  To date, the only effective means to respond to the spreading of COVID-19
pandemic are non-pharmaceutical interventions (NPIs), which entail policies to
reduce social activity and mobility restrictions. Quantifying their effect is
difficult, but it is key to reduce their social and economical consequences.
Here, we introduce a meta-population model based on temporal networks,
calibrated on the COVID-19 outbreak data in Italy and apt to evaluate the
outcomes of these two types of NPIs. Our approach combines the advantages of
granular spatial modelling of meta-population models with the ability to
realistically describe social contacts via activity-driven networks. We provide
a valuable framework to assess the viability of different NPIs, varying with
respect to their timing and severity. Results suggest that the effects of
mobility restrictions largely depend on the possibility to implement timely
NPIs in the early phases of the outbreak, whereas activity reduction policies
should be prioritised afterwards.
