One of the difficulties faced by policy makers during the COVID-19 outbreak in Italy was the monitoring of the virus diffusion. Due to changing criteria and insufficient resources to test all suspected cases, the number of ‘confirmed infected’ cases rapidly proved to be unreliably reported by official statistics. This limited the ability of epidemiologic models to predict the evolution of the infectious disease. This paper explores the possibility of using information obtained from Google Trends to supplement official statistics in order to predict when the number of deaths due to COVID-19 will peak in Italy. We estimate and regularize a panel model with regional and time fixed effects. Our preferred specification shows a positive and significant correlation between Google searches for commonly reported COVID-19 symptoms and deaths recorded. The analysis suggests that the social distancing measures implemented in early March in Italy were effective in slowing down the spread of the virus.