Predicting the Olea pollen concentration with a machine learning algorithm ensemble

Identifying influence factors and thresholds of the next day's pollen concentration in different seasons using interpretable machine learning

The prevalence of pollen allergies is a pressing global issue, with projections suggesting that half of the world's population will be affected by 2050 according to the estimation of the World Health Organization (WHO). Accurately forecasting pollen allergy risks requires identifying key factors and their thresholds for aerosol pollen. To address this, we developed a technical framework combining advanced machine learning and SHapley Additive exPlanations (SHAP) technology, focusing on Beijing. By analyzing meteorological data and vegetation phenology, we identified the factors influencing next-day's pollen concentration (NDP) in Beijing and their thresholds. Our results highlight vegetation phenology data from Synthetic Aperture Radar (SAR), temperature, wind speed, and atmospheric pressure as crucial factors in spring. In contrast, the Normalized Difference Vegetation Index (NDVI), air temperature, and wind speed are significant in autumn. Leveraging SHAP technology, we established season-specific thresholds for these factors. Our study not only confirms previous research but also unveils seasonal variations in the relationship between radar-derived vegetation phenology data and NDP. Additionally, we observe seasonal fluctuations in the influence patterns and threshold values of daily air temperatures on NDP. These insights are pivotal for improving pollen concentration prediction accuracy and managing allergic risks effectively.

Projections of the start of the airborne pollen season in Barcelona (NE Iberian Peninsula) over the 21st century

The effects of global warming are numerous and recent studies reveal that they can affect the timing of pollination. Temperature is the meteorological variable that presents a clearer relationship with the start of the pollination season of most of the observed airborne pollen taxa. In Catalonia, in the last fifty years, the average annual air temperature has increased by +0.23 °C/decade, and the local warming has been slightly higher than the one on a global scale. Projections point to an increase in temperature in the coming decades, which would be more marked towards the middle of the century. To analyse the effect of the increase in temperature due to global warming on the starting date of pollen season in Barcelona, a forecasting model has been applied to a set of projected future temperatures estimated by the European RESCCUE project. This model, largely used in the literature, is based on determining the thermal needs of the plant for the pollen season to begin. The model calibration to obtain the initial parameters has been made by using 20 years of pollen data (2000-2019), and the model effectiveness has subsequently been tested through an internal evaluation over the period of the calibration and an external evaluation on 4 years not included in the calibration (2020-2023). The mean bias error in the internal calibration ranged between -0.4 and - 0.6 days, and between +0.5 and - 8.3 in the external one, depending on the taxon. The results of the application of the model to the temperature projections over the 21st century point to a progressive advancement in the pollination dates of several pollen types abundant in the city, allergenic most of them. These advances ranged, at the end of the century, between 15 and 27 days, depending on the climate model, for the scenario of the highest concentrations (RCP8.5) and between 7 and 12 days for the emissions stabilization scenario (RCP4.5).

A deep learning LSTM-based approach for forecasting annual pollen curves: Olea and Urticaceae pollen types as a case study

Airborne pollen can trigger allergic rhinitis and other respiratory diseases in the synthesised population, which makes it one of the most relevant biological contaminants. Therefore, implementing accurate forecast systems is a priority for public health. The current forecast models are generally useful, but they falter when long time series of data are managed. The emergence of new computational techniques such as the LSTM algorithms could constitute a significant improvement for the pollen risk assessment. In this study, several LSTM variants were applied to forecast monthly pollen integrals in Málaga (southern Spain) using meteorological variables as predictors. Olea and Urticaceae pollen types were modelled as proxies of different annual pollen curves, using data from the period 1992-2022. The aims of this study were to determine the LSTM variants with the highest accuracy when forecasting monthly pollen integrals as well as to compare their performance with the traditional pollen forecast methods. The results showed that the CNN-LSTM were the most accurate when forecasting the monthly pollen integrals for both pollen types. Moreover, the traditional forecast methods were outperformed by all the LSTM variants. These findings highlight the importance of implementing LSTM models in pollen forecasting for public health and research applications.

A Framework for Augmented Intelligence in Allergy and Immunology Practice and Research—A Work Group Report of the AAAAI Health Informatics, Technology, and Education Committee

Artificial and augmented intelligence (AI) and machine learning (ML) methods are expanding into the health care space. Big data are increasingly used in patient care applications, diagnostics, and treatment decisions in allergy and immunology. How these technologies will be evaluated, approved, and assessed for their impact is an important consideration for researchers and practitioners alike. With the potential of ML, deep learning, natural language processing, and other assistive methods to redefine health care usage, a scaffold for the impact of AI technology on research and patient care in allergy and immunology is needed. An American Academy of Asthma Allergy and Immunology Health Information Technology and Education subcommittee workgroup was convened to perform a scoping review of AI within health care as well as the specialty of allergy and immunology to address impacts on allergy and immunology practice and research as well as potential challenges including education, AI governance, ethical and equity considerations, and potential opportunities for the specialty. There are numerous potential clinical applications of AI in allergy and immunology that range from disease diagnosis to multidimensional data reduction in electronic health records or immunologic datasets. For appropriate application and interpretation of AI, specialists should be involved in the design, validation, and implementation of AI in allergy and immunology. Challenges include incorporation of data science and bioinformatics into training of future allergists-immunologists.