Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis [open pdf - 1MB]
From the Abstract: "The COVID-19 [coronavirus disease 2019] pandemic has affected virtually every region in the world. At the time of this study, the number of daily new cases in the United States was greater than that in any other country, and the trend was increasing in most states. Google Trends provides data regarding public interest in various topics during different periods. Analyzing these trends using data mining methods may provide useful insights and observations regarding the COVID-19 outbreak. [...] The objective of this study is to consider the predictive ability of different search terms not directly related to COVID-19 with regard to the increase of daily cases in the United States. In particular, we are concerned with searches related to dine-in restaurants and bars. Data were obtained from the Google Trends application programming interface and the COVID-19 Tracking Project. [...] Our results showed that for states and territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly occurred after reopening, significantly affected the number of daily new cases on average. California, for example, showed the most searches for restaurants on June 7, 2020; this affected the number of new cases within two weeks after the peak[.]" This article can also be found on the Journal of Medical Internet Research (JMIR) Public Health and Surveillance website: [https://publichealth.jmir.org/2021/4/e22880].
Milad Asgari Mehrabadi, Nikil Dutt, Amir M Rahmani. Posted here with permission. Document is under a Creative Commons license and requires proper attribution and noncommercial use to be shared: [https://creativecommons.org/licenses/by/4.0/]
JMIR Public Health and Surveillance: https://publichealth.jmir.org/
JMIR Public Health and Surveillance (2021), v.7 issue 4