  We study the effectiveness of tracking and testing in mitigating or
suppressing epidemic outbreaks, in combination with or as an alternative to
quarantines and global lockdowns. We study these intervention methods on a
network-based SEIR model, augmented with an additional probability to model
symptomatic, asymptomatic and pre-symptomatic cases. Our focus is on the basic
trade-offs between economic costs and human lives lost, and how these
trade-offs change under different lockdown, quarantine, tracking and testing
policies.
  Our main findings are as follows: (i) Tests combined with patient quarantines
reduce both economic costs and mortality, but require a large-scale testing
capacity to achieve a significant improvement; (ii) Tracking significantly
reduces both economic costs and mortality; (iii) Tracking combined with a
limited number of tests can achieve containment without lockdowns; (iv) If
there is a small flow of new incoming infections, dynamic "On-Off" lockdowns
are more efficient than fixed lockdowns.
  Our simulation results underline the extreme effectiveness of tracking and
testing policies in reducing both economic costs and mortality and their
potential to contain epidemic outbreaks without imposing social distancing
restrictions. This highlights the difficult social question of trading-off
these gains with the privacy loss that tracking necessarily entails.
