OBJECTIVE: Incorporating spatial analyses and online health information queries may be beneficial in understanding the role of Google relative search volume (RSV) data as a secondary public health surveillance tool during pandemics. Hence, this study identified COVID-19 clustering and defined the predictability performance of Google RSV models in clustered and non-clustered areas of the United States. METHODS: Getis-Ord General and local G statistics were used to identify monthly clustering patterns. Monthly country- and state-level correlations between new daily COVID-19 cases and Google RSVs were assessed using Spearman's rank correlation coefficients and Poisson regression models for the period of January to December 2020. RESULTS: Huge clusters involving multiple states were found, which resulted from various control measures in each state. This demonstrates the importance of state-to-state coordination in implementing control measures to tackle the spread of outbreaks. Variability in Google RSV model performances was found among states and time periods, possibly suggesting the necessity of utilizing different frameworks for Google RSV data in each state. Moreover, the sign of a correlation can be utilized to understand public responses to control and preventive measures, as well as in communicating risk. CONCLUSION: COVID-19 Google RSV model accuracy in the United States may be influenced by COVID-19 transmission dynamics, policy-driven community awareness, and past outbreak experiences.
High variability in model performance of Google relative search volume in spatially clustered COVID-19 areas of the United States