IDEAS home Printed from https://ideas.repec.org/a/zbw/espost/235602.html
   My bibliography  Save this article

Google Trends as a Method to Predict New COVID-19 Cases and Socio-Psychological Consequences of the Pandemic

Author

Listed:
  • Jurić, Tado

Abstract

Understanding how people react to the COVID-19 crisis, and what the consequences are of the COVID-19 pandemic is key to enable public health and other agencies to develop optimal intervention strategies. Because the timely identification of new cases of infection has proven to be the key to timely respond to the spread of infection within a particular region, we have developed a method that can detect and predict the emergence of new cases of COVID-19 at an early stage. Further, this method can give useful insights into a family's life during the pandemic and give the prediction of birth rates. The basic methodological concept of our approach is to monitor the digital trace of language searches with the Google Trends analytical tool (GT). We divided the keyword frequency for selected words giving us a search frequency index and then compared searches with official statistics to prove the significations of results. 1) Google Trends tools are suitable for predicting the emergence of new COVID-19 cases in Croatia. The data collected by this method correlate with official data. In Croatia search activities using GT for terms such as "PCR +COVID", and symptoms "cough + corona", "pneumonia + corona"; "muscle pain + corona" correlate strongly with officially reported cases of the disease. 2) The method also shows effects on family life, increase in stress, and domestic violence. 3) Birth rate in 2021 will be just 87% of what it would be "a normal year" in Croatia. 4) This tool can give useful insights into domestic violence. Unquestionably, there are still significant open methodological issues and the questionable integrity of the data obtained using this source. The fact is also a problem that GT does not provide data on which population was sampled or how it was structured. Although these open-ended issues pose serious challenges for making clear estimates, statistics offer a range of tools available to deal with imperfect data as well as to develop controls that take data quality into account. All these insights show that GT has the potential to capture attitudes in the broad spectrum of family life themes. The benefit of this method is reliable estimates that can enable public health officials to prepare and better respond to the possible return of a pandemic in certain parts of the country and the need for responses to protect family well-being.

