Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/32000
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: COVID-19 predictability in the United States using Google Trends time series
Author(s): Mavragani, Amaryllis
Gkillas, Konstantinos
Keywords: Bioinformatics
Epidemiology
Infectious diseases
Public health
Statistical methods
Issue Date: Dec-2020
Date Deposited: 26-Nov-2020
Citation: Mavragani A & Gkillas K (2020) COVID-19 predictability in the United States using Google Trends time series. Scientific Reports, 10 (1), Art. No.: 20693. https://doi.org/10.1038/s41598-020-77275-9
Abstract: During the unprecedented situation that all countries around the globe are facing due to the Coronavirus disease 2019 (COVID-19) pandemic, which has also had severe socioeconomic consequences, it is imperative to explore novel approaches to monitoring and forecasting regional outbreaks as they happen or even before they do so. To that end, in this paper, the role of Google query data in the predictability of COVID-19 in the United States at both national and state level is presented. As a preliminary investigation, Pearson and Kendall rank correlations are examined to explore the relationship between Google Trends data and COVID-19 data on cases and deaths. Next, a COVID-19 predictability analysis is performed, with the employed model being a quantile regression that is bias corrected via bootstrap simulation, i.e., a robust regression analysis that is the appropriate statistical approach to taking against the presence of outliers in the sample while also mitigating small sample estimation bias. The results indicate that there are statistically significant correlations between Google Trends and COVID-19 data, while the estimated models exhibit strong COVID-19 predictability. In line with previous work that has suggested that online real-time data are valuable in the monitoring and forecasting of epidemics and outbreaks, it is evident that such infodemiology approaches can assist public health policy makers in addressing the most crucial issues: flattening the curve, allocating health resources, and increasing the effectiveness and preparedness of their respective health care systems.
DOI Link: 10.1038/s41598-020-77275-9
Rights: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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