Short-term Forecasting of the COVID-19 Pandemic using Google Trends Data: Evidence from 158 Countries

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.

Note: Funding: The author gratefully acknowledges financial support from the grant of the Russian Science Foundation n. 20-68-47030.

Conflict of Interest: No conflict of interest to declare.

Keywords: COVID-19, Google Trends, VAR, ARIMA, ARIMA-X, ETS, LASSO, SIR model

JEL Classification: C22, C32, C51, C53, G17, I18, I19