Utilizing Internet Search Trends and Wastewater Surveillance to Identify Infectious Disease Outbreaks in Communities.

This study proposes a novel approach for viral infectious disease surveillance using Google Trends data to model wastewater virus concentrations, providing a rapid, low-cost method for indicating outbreaks. Google Trends search terms were found to correlate strongly with wastewater viral concentrations and clinical cases for influenza A and respiratory syncytial virus ( = 0.76 and 0.66). For norovirus and mpox, for which clinical data were limited, Google Trends showed significant correlations with wastewater concentrations. Three modeling approaches were developed: simple linear, stepwise selection, and principal component analysis. These models demonstrated strong predictive power for both norovirus ( of up to 0.66) and mpox ( of up to 0.60) wastewater concentrations. The approach was validated using a case study of a documented 2021 norovirus outbreak in Hartford, CT, where Google Trends indicators rose in tandem with wastewater concentrations, potentially providing earlier outbreak detection than clinical case data. This method offers a complementary data stream to wastewater surveillance for public health decision-making, particularly valuable in areas lacking a robust clinical testing infrastructure. Limitations include potential confounding factors, such as media coverage and the need to consider local idioms in international applications.