Suggested Citation

  • Jurić, Tado, 2021. "Google Trends as a Method to Predict New COVID-19 Cases and Socio-Psychological Consequences of the Pandemic," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 7(forthcomi).
    • Handle: RePEc:zbw:espost:235602
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/235602/1/2021-4210-AJMS-Juric-05.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wilde, Joshua & Chen, Wei & Lohmann, Sophie, 2020. "COVID-19 and the Future of US Fertility: What Can We Learn from Google?," SocArXiv 2bgqs, Center for Open Science.
    2. Emilio Zagheni & Ingmar Weber & Krishna Gummadi, 2017. "Leveraging Facebook's Advertising Platform to Monitor Stocks of Migrants," Population and Development Review, The Population Council, Inc., vol. 43(4), pages 721-734, December.
    3. Nina Cesare & Hedwig Lee & Tyler McCormick & Emma Spiro & Emilio Zagheni, 2018. "Promises and Pitfalls of Using Digital Traces for Demographic Research," Demography, Springer;Population Association of America (PAA), vol. 55(5), pages 1979-1999, October.
    4. LUPPI, FRANCESCA & Arpino, Bruno & Rosina, Alessandro, 2020. "The impact of COVID-19 on fertility plans in Italy, Germany, France, Spain and UK," SocArXiv wr9jb, Center for Open Science.
    5. Jennifer Beam Dowd & Liliana Andriano & David M. Brazel & Valentina Rotondi & Per Block & Xuejie Ding & Yan Liu & Melinda C. Mills, 2020. "Demographic science aids in understanding the spread and fatality rates of COVID-19," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(18), pages 9696-9698, May.
    6. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    7. Neil M. Ferguson & Derek A.T. Cummings & Simon Cauchemez & Christophe Fraser & Steven Riley & Aronrag Meeyai & Sopon Iamsirithaworn & Donald S. Burke, 2005. "Strategies for containing an emerging influenza pandemic in Southeast Asia," Nature, Nature, vol. 437(7056), pages 209-214, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jurić Tado, 2022. "Forecasting Migration and Integration Trends Using Digital Demography – A Case Study of Emigration Flows from Croatia to Austria and Germany," Comparative Southeast European Studies, De Gruyter, vol. 70(1), pages 125-152, March.
    2. Jurić, Tado, 2022. "Forecasting Migration and Integration Trends Using Digital Demography – A Case Study of Emigration Flows from Croatia to Austria and Germany," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 70(1), pages 125-152.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andreea Avramescu & Arkadiusz Wiśniowski, 2021. "Now-casting Romanian migration into the United Kingdom by using Google Search engine data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 45(40), pages 1219-1254.
    2. Lawrence M Berger & Giulia Ferrari & Marion Leturcq & Lidia Panico & Anne Solaz, 2021. "COVID-19 lockdowns and demographically-relevant Google Trends: A cross-national analysis," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-28, March.
    3. Jurić, Tado, 2022. "Forecasting Migration and Integration Trends Using Digital Demography – A Case Study of Emigration Flows from Croatia to Austria and Germany," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 70(1), pages 125-152.
    4. Selin Köksal & Luca Maria Pesando & Valentina Rotondi & Ebru Şanlıtürk, 2022. "Harnessing the Potential of Google Searches for Understanding Dynamics of Intimate Partner Violence Before and After the COVID-19 Outbreak," European Journal of Population, Springer;European Association for Population Studies, vol. 38(3), pages 517-545, August.
    5. Jurić Tado, 2022. "Forecasting Migration and Integration Trends Using Digital Demography – A Case Study of Emigration Flows from Croatia to Austria and Germany," Comparative Southeast European Studies, De Gruyter, vol. 70(1), pages 125-152, March.
    6. Emery, Tom & Koops, Judith C., 2021. "The Impact of COVID-19 on Fertility behaviour and Intentions in the Republic of Moldova," SocArXiv fcqd9, Center for Open Science.
    7. André Grow & Daniela Perrotta & Emanuele Del Fava & Jorge Cimentada & Francesco Rampazzo & B. Sofia Gil-Clavel & Emilio Zagheni & René D. Flores & Ilana Ventura & Ingmar G. Weber, 2021. "How reliable is Facebook’s advertising data for use in social science research? Insights from a cross-national online survey," MPIDR Working Papers WP-2021-006, Max Planck Institute for Demographic Research, Rostock, Germany.
    8. Bekiros, Stelios & Kouloumpou, Dimitra, 2020. "SBDiEM: A new mathematical model of infectious disease dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    9. Arpino, Bruno & LUPPI, FRANCESCA & Rosina, Alessandro, 2021. "Changes in fertility plans during the COVID-19 pandemic in Italy: the role of occupation and income vulnerability," SocArXiv 4sjvm, Center for Open Science.
    10. Arpino, Bruno & Pasqualini, Marta & Bordone, Valeria & Solé-Auró, Aïda, 2020. "Indirect consequences of COVID-19 on people’s lives. Findings from an on-line survey in France, Italy and Spain," SocArXiv 4sfv9, Center for Open Science.
    11. Ayaz Hyder & David L Buckeridge & Brian Leung, 2013. "Predictive Validation of an Influenza Spread Model," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-20, June.
    12. Monica Alexander & Kivan Polimis & Emilio Zagheni, 2022. "Combining Social Media and Survey Data to Nowcast Migrant Stocks in the United States," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(1), pages 1-28, February.
    13. Douglas R. Leasure & Ridhi Kashyap & Francesco Rampazzo & Claire A. Dooley & Benjamin Elbers & Maksym Bondarenko & Mark Verhagen & Arun Frey & Jiani Yan & Evelina T. Akimova & Masoomali Fatehkia & Rob, 2023. "Nowcasting Daily Population Displacement in Ukraine through Social Media Advertising Data," Population and Development Review, The Population Council, Inc., vol. 49(2), pages 231-254, June.
    14. Lyócsa, Štefan & Baumöhl, Eduard & Výrost, Tomáš & Molnár, Peter, 2020. "Fear of the coronavirus and the stock markets," Finance Research Letters, Elsevier, vol. 36(C).
    15. Mattia Mazzoli & Boris Diechtiareff & Antònia Tugores & Willian Wives & Natalia Adler & Pere Colet & José J Ramasco, 2020. "Migrant mobility flows characterized with digital data," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-20, March.
    16. Taesik Lee & Hayong Shin, 2016. "Combining syndromic surveillance and ILI data using particle filter for epidemic state estimation," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 233-253, June.
    17. Jurić, Tado, 2022. "Predicting refugee flows from Ukraine with an approach to Big (Crisis) Data: a new opportunity for refugee and humanitarian studies," EconStor Preprints 251215, ZBW - Leibniz Information Centre for Economics.
    18. Bert Leysen & Pieter-Paul Verhaeghe, 2023. "Searching for migration: estimating Japanese migration to Europe with Google Trends data," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(5), pages 4603-4631, October.
    19. André Grow & Daniela Perrotta & Emanuele Del Fava & Jorge Cimentada & Francesco Rampazzo & Sofia Gil‐Clavel & Emilio Zagheni & René D. Flores & Ilana Ventura & Ingmar Weber, 2022. "Is Facebook's advertising data accurate enough for use in social science research? Insights from a cross‐national online survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 343-363, December.
    20. Lisa Singh & Carole Roan Gresenz, 2022. "Social Media Data for Firearms Research: Promise and Perils," The ANNALS of the American Academy of Political and Social Science, , vol. 704(1), pages 267-291, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:espost:235602. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/zbwkide.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.