Centre de Physique Théorique


Mercredi 3 novembre 2021

16h00 – 17h00, Salle Sém 1, CPT

Adaptive temporal networks and the control of epidemics in large gatherings

Marco Mancastroppa (Univ. Parma, Italy)

Adaptive temporal networks are a powerful paradigm for the representation of systems composed of interacting elements, whose interactions change over time and their evolution is coupled to a dynamical process occurring within the system. This is the case of epidemic spreading on the social interaction network : the presence of an epidemic induces adaptive behaviors in the population, due to symptoms onset, risk awareness and the implementation of control measures. Among these measures, contact tracing is crucial to control epidemic spreading without disrupting societal activities. Large gatherings can be a source of superspreading events, however the effects of tracing in large groups have not been fully assessed so far. Within the framework of simplicial adaptive temporal networks, we model contact tracing on large groups. We show that alongside forward tracing, which reconstructs to whom disease spreads, and backward tracing, which searches from whom disease spreads, a third type of tracing is active in groups, the sideward tracing. This is an indirect tracing acting on asymptomatic individuals, even if they have neither been infected by nor they have transmitted the infection to the index case. We analyze this effect on an epidemic model for SARS-CoV-2 and we estimate the contribution of the three tracing mechanisms to the suppression of the epidemic, showing the relevance of sideward tracing and suggesting optimal tracing strategies. We also test our results on an empirical dataset of gatherings in the University of Parma.