Yuhui Ouyang; Zhaoyin Yin; Jun Yang; Luo Zhang. Weed Pollen Grading Prediction in Summer and Autumn in Beijing -- A Modeling Based on Patients with Weed Pollen Allergy. Sci. Discov. 2023, 11(2), 68-73. doi: 10.11648/j.sd.20231102.17

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AMA Style

Yuhui Ouyang, Zhaoyin Yin, Jun Yang, Luo Zhang. Weed Pollen Grading Prediction in Summer and Autumn in Beijing -- A Modeling Based on Patients with Weed Pollen Allergy. Sci Discov. 2023;11(2):68-73. doi: 10.11648/j.sd.20231102.17

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@article{10.11648/j.sd.20231102.17,
  author = {Yuhui Ouyang and Zhaoyin Yin and Jun Yang and Luo Zhang},
  title = {Weed Pollen Grading Prediction in Summer and Autumn in Beijing -- A Modeling Based on Patients with Weed Pollen Allergy},
  journal = {Science Discovery},
  volume = {11},
  number = {2},
  pages = {68-73},
  doi = {10.11648/j.sd.20231102.17},
  url = {https://doi.org/10.11648/j.sd.20231102.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20231102.17},
  abstract = {Background: Pollinosis is an allergic disease caused by pollen allergens, which has a high incidence in Northern China. Weed pollen allergy in summer and autumn is the main reason for the seasonal increase in hospital visits in many cities. Objective: To develop a grading model of weed pollen deposition based on the data of allergic patients to predict development in patient with pollen allergy. Methods: Weed pollen data from four pollen monitoring stations in Beijing and the number of weed pollen allergen positive cases detected by serum specific immunoglobulin E (sIgE) in Beijing Tongren Hospital from 2013 to 2016 were used to develop a statistical model of pollen deposition and provide optimized threshold values. Results: There was a logarithmic correlation between the number of patients with weed pollen allergy and weed pollen deposition, and the average pollen deposition for three consecutive days was most correlated with the number of allergic patients. Based on the threshold of the number of patients and the characteristics of weed pollen, a five-stage pollen deposition grading model was developed to predict the degree of pollen allergy. Conclusions: Graded prediction of weed pollen deposition provide guidance for allergen protection of people with pollen allergy, and also provide a time window for intervention treatment before pollen stage and allergy-related clinical research.},
 year = {2023}
}

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TY  - JOUR
T1  - Weed Pollen Grading Prediction in Summer and Autumn in Beijing -- A Modeling Based on Patients with Weed Pollen Allergy
AU  - Yuhui Ouyang
AU  - Zhaoyin Yin
AU  - Jun Yang
AU  - Luo Zhang
Y1  - 2023/04/23
PY  - 2023
N1  - https://doi.org/10.11648/j.sd.20231102.17
DO  - 10.11648/j.sd.20231102.17
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 68
EP  - 73
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20231102.17
AB  - Background: Pollinosis is an allergic disease caused by pollen allergens, which has a high incidence in Northern China. Weed pollen allergy in summer and autumn is the main reason for the seasonal increase in hospital visits in many cities. Objective: To develop a grading model of weed pollen deposition based on the data of allergic patients to predict development in patient with pollen allergy. Methods: Weed pollen data from four pollen monitoring stations in Beijing and the number of weed pollen allergen positive cases detected by serum specific immunoglobulin E (sIgE) in Beijing Tongren Hospital from 2013 to 2016 were used to develop a statistical model of pollen deposition and provide optimized threshold values. Results: There was a logarithmic correlation between the number of patients with weed pollen allergy and weed pollen deposition, and the average pollen deposition for three consecutive days was most correlated with the number of allergic patients. Based on the threshold of the number of patients and the characteristics of weed pollen, a five-stage pollen deposition grading model was developed to predict the degree of pollen allergy. Conclusions: Graded prediction of weed pollen deposition provide guidance for allergen protection of people with pollen allergy, and also provide a time window for intervention treatment before pollen stage and allergy-related clinical research.
VL  - 11
IS  - 2
ER  -

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Author Information

Yuhui Ouyang

Department of Allergy, Beijing Tongren Hospital, Capital Medical University, Beijing, China
Zhaoyin Yin

Institute of Urban Meteorology, China Meteorological Administration, Beijing, China
Jun Yang

Beijing Key Laboratory of Nasal Diseases, Beijing Institute of Otolaryngology, Beijing, China
Luo Zhang

