Google Trends search activity and laboratory-confirmed dengue cases in Nepal, 2019–2024: a correlation and time-lag analysis

Background: Dengue fever has emerged as a major public health concern in Nepal, where increasing outbreaks continue to challenge disease surveillance and timely public health response. Conventional surveillance systems often experience reporting delays, limiting their usefulness for early outbreak detection. Google Trends has been explored as a complementary digital surveillance tool in several countries; however, its utility for nationwide dengue surveillance in Nepal has not been investigated. This study evaluated the relationship between Google Trends search activity and official national dengue surveillance data and explored its potential for early outbreak detection. Objective: The primary objective is to evaluate the utility of Google Trends as a complementary digital surveillance indicator, to identify whether public online search behaviour can support the earlier detection of outbreaks compared to conventional public health surveillance systems. Methods: We conducted a retrospective nationwide time-series study using 72 months of laboratory-confirmed dengue case data reported by the Epidemiology and Disease Control Division (EDCD) and monthly Google Trends Relative Search Volume (RSV) for the search terms "Dengue fever," "Dengue Symptoms," and "Fever" from January 2019 to December 2024. Pearson and Spearman correlation analyses were used to evaluate associations between search activity and confirmed dengue cases. Simple linear regression was used to assess the explanatory value of each search term, and time-lagged correlation analysis was performed to examine whether changes in search activity preceded or followed official case reporting. Results: A total of 161,892 laboratory-confirmed dengue cases were reported during the study period, with more than 80% occurring during the post-monsoon months of August to October. Google Trends search activity closely followed these seasonal patterns. "Dengue fever" showed the strongest association with confirmed dengue cases (Pearson r = 0.784, Spearman ρ = 0.765, R² = 0.615; p < 0.001), followed by "Dengue Symptoms" (r = 0.695, R² = 0.483; p < 0.001) and "Fever" (r = 0.618, R² = 0.382; p < 0.001). Time-lag analysis showed significant correlations at a one-month lead for all three search terms, with "Fever" showing the strongest Lag +1 correlation (r = 0.556, p < 0.001). Conclusions: Google Trends search activity showed strong temporal agreement with national dengue surveillance data and demonstrated measurable lead-time before reported case peaks. These findings suggest that Google Trends may serve as a practical, low-cost complementary surveillance tool for dengue monitoring in Nepal. Further research using district-level data, higher temporal resolution, and locally relevant search terms is needed to improve its integration into routine surveillance systems.