Authors: Paolo Brunori; Giuliano Resce; Laura Serlenga
Addresses: International Inequalities Institute, London School of Economics, Houghton Street Site, WC2A 2AE, London, UK ' Department of Economics, University of Molise, Via F. de Sanctis – 86100, Campobasso, Italy ' Dipartimento di Economia e Finanza, Università degli Studi di Bari 'Aldo Moro', Largo Abbazia Santa Scolastica, 70124 – Bari, Italy
Abstract: One of the difficulties faced by policymakers during the COVID-19 outbreak in Italy was the monitoring of the virus diffusion. Due to changes in the criteria and insufficient resources to test all suspected cases, the number of 'confirmed infected' rapidly proved to be unreliably reported by official statistics. We explore the possibility of using information obtained from Google Trends to predict the evolution of the epidemic. Following the most recent developments on the statistical analysis of longitudinal data, we estimate a dynamic heterogeneous panel. This approach allows to takes into account the presence of common shocks and unobserved components in the error term both likely to occur in this context. We find that Google queries contain useful information to predict number patients admitted to the intensive care units, number of deaths and excess mortality in Italian regions.
Keywords: COVID-19; Google Trends; dynamic panel data; Italy.
International Journal of Computational Economics and Econometrics, 2022 Vol.12 No.4, pp.445 - 458
Received: 02 Apr 2021
Accepted: 09 Jun 2021
Published online: 20 Oct 2022 *