  We consider an epidemic process on adaptive activity-driven temporal
networks, with adaptive behaviour modelled as a change in activity and
attractiveness due to infection. By using a mean-field approach, we derive an
analytical estimate of the epidemic threshold for SIS and SIR epidemic models
for a general adaptive strategy, which strongly depends on the correlations
between activity and attractiveness in the susceptible and infected states. We
focus on strong social distancing, implementing two types of quarantine
inspired by recent real case studies: an active quarantine, in which the
population compensates the loss of links rewiring the ineffective connections
towards non-quarantining nodes, and an inactive quarantine, in which the links
with quarantined nodes are not rewired. Both strategies feature the same
epidemic threshold but they strongly differ in the dynamics of active phase. We
show that the active quarantine is extremely less effective in reducing the
impact of the epidemic in the active phase compared to the inactive one, and
that in SIR model a late adoption of measures requires inactive quarantine to
reach containment.
