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. Copyright © 2022 Inderscience Enterprises Ltd.
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.