  Following the spread of the COVID-19 pandemic and pending the establishment
of vaccination campaigns, several non pharmaceutical interventions such as
partial and full lockdown, quarantine and measures of physical distancing have
been imposed in order to reduce the spread of the disease and to lift the
pressure on healthcare system. Mathematical models are important tools for
estimating the impact of these interventions, for monitoring the current
evolution of the epidemic at a national level and for estimating the potential
long-term consequences of relaxation of measures. In this paper, we model the
evolution of the COVID-19 epidemic in Belgium with a deterministic
age-structured extended compartmental model. Our model takes special
consideration for nursing homes which are modelled as separate entities from
the general population in order to capture the specific delay and dynamics
within these entities. The model integrates social contact data and is fitted
on hospitalisations data (admission and discharge), on the daily number of
COVID-19 deaths (with a distinction between general population and nursing home
related deaths) and results from serological studies, with a sensitivity
analysis based on a Bayesian approach. We present the situation as in November
2020 with the estimation of some characteristics of the COVID-19 deduced from
the model. We also present several mid-term and long-term projections based on
scenarios of reinforcement or relaxation of social contacts for different
general sectors, with a lot of uncertainties remaining.
