Husnayain, Atina and Chuang, Ting-Wu and Fuad, Anis and Su, Emily Chia-Yu (2021) High variability in model performance of Google relative search volumes in spatially clustered COVID-19 areas of the USA. International Journal of Infectious Diseases, 109. 269 – 278. ISSN 12019712
Full text not available from this repository. (Request a copy)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. © 2021 The Author(s)
Item Type: | Article |
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Additional Information: | Cited by: 7; All Open Access, Gold Open Access, Green Open Access |
Uncontrolled Keywords: | COVID-19; Humans; Pandemics; Public Health Surveillance; SARS-CoV-2; Search Engine; Article; cluster analysis; coronavirus disease 2019; District of Columbia; epidemic; explanatory variable; health statistics; human; incidence; infection control; infection prevention; information processing; interpersonal communication; medical informatics; prediction; public health surveillance; search engine; spatial analysis; United States; virus transmission; health survey; pandemic |
Subjects: | R Medicine > RP Public Health and Nutrition |
Divisions: | Faculty of Medicine, Public Health and Nursing > Biomedical Sciences |
Depositing User: | Sri JUNANDI |
Date Deposited: | 27 Sep 2024 00:37 |
Last Modified: | 27 Sep 2024 00:37 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/4606 |