A New Data Integration Framework for Covid-19 Social Media Information

Abstract

The Covid-19 pandemic presents a serious threat to people's health, resulting in over 250 million confirmed cases and over 5 million deaths globally. In order 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 as well as social media information with a novel data integration methodology. The proposed approach is based on vine copulas, which allow us to improve predictions by exploiting the dependencies between different sources of information. We apply the methodology to combine structured datasets retrieved from official sources and to a big unstructured dataset of information collected from social media. The results show that the proposed approach, compared to traditional approaches, yields more accurate estimations and predictions of the evolution of the Covid-19 pandemic.