  Infectious epidemics can be simulated by employing dynamical processes as
interactions on network structures. Here, we introduce techniques from the
Multi-Agent System (MAS) domain in order to account for individual level
characterization of societal dynamics for the SARS-CoV-2 pandemic. We
hypothesize that a MAS model which considers rich spatial demographics, hourly
mobility data and daily contagion information from the metropolitan area of
Toronto can explain significant emerging behavior. To investigate this
hypothesis we designed, with our modeling framework of choice, GAMA, an
accurate environment which can be tuned to reproduce mobility and healthcare
data, in our case coming from TomTom's API and Toronto's Open Data. We observed
that some interesting contagion phenomena are directly influenced by mobility
restrictions and curfew policies. We conclude that while our model is able to
reproduce non-trivial emerging properties, large-scale simulation are needed to
further investigate the role of different parameters. Finally, providing such
an end-to-end model can be critical for policy-makers to compare their outcomes
with past strategies in order to devise better plans for future measures.
