Exploiting mobility data to forecast Covid-19 spread

Infectious diseases are spread through human-human transmissions; thus, the analysis of spatio-temporal mobility data can play a fundamental role to enable epidemic forecasting. This paper presents a data-driven predictive approach that analizes both mobility and infection data to discover spatio-temporal predictive epidemic patterns. Preliminary results, obtained by analyzing data related to mobility and COVID-19 infections in Chicago, show that the approach is promising.