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Associations between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States

Author

Listed:
  • Mostafa Abbas

    (Department of Translational Data Science and Informatics, Geisinger, Danville, PA 17822, USA)

  • Thomas B. Morland

    (Department of General Internal Medicine, Geisinger, Danville, PA 17822, USA)

  • Eric S. Hall

    (Department of Translational Data Science and Informatics, Geisinger, Danville, PA 17822, USA)

  • Yasser EL-Manzalawy

    (Department of Translational Data Science and Informatics, Geisinger, Danville, PA 17822, USA)

Abstract

We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19-related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.

Suggested Citation

  • Mostafa Abbas & Thomas B. Morland & Eric S. Hall & Yasser EL-Manzalawy, 2021. "Associations between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States," IJERPH, MDPI, vol. 18(9), pages 1-24, April.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:9:p:4560-:d:543278
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    References listed on IDEAS

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    1. Dubin, Joel A. & Muller, Hans-Georg, 2005. "Dynamical Correlation for Multivariate Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 872-881, September.
    2. Han Shang, 2014. "A survey of functional principal component analysis," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(2), pages 121-142, April.
    3. Sasikiran Kandula & Jeffrey Shaman, 2019. "Reappraising the utility of Google Flu Trends," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-16, August.
    4. Heidi Ledford, 2020. "Why do COVID death rates seem to be falling?," Nature, Nature, vol. 587(7833), pages 190-192, November.
    5. 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.
    6. Aucejo, Esteban M. & French, Jacob & Ugalde Araya, Maria Paola & Zafar, Basit, 2020. "The impact of COVID-19 on student experiences and expectations: Evidence from a survey," Journal of Public Economics, Elsevier, vol. 191(C).
    7. Nicola Scafetta, 2020. "Distribution of the SARS-CoV-2 Pandemic and Its Monthly Forecast Based on Seasonal Climate Patterns," IJERPH, MDPI, vol. 17(10), pages 1-34, May.
    8. 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.
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    Cited by:

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    3. Maria da Penha de Andrade Abi Harb & Lena Veiga e Silva & Nandamudi Lankalapalli Vijaykumar & Marcelino Silva da Silva & Carlos Renato Lisboa Francês, 2022. "An Analysis of the Deleterious Impact of the Infodemic during the COVID-19 Pandemic in Brazil: A Case Study Considering Possible Correlations with Socioeconomic Aspects of Brazilian Demography," IJERPH, MDPI, vol. 19(6), pages 1-19, March.
    4. Michael Olumekor & Hossam Haddad & Nidal Mahmoud Al-Ramahi, 2023. "The Relationship between Search Engines and Entrepreneurship Development: A Granger-VECM Approach," Sustainability, MDPI, vol. 15(6), pages 1-16, March.

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