Forecasting seasonal allergic rhinitis through integrated analysis of social media and online drug sales data
Abstract
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 mm3), the Baidu search index for "allergic rhinitis," and online purchase rates of 4 oral second-generation H1-antihistamines (loratadine, desloratadine, cetirizine, levocetirizine) from the Meituan platform, standardized per 100,000 population. Cross-correlation functions were computed on pre-whitened series to assess lead-lag relationships. A single-mediator regression framework applied to pre-whitened data and estimated with Newey-West heteroskedasticity- and autocorrelation-consistent standard errors quantified the mediating role of the Baidu Index between pollen and antihistamine purchases. ARIMAX (1,1,1) models with different exogenous inputs (pollen only, Baidu Index only, both combined) and a naïve random-walk benchmark were used to forecast 1-, 2-, 3-, and 7-day demand; performance was evaluated using RMSE, MAPE, and Pearson correlation (PCC).
Results: All 3 series exhibited pronounced and…
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