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The ongoing COVID-19 pandemic is the first epidemic in human history in whichdigital contact-tracing has been deployed at a global scale. Tracking andquarantining all the contacts of individuals who test positive to a virus canhelp slowing-down an epidemic, but the impact of contact-tracing is severelylimited by the generally low adoption of contact-tracing apps in thepopulation. We derive here an analytical expression for the effectiveness ofcontact-tracing app installation strategies in a SIR model on a given contactgraph. We propose a decentralised heuristic to improve the effectiveness ofcontact tracing under fixed adoption rates, which targets a set of individualsto install contact-tracing apps, and can be easily implemented. Simulations ona large number of real-world contact networks confirm that this heuristicrepresents a feasible alternative to the current state of the art.

Optimising the mitigation of epidemic spreading through targeted adoption of contact tracing apps