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Pollen seasons progress differently in their timing, course, and intensity in different countries/biogeographical regions depending on regional factors such as vegetation, elevation, urbanization, and others. The variable regional situation often provides obstacles for a standard season definition.1 The season definition of the European Academy of Allergy and Clinical Immunology (EAACI) was published as a pollen concentration season definition depending on the selected pollen type reaching a certain threshold after a certain period of consecutive days, 2, 3 and it was demonstrated that they can be correlated with pollen-induced symptom loads.4 The season definition was developed for studies with a medical framework, particularly to allow a prospective approach. However, pollen concentrations do not reach the same level in Europe or globally and affect persons concerned differently.5 Therefore, there is a strong need to expand the scope of application of the EAACI season definition to a retrospective approach if the standard season definition criteria are not met within the site selection or during a clinical trial.

Ragweed Pollen Alarm System (R-PAS) has been running since 2014 to provide pollen information for countries in the Pannonian Biogeographical Region (PBR). The aim of this study is to develop forecast models of the representative aerobiological monitoring stations, identified by analysis based on a Neural Network computation. Monitoring stations with 7-day Hirst type pollen trap·having 10- year Iong validated dataset of ragweed pollen were selected for the study from the PBR. Variables including meteorological data, pollen data of the previous days·and nearby monitoring stations were used as input of the model. We used the·multilayer perceptron model to forecast the pollen concentration. The multilayer perceptron (MLP) is a feedforward artificial neural network. MLP is a data driven method it can use to forecast complex systems. ln our case it has three layers with one hidden layer. MLP utilizes a supervised learning technique called backpropagation for training to get better performance. The Neural Network tests selected different set of variables for predict pollen levels for the next 3 days in each monitoring stations. The predicted pollen Ievels are shown on isarithmic map. We use MAE, RMSE and correlation coefficients to show the forecasting system's performance. Visualization of the results of Neural Network forecast on isarithmic maps is a good tool to communicate pollen information to general public in the PBR.

Pollen exposure weakens the immunity against certain seasonal respiratory viruses by diminishing the antiviral interferon response. Here we investigate whether the same applies to the pandemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is sensitive to antiviral interferons, if infection waves coincide with high airborne pollen concentrations. Our original hypothesis was that more airborne pollen would lead to increases in infection rates. To examine this, we performed a cross-sectional and longitudinal data analysis on SARS-CoV-2 infection, airborne pollen, and meteorological factors. Our dataset is the most comprehensive, largest possible worldwide from 130 stations, across 31 countries and five continents. To explicitly investigate the effects of social contact, we additionally considered population density of each study area, as well as lockdown effects, in all possible combinations: without any lockdown, with mixed lockdown?no lockdown regime, and under complete lockdown. We found that airborne pollen, sometimes in synergy with humidity and temperature, explained, on average, 44% of the infection rate variability. Infection rates increased after higher pollen concentrations most frequently during the four previous days. Without lockdown, an increase of pollen abundance by 100 pollen/m3 resulted in a 4% average increase of infection rates. Lockdown halved infection rates under similar pollen concentrations. As there can be no preventive measures against airborne pollen exposure, we suggest wide dissemination of pollen?virus coexposure dire effect information to encourage high-risk individuals to wear particle filter masks during high springtime pollen concentrations. © 2021 National Academy of Sciences. All rights reserved., 1.1.1.2/VIAA/2/18/283; European Cooperation in Science and Technology, COST: CA18226; Academy of Finland, AKA: 318194, PS4A; Ministerio de Educación, Cultura y Deporte, MECD: FPU15/01668; Department of the Environment, Australian Government; Lietuvos Mokslo Taryba; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MPNTR: 451-03-68/2020-14/200358; European Social Fund, ESF: 09.3.3-LMT-K-712-01-0066; Helmholtz Association; Christine Kühne – Center for Allergy Research and Education, CK-CARE, ACKNOWLEDGMENTS. We thank Mr. Luis-Leopold Moelter and Mr. Mehmet Gökkaya for assistance in overall data curation. The study was partly implemented in the frame of the European Cooperation in Science and Technology (EU-COST) program, "New approaches in detection of pathogens and aeroallergens (ADOPT)," Grant CA18226 (EU Framework Program Horizon 2020). D.B. and C.T.-H. were supported by the Helmholtz Climate Initiative (HI-CAM), Mitigation and Adaptation. A.Ch. and D.V. were supported by the Municipality of Thessaloniki, Greece (Directorate for the Management of the Urban Environment, Department of Environment). This research has been partly supported by the European Social Fund (Project 09.3.3-LMT-K-712-01-0066) under grant agreement with the Research Council of Lithuania (LMTLT). The study was also partly conducted within the frame of the project of the European Community European Regional Development Fund (EC ERDF) and PostDoc Latvia (Grant 1.1.1.2/VIAA/2/18/283). M.S. acknowledges the Academy of Finland (Project PS4A, Grant 318194). We thank the Department of Health and Rehabilitation of Vinnytsia Regional Council, Ukraine, for providing the numbers of COVID-19 cases. A.H.A. acknowledges Angel Chaves and the Government of Navarra: Institute of Public and Labor Health of Navarra, within LIFE-IP NAdatpa-CC (LIFE16 IPC/ES/000001). A.P. was supported by a predoctoral grant financed by the Ministry of Education, Culture and Sports of Spain, in the Program for the Promotion of Talent and its Employability (Grant FPU15/01668). B.S. acknowledges the Ministry of Education, Science, and Technological Development of the Republic of Serbia (Grant 451-03-68/2020-14/200358). C.T.H. acknowledges the Christine Kühne–Center for Allergy Research and Education (CK-CARE), and The Initiative and Networking Fund of the Helmholtz Association (Immunology & Inflammation). We thank Jan Bumberger, Marcus Karsten, Paul Remmler, Jan C. Simon, and Regina Treudler for data provision and curation from Leipzig, Germany. We thank Claudia Langford Brown and Dana Flanders for statistical advice and scientific discussions. We thank Penelope Jones, Edith Bucher, Reyhan Gumusburun, Haydar Soydaner Karakus, Su Ozgur, and Asli Tetik Vardarli for data curation.