Pollen Allergy Hazard Mapping in ICT

Sadiq, Mayasah A.; Iftikhar, Asma; Naeem, Muhammad

doi:10.1109/rast.2007.4284072

Cited by 2 publications

(3 citation statements)

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“…Exposure to high concentrations pollen of some plants is known to cause allergy symptoms in many individuals [27,28]. One such plant species is the paper mulberry tree (Broussonetia papyrifera), whose high concentration is associated with increased risk of hospital visits due to asthma attacks [29] and severe respiratory problems in a large number of residents of Islamabad, Pakistan [7].…”

Section: Implications For Clinicians and Policymakersmentioning

confidence: 99%

“…The high level of paper mulberry pollen in spring can cause severe asthmatic reactions [6]. The World Health Organisation (WHO) estimates that it causes more than 40% of all respiratory allergies in Islamabad, including symptoms of allergic rhinoconjunctivitis and asthma, which sometimes require hospitalisation [7]. Therefore, there is a need to predict the onset of pollen seasons to inform those affected to take preventive measures beforehand [8,9].…”

Section: Introductionmentioning

confidence: 99%

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

Kakakhail,

Rextin,

Buters

et al. 2024

PLoS ONE

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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.

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Section: Implications For Clinicians and Policymakersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Kakakhail,

Rextin,

Buters

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…It provides first aid video demonstrations through the multimedia technology and 3-axes accelerometer mounted on the adrenaline injector, which sends the necessary information to emergency services. Solutions using geographical information system (GIS) and remote sensing techniques are also some of the monitoring ways to investigate the allergic reactions caused by the pollen grains, environmental pollution, etc., (Sadiq et al, 2007). In the existing monitoring approach, the ECG signal is used as a real-time measurement when a patient is being reacted by food allergy (Gutiérrez et al, 2013).…”

Section: Existing Monitoring Ways For Allergic Reactionsmentioning

confidence: 99%

A theoretical model of healthcare monitoring surveillance system for patients with severe allergies

al.¹

2020

Int. j. adv. appl. sci.

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Allergies influenced by some unwanted foods, dust, and pollens create quick reactions, which may be basic skin diseases, life-threatening disorders, and serious breathing problems such as asthma. The most severe allergic reaction data is quickly and efficiently handled by the healthcare monitoring systems when merged with the information and communication technologies (ICT). A four-month study was conducted with the existing healthcare monitoring systems to know how quickly they provide solutions to the patients suffering from severe allergies. The study comprises collecting patients data, especially children under the age of five years, which include regular and specific food they eat, hygienic or dusty surrounding, environmental conditions influenced by allergy creation actors and quality of life before and after the allergic reactions. Accuracy of the data depends on the efficiency of the Healthcare Monitoring Surveillance System (HMSS), which employs the Multiple-Input Multiple-Output (MIMO) scheme as a new mechanism. In this research work, HMSS with MIMO technology provides not only better accuracy, quality of life, and quickness as compared to existing state-of-the-art HMSSs, but also improves the lifetime of the monitoring system with reliability, maintainability, and availability. The produced results show the supremacy of the proposed mechanism when accuracy is the main concern.

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Pollen Allergy Hazard Mapping in ICT

Cited by 2 publications

References 0 publications

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

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

A theoretical model of healthcare monitoring surveillance system for patients with severe allergies

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