  Isolation of symptomatic individuals, tracing and testing of their
nonsymptomatic contacts are fundamental strategies for mitigating the current
COVID-19 pandemic. The breaking of contagion chains relies on two complementary
strategies: manual reconstruction of contacts based on interviews and a digital
(app-based) privacy-preserving contact tracing. We compare their effectiveness
using model parameters tailored to describe SARS-CoV-2 diffusion within the
activity-driven model, a general empirically validated framework for network
dynamics. We show that, even for equal probability of tracing a contact, manual
tracing robustly performs better than the digital protocol, also taking into
account the intrinsic delay and limited scalability of the manual procedure.
This result is explained in terms of the stochastic sampling occurring during
the case-by-case manual reconstruction of contacts, contrasted with the
intrinsically prearranged nature of digital tracing, determined by the decision
to adopt the app or not by each individual. The better performance of manual
tracing is enhanced by heterogeneity in agent behavior: superspreaders not
adopting the app are completely invisible to digital contact tracing, while
they can be easily traced manually, due to their multiple contacts. We show
that this intrinsic difference makes the manual procedure dominant in realistic
hybrid protocols.
