  The Covid-19 pandemic presents a serious threat to people health, resulting
in over 250 million confirmed cases and over 5 million deaths globally. To
reduce the burden on national health care systems and to mitigate the effects
of the outbreak, accurate modelling and forecasting methods for short- and
long-term health demand are needed to inform government interventions aiming at
curbing the pandemic. Current research on Covid-19 is typically based on a
single source of information, specifically on structured historical pandemic
data. Other studies are exclusively focused on unstructured online retrieved
insights, such as data available from social media. However, the combined use
of structured and unstructured information is still uncharted. This paper aims
at filling this gap, by leveraging historical and social media information with
a novel data integration methodology. The proposed approach is based on vine
copulas, which allow us to exploit the dependencies between different sources
of information. We apply the methodology to combine structured datasets
retrieved from official sources and a big unstructured dataset of information
collected from social media. The results show that the combined use of official
and online generated information contributes to yield a more accurate
assessment of the evolution of the Covid-19 pandemic, compared to the sole use
of official data.
