One of the di culties faced by policy makers during the COVID-19 outbreak in Italy was the monitoring of the virus di usion. Due to changes in the criteria and insu cient resources to test all suspected cases, the number of `con rmed infected' rapidly proved to be unreliably reported by o cial 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 nd 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.
“Searching for the peak: Google Trends and the COVID-19 outbreak in Italy”
Paolo Brunori;Laura Serlenga
2022-01-01
Abstract
One of the di culties faced by policy makers during the COVID-19 outbreak in Italy was the monitoring of the virus di usion. Due to changes in the criteria and insu cient resources to test all suspected cases, the number of `con rmed infected' rapidly proved to be unreliably reported by o cial 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 nd 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
