Abstract
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.
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