  Human behaviour strongly influences the spread of infectious diseases:
understanding the interplay between epidemic dynamics and adaptive behaviours
is essential to improve response strategies to epidemics, with the goal of
containing the epidemic while preserving a sufficient level of operativeness in
the population. Through activity-driven temporal networks, we formulate a
general framework which models a wide range of adaptive behaviours and
mitigation strategies, observed in real populations. We analytically derive the
conditions for a widespread diffusion of epidemics in the presence of arbitrary
adaptive behaviours, highlighting the crucial role of correlations between
agents behaviour in the infected and in the susceptible state. We focus on the
effects of sick-leave, comparing the effectiveness of different strategies in
reducing the impact of the epidemic and preserving the system operativeness. We
show the critical relevance of heterogeneity in individual behavior: in
homogeneous networks, all sick-leave strategies are equivalent and poorly
effective, while in heterogeneous networks, strategies targeting the most
vulnerable nodes are able to effectively mitigate the epidemic, also avoiding a
deterioration in system activity and maintaining a low level of absenteeism.
Interestingly, with targeted strategies both the minimum of population activity
and the maximum of absenteeism anticipate the infection peak, which is
effectively flattened and delayed, so that full operativeness is almost
restored when the infection peak arrives. We also provide realistic estimates
of the model parameters for influenza-like illness, thereby suggesting
strategies for managing epidemics and absenteeism in realistic populations.
