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Searching for the peak Google Trends and the Covid-19 outbreak in Italy

Author

Listed:
  • Paolo Brunori

    (Università degli Studi di Firenze)

  • Giuliano Resce

    (Sose (Rome))

Abstract

One of the difficulties faced by policy makers during the Covid-19 outbreak in Italy was the monitoring of the virus diffusion. Due to changing criteria and insufficient resources to test all suspected cases, the number of "confirmed infected" cases rapidly proved to be unreliably reported by official statistics. This limited the ability of epidemiologic models to predict the evolution of the infectious disease. This paper explores the possibility of using information obtained from Google Trends to supplement official statistics in order to predict when the number of deaths due to Covid-19 will peak in Italy. We estimate and regularize a panel model with regional and time fixed effects. Our preferred specification shows a positive and significant correlation between Google searches for commonly reported Covid-19 symptoms and deaths recorded. The analysis suggests that the social distancing measures implemented in early March in Italy were effective in slowing down the spread of the virus.

Suggested Citation

  • Paolo Brunori & Giuliano Resce, 2020. "Searching for the peak Google Trends and the Covid-19 outbreak in Italy," SERIES 04-2020, Dipartimento di Economia e Finanza - Università degli Studi di Bari "Aldo Moro", revised Apr 2020.
    • Handle: RePEc:bai:series:series_wp_04-2020
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    References listed on IDEAS

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    1. Raffaele Lagravinese & Paolo Liberati & Giuliano Resce, 2017. "Exploring health outcomes by stochastic multi-objective acceptability analysis: an application to Italian regions," Working Papers. Collection B: Regional and sectoral economics 1703, Universidade de Vigo, GEN - Governance and Economics research Network.
    2. Lagravinese, Raffaele & Liberati, Paolo & Resce, Giuliano, 2019. "Exploring health outcomes by stochastic multicriteria acceptability analysis: An application to Italian regions," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1168-1179.
    3. David A Broniatowski & Michael J Paul & Mark Dredze, 2013. "National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
    4. Salvatore Greco & Alessio Ishizaka & Benedetto Matarazzo & Gianpiero Torrisi, 2018. "Stochastic multi-attribute acceptability analysis (SMAA): an application to the ranking of Italian regions," Regional Studies, Taylor & Francis Journals, vol. 52(4), pages 585-600, April.
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    Cited by:

    1. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2022. "A babel of web-searches: Googling unemployment during the pandemic," Labour Economics, Elsevier, vol. 74(C).

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    More about this item

    Keywords

    Covid-19; Google Trends; Lasso;
    All these keywords.

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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