  The recent COVID-19 pandemic underscores the significance of early-stage
non-pharmacological intervention strategies. The widespread use of masks and
the systematic implementation of contact tracing strategies provide a
potentially equally effective and socially less impactful alternative to more
conventional approaches, such as large-scale mobility restrictions. However,
manual contact tracing faces strong limitations in accessing the network of
contacts, and the scalability of currently implemented protocols for
smartphone-based digital contact tracing becomes impractical during the rapid
expansion phases of the outbreaks, due to the surge in exposure notifications
and associated tests. A substantial improvement in digital contact tracing can
be obtained through the integration of probabilistic techniques for risk
assessment that can more effectively guide the allocation of new diagnostic
tests. In this study, we first quantitatively analyze the diagnostic and social
costs associated with these containment measures based on contact tracing,
employing three state-of-the-art models of SARS-CoV-2 spreading. Our results
suggest that probabilistic techniques allow for more effective mitigation at a
lower cost. Secondly, our findings reveal a remarkable efficacy of
probabilistic contact-tracing techniques in capturing backward propagations and
super-spreading events, relevant features of the diffusion of many pathogens,
including SARS-CoV-2.

Populations and Evolution;Statistical Mechanics;Artificial Intelligence;Machine Learning;Physics and Society