  Over a year after the start of the COVID-19 epidemics, we are still facing
the virus and it is hard to correctly predict its future spread over weeks to
come, as well as the impacts of potential political interventions. Current
epidemic models mainly fall in two approaches: compartmental models, divide the
population in epidemiological classes and rely on the mathematical resolution
of differential equations to give a macroscopic view of the epidemical
dynamics, allowing to evaluate its spread a posteriori; agent-based models are
computer models that give a microscopic view of the situation, since each human
is modelled as one autonomous agent, allowing to study the epidemical dynamics
in relation to (heterogeneous) individual behaviours. In this work, we compared
both methodologies and combined them to try and take advantage of the benefits
of each, and to overcome their limits. In particular, agent-based simulation
can be used to refine the values of the parameters of a compartmental model, or
to predict how these values evolve depending on sanitary policies applied. In
this report we discuss the conditions of such a combination of approaches, and
future improvements.
