Dry eye disease seasonal pattern and climatic association in Saudi Arabia using Google Trends

The study aims to investigate the dry eye search term pattern in Saudi Arabia using Google Trends, and to explore the association of weather changes on dry eye disease (DED) search interests.

Time series analysis for data that were collected from Google Trends (GTs) on period from January 2011 to October 2024 using Arabic term for Dry Eye with setting allocated in Saudi Arabia. Seasonality was evaluated using Fourier terms in ARIMA regression model. Monthly variation was further evaluated. Climate factors, mean surface air temperature, relative humidity and accumulated precipitate were incorporated into ARIMAX model to find environmental relationship with DED. Kruskal-Wallis test was performed to confirm significant finding in monthly variation.

Dry eye disease (DED) related search term demonstrates significant monthly differences (p = 0.008). Monthly effect ARIMA model (R² = 0.93) identified sustained high season from February through August higher than January. June represents maximum annual peak. A significant upward trend of 0.48 per month in RSV is also noted over the 13-year period (p < 0.001). In ARIMAX model, relative humidity is most important associated factor with dry eye search activity (β = -0.26, p = 0.002).

This study is the first evidence of seasonality of DED using Google Trends (GTs) in Saudi Arabia and highlighting the growing public health concern of DED. Understanding the disease pattern can aid public health implications to decrease risk of DED. This finding can serve as valuable reference to supplement traditional methods.