  Mathematical modeling has been fundamental to achieving near real-time
accurate forecasts of the spread of COVID-19. Similarly, the design of
non-pharmaceutical interventions has played a key role in the application of
policies to contain the spread. However, there is less work done regarding
quantitative approaches to characterize the impact of each intervention, which
can greatly vary depending on the culture, region, and specific circumstances
of the population under consideration. In this work, we develop a
high-resolution, data-driven agent-based model of the spread of COVID-19 among
the population in five Spanish cities. These populations synthesize multiple
data sources that summarize the main interaction environments leading to
potential contacts. We simulate the spreading of COVID-19 in these cities and
study the effect of several non-pharmaceutical interventions. We illustrate the
potential of our approach through a case study and derive the impact of the
most relevant interventions through scenarios where they are suppressed. Our
framework constitutes a first tool to simulate different intervention scenarios
for decision-making.
