  A contact-tracing strategy has been deemed necessary to contain the spread of
COVID-19 following the relaxation of lockdown measures. Using an agent-based
model, we explore one of the technology-based strategies proposed, a
contact-tracing smartphone app. The model simulates the spread of COVID-19 in a
population of agents on an urban scale. Agents are heterogeneous in their
characteristics and are linked in a multi-layered network representing the
social structure - including households, friendships, employment and schools.
We explore the interplay of various adoption rates of the contact-tracing app,
different levels of testing capacity, and behavioural factors to assess the
impact on the epidemic. Results suggest that a contact tracing app can
contribute substantially to reducing infection rates in the population when
accompanied by a sufficient testing capacity or when the testing policy
prioritises symptomatic cases. As user rate increases, prevalence of infection
decreases. With that, when symptomatic cases are not prioritised for testing, a
high rate of app users can generate an extensive increase in the demand for
testing, which, if not met with adequate supply, may render the app
counterproductive. This points to the crucial role of an efficient testing
policy and the necessity to upscale testing capacity.