Department of Allergy, Beijing Tongren Hospital, Capital Medical University, Beijing, China

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Plain Text BibTeX RIS

APA Style

Yuhui Ouyang, Zhaoyin Yin, Jun Yang, Luo Zhang. (2023). Weed Pollen Grading Prediction in Summer and Autumn in Beijing -- A Modeling Based on Patients with Weed Pollen Allergy. Science Discovery, 11(2), 68-73. https://doi.org/10.11648/j.sd.20231102.17

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ACS Style

Yuhui Ouyang; Zhaoyin Yin; Jun Yang; Luo Zhang. Weed Pollen Grading Prediction in Summer and Autumn in Beijing -- A Modeling Based on Patients with Weed Pollen Allergy. Sci. Discov. 2023, 11(2), 68-73. doi: 10.11648/j.sd.20231102.17

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AMA Style

Yuhui Ouyang, Zhaoyin Yin, Jun Yang, Luo Zhang. Weed Pollen Grading Prediction in Summer and Autumn in Beijing -- A Modeling Based on Patients with Weed Pollen Allergy. Sci Discov. 2023;11(2):68-73. doi: 10.11648/j.sd.20231102.17

Copy | Download

@article{10.11648/j.sd.20231102.17,
  author = {Yuhui Ouyang and Zhaoyin Yin and Jun Yang and Luo Zhang},
  title = {Weed Pollen Grading Prediction in Summer and Autumn in Beijing -- A Modeling Based on Patients with Weed Pollen Allergy},
  journal = {Science Discovery},
  volume = {11},
  number = {2},
  pages = {68-73},
  doi = {10.11648/j.sd.20231102.17},
  url = {https://doi.org/10.11648/j.sd.20231102.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20231102.17},
  abstract = {Background: Pollinosis is an allergic disease caused by pollen allergens, which has a high incidence in Northern China. Weed pollen allergy in summer and autumn is the main reason for the seasonal increase in hospital visits in many cities. Objective: To develop a grading model of weed pollen deposition based on the data of allergic patients to predict development in patient with pollen allergy. Methods: Weed pollen data from four pollen monitoring stations in Beijing and the number of weed pollen allergen positive cases detected by serum specific immunoglobulin E (sIgE) in Beijing Tongren Hospital from 2013 to 2016 were used to develop a statistical model of pollen deposition and provide optimized threshold values. Results: There was a logarithmic correlation between the number of patients with weed pollen allergy and weed pollen deposition, and the average pollen deposition for three consecutive days was most correlated with the number of allergic patients. Based on the threshold of the number of patients and the characteristics of weed pollen, a five-stage pollen deposition grading model was developed to predict the degree of pollen allergy. Conclusions: Graded prediction of weed pollen deposition provide guidance for allergen protection of people with pollen allergy, and also provide a time window for intervention treatment before pollen stage and allergy-related clinical research.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Weed Pollen Grading Prediction in Summer and Autumn in Beijing -- A Modeling Based on Patients with Weed Pollen Allergy
AU  - Yuhui Ouyang
AU  - Zhaoyin Yin
AU  - Jun Yang
AU  - Luo Zhang
Y1  - 2023/04/23
PY  - 2023
N1  - https://doi.org/10.11648/j.sd.20231102.17
DO  - 10.11648/j.sd.20231102.17
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 68
EP  - 73
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20231102.17
AB  - Background: Pollinosis is an allergic disease caused by pollen allergens, which has a high incidence in Northern China. Weed pollen allergy in summer and autumn is the main reason for the seasonal increase in hospital visits in many cities. Objective: To develop a grading model of weed pollen deposition based on the data of allergic patients to predict development in patient with pollen allergy. Methods: Weed pollen data from four pollen monitoring stations in Beijing and the number of weed pollen allergen positive cases detected by serum specific immunoglobulin E (sIgE) in Beijing Tongren Hospital from 2013 to 2016 were used to develop a statistical model of pollen deposition and provide optimized threshold values. Results: There was a logarithmic correlation between the number of patients with weed pollen allergy and weed pollen deposition, and the average pollen deposition for three consecutive days was most correlated with the number of allergic patients. Based on the threshold of the number of patients and the characteristics of weed pollen, a five-stage pollen deposition grading model was developed to predict the degree of pollen allergy. Conclusions: Graded prediction of weed pollen deposition provide guidance for allergen protection of people with pollen allergy, and also provide a time window for intervention treatment before pollen stage and allergy-related clinical research.
VL  - 11
IS  - 2
ER  -

Copy | Download