High variability in model performance of Google relative search volumes in spatially clustered COVID-19 areas of the USA. (August 2021)

• Record Type: Journal Article
• Authors: Husnayain, Atina; Chuang, Ting-Wu; Fuad, Anis; Su, Emily Chia-Yu
• Is Part Of: International journal of infectious diseases. Volume 109(2021)
• Page Start: 269
• Page End: 278
• Publication Date: 2021-08
• Subjects: COVID-19 -- United States -- Spatial analysis -- Google Trends -- Predictability performance -- Infodemiology; Communicable diseases -- Periodicals; Communicable Diseases -- Periodicals; Communicable diseases; Periodicals; Electronic journals; 616.9
• DOI: 10.1016/j.ijid.2021.07.031
• Languages: English
• ISSNs: 1201-9712

Abstract

Highlights: State-to-state coordination in implementing control measures is needed urgently. Google relative search volume (RSV) model performance varied between states and time periods. Each state may use Google RSV data in different frameworks. Google RSV model accuracy may be influenced by coronavirus disease 2019 transmission dynamics. Google RSV model accuracy may also be altered by policy-driven community awareness and past outbreak experience. ABSTRACT: 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. This study identified coronavirus disease 2019 (COVID-19) clustering and defined the predictability performance of Google RSV models in clustered and non-clustered areas of the USA. 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 January–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 performance was found among states and time periods, possibly suggesting the need to use different frameworks for Google RSV data in each state. Moreover, the sign of 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 USA may be influenced by COVID-19 transmission dynamics, policy-driven community awareness and past outbreak experiences.