Our understanding of infectious diseases prevention and control is rooted in the theory of host population transmission dynamics. Contacts between hosts (along which transmission can occur) and contacts between populations of hosts (along which spatial diffusion can take place) drive the epidemiology of infectious diseases, determining if and how quickly they spread, and who gets infected. Digital data has revolutionized how we study and model disease epidemics, providing unprecedented sources of information to characterize individual and population dynamics, from traditional datasets generally taking an aggregated form (commuting, public transportation, census, air travel, contact surveys), to sensor data typically showing a higher resolution (mobile phones, GPS traces, RFID, other sensors). They provide details at different scales, and often contain geographical and temporal information, as well as metadata. While offering the potential to fuel a wide spectrum of data-driven numerical simulations of epidemic spreading processes, data sources also challenge traditional modeling approaches that are not suited to easily integrate higher-resolution information. This lecture will focus on the design of data-driven models for infectious disease spread exploring different data availabilities and resolutions.