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Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries

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  • Fantazzini, Dean

    (Moscow School of Economics, Moscow State University, Moscow;)

Abstract

The ability of Google Trends data to forecast the number of new daily cases and deaths of COVID-19 is examined using a dataset of 158 countries. The analysis includes the computations of lag correlations between confirmed cases and Google data, Granger causality tests, and an out-of-sample forecasting exercise with 18 competing models with a forecast horizon of 14 days ahead. This evidence shows that Google-augmented models outperform the competing models for most of the countries. This is significant because Google data can complement epidemiological models during difficult times like the ongoing COVID-19 pandemic, when official statistics maybe not fully reliable and/or published with a delay. Moreover, real-time tracking with online-data is one of the instruments that can be used to keep the situation under control when national lockdowns are lifted and economies gradually reopen.

Suggested Citation

  • Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 33-54.
    • Handle: RePEc:ris:apltrx:0398
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    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Health > Measurement

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    Cited by:

    1. Schneider, Tim & Meub, Lukas & Bizer, Kilian, 2021. "Consumer information in a market for expert services: Experimental evidence," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 94(C).
    2. Szalkowski, Gabriel Andy & Mikalef, Patrick, 2023. "Understanding digital platform evolution using compartmental models," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    3. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    4. Nelson Mileu & Nuno M. Costa & Eduarda M. Costa & André Alves, 2022. "Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data," Data, MDPI, vol. 7(8), pages 1-17, July.

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    More about this item

    Keywords

    Covid-19; Google Trends; VAR; ARIMA; ARIMA-X; ETS; LASSO; SIR model.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • I19 - Health, Education, and Welfare - - Health - - - Other

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