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Digital Pathways, Pandemic Trajectories. Using Google Trends to Track Social Responses to COVID-19

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  • Beytía, Pablo
  • Infante, Carlos Cruz

Abstract

We already know which countries have controlled the spread of COVID-19 better and what “good practices” have enabled them to do so. Eventually, some of these policies could be replicated in other countries. But it is not enough to make a well-informed public intervention. We also need quickly available indicators of how actively populations are responding to the virus threat because current changes in social behaviour could mean significant differences in the spread of the COVID-19 in two weeks (after the incubation period). In this article, Pablo Beytía Reyes and Carlos Cruz Infante explore the potential of Google Trends to quickly track social responses to the pandemic. In all the countries that have reached a downward changepoint in the COVID-19 contagion, an “information saturation peak” preceded it: people were massively searching for information on the subject over 2 to 5 days, and about a week after the peak of searches was reached, a decline in the growth trend of coronavirus confirmed cases could be observed. Does it make sense to associate a Google search boom with a decrease in transmission trends? The authors propose that the frequency of searches is a quick indicator of 1) people’ concerns on the virus, 2) the development of a more informed citizenry on how to avoid transmission and 3) active social response to the virus spread, which generally lead to a downward change in the contagion trend.

Suggested Citation

  • Beytía, Pablo & Infante, Carlos Cruz, 2020. "Digital Pathways, Pandemic Trajectories. Using Google Trends to Track Social Responses to COVID-19," SocArXiv yndb7, Center for Open Science.
  • Handle: RePEc:osf:socarx:yndb7
    DOI: 10.31219/osf.io/yndb7
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    References listed on IDEAS

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    1. Killick, Rebecca & Eckley, Idris A., 2014. "changepoint: An R Package for Changepoint Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i03).
    2. Lynn Wu & Erik Brynjolfsson, 2015. "The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 89-118, National Bureau of Economic Research, Inc.
    3. Vanja Dukic & Hedibert F. Lopes & Nicholas G. Polson, 2012. "Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1410-1426, December.
    4. Qinneng Xu & Yulia R Gel & L Leticia Ramirez Ramirez & Kusha Nezafati & Qingpeng Zhang & Kwok-Leung Tsui, 2017. "Forecasting influenza in Hong Kong with Google search queries and statistical model fusion," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-17, May.
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    Cited by:

    1. Mauro Caselli & Andrea Fracasso & Sergio Scicchitano, 2020. "From the lockdown to the new normal: An analysis of the limitations to individual mobility in Italy following the Covid-19 crisis," Discussion Paper series in Regional Science & Economic Geography 2020-07, Gran Sasso Science Institute, Social Sciences, revised Oct 2020.

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