Modelling seasonal allergy incidence based on pollen monitoring data and its application in pharmaceutical logistics
Sofya M. Davydova - Graduate Student. Perm Campus of the National Research University “Higher School of Economics”
Konstantin G. Shvarts - Dr. Sc. (Physics and Mathematics), Prof. Perm National Research Polytechnic University
Vladislav V. Semerikov - Dr. Sc. (Medicine), Head of Epidemiology Dept. Perm Regional Clinical Hospital for Infectious Diseases, Chief Freelance Epidemiologist. Ministry of Health of Perm Krai, Prof. of Extreme Medicine and Commodity Science Dept. Perm State Pharmaceutical Academy
Abstract
The article aims to develop and test a model for forecasting seasonal allergic diseases based on aeropalynological monitoring data in Perm. The methods include mathematical modelling, machine learning techniques (LSTM, XGBoost, regression models) and time series analysis. The evidence base covers long-term data on pollen concentrations of major allergenic plants, meteorological parameters and statistics on the supply of antihistamines to medical and pharmaceutical organisations in Perm Krai. The models built have shown high forecast accuracy, which has made it possible to formulate practical recommendations for inventory management in retail pharmacies. The results obtained are useful for optimising pharmaceutical logistics, improving healthcare system preparedness, and minimising the consequences of seasonal allergy exacerbations, ensuring more efficient resource planning in the region.
Keywords: aeropalynological monitoring; forecasting; allergic diseases; machine learning; pollen allergens; business analytics; pharmaceutical logistics.
For citation: Davydova S. M., Shvarts K. G., Semerikov V. V. Modelling seasonal allergy incidence based on pollen monitoring data and its application in pharmaceutical logistics. Digital Models and Solutions. 2025. Vol. 4, no. 4, pp. 5–20. DOI: 10.29141/2949-477X-2025-4-4-1. EDN: CWWJPD.
