Google It Up! A Google Trends-based analysis of COVID-19 outbreak in Iran

Mohammad Reza Farzanegan (), Mehdi Feizi and Saeed Malek Sadati ()

MAGKS Papers on Economics from Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung)

Abstract: Soon after the first identified COVID-19 cases in Iran, the spread of the new Coronavirus has affected almost all its provinces. In the absence of credible data on people's unfiltered concerns and needs, especially in developing countries, Google search data is a reliable source that truthfully captures the public sentiment. This study examines the within province changes of confirmed cases of Corona across Iranian provinces from 19 Feb. 2020 to 9 March 2020. Using real-time Google Trends data, panel fixed effects, and GMM regression estimations, we show a robust negative association between the intensity of search for disinfection methods and materials in the past and current confirmed cases of the COVID-19 virus. In addition, we find a positive and robust association between the intensity of the searches for symptoms of Corona and the number of confirmed cases within the Iranian provinces. These findings are robust to control for province and period fixed effects, province-specific time trends, and lag of confirmed cases. Our results show how not only prevention could hinder affection in an epidemic disease but also prophecies, shaped by individual concerns and reflected in Google search queries, might not be self-fulfilling.

Keywords: Google Trends; COVID-19; Iran; epidemic disease (search for similar items in EconPapers)
Pages: 17 pages
Date: 2020
New Economics Papers: this item is included in nep-ara, nep-big and nep-hea
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Citations: View citations in EconPapers (2)

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