Forecasting seasonal allergic rhinitis through integrated analysis of social media and online drug sales data.

Background: Allergic rhinitis (AR) is a highly prevalent, seasonally variable disease that poses a growing public health challenge. Conventional surveillance based on clinic visits and surveys is slow and may miss self-medicating patients. Integrating environmental monitoring with digital traces such as search queries and online drug purchases may provide more timely insight into allergen exposure, symptom awareness, and medication demand. Methods: We analyzed daily data for urban Beijing from March 1, 2022 to October 15, 2024, including pollen concentration (grains per 1000 mm Results: All 3 series exhibited pronounced and recurrent seasonal patterns, with Baidu search activity rising in close temporal alignment with pollen peaks and antihistamine purchases typically lagging by 1-2 days. Pre-whitened cross-correlations remained moderate and statistically significant around lag 0, indicating genuine contemporaneous associations after removing the shared seasonal component. Mediation analysis showed that pollen concentration had a significant total effect on antihistamine purchase rates (β = 0.34, 95% CI [0.13, 0.54]), of which approximately 47% was transmitted indirectly via the Baidu Index (indirect β = 0.16, 95% CI [0.08, 0.25]). The ARIMAX (1,1,1) model integrating both pollen and Baidu Index achieved the best forecasting performance (1-day RMSE = 3.80, MAPE = 7.68%, PCC = 0.89) and consistently outperformed single-predictor models and the random-walk benchmark across all horizons. Conclusion: Pollen exposure influences antihistamine demand both directly and indirectly through public information-seeking behavior captured by the Baidu Index, and integrating environmental and digital data enables timely surveillance of seasonal allergic-rhinitis-related medication use.