Abstract

In early 2020, it emerged in Italy a large outbreak of Covid-19, the infectious disease caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). In this context, producing as accurate as possible forecasting models was crucial both for the containment of the epidemic and for the planning of effective policy interventions. In this article I describe the main data sources which have been used to monitor the spread of the Covid-19 virus diffusion in Italy in the early stages of the epidemic (i.e. spring 2020); I focus on administrative records, Big Data from online search engines, and survey data, including data on contagion, fatalities, and Intensive Care Units admissions and exits. I discuss data quality issues associated with these sources of data, and I comment the main challenges faced in the dissemination of data and research findings in the first stages of the epidemic.