Modelling digital and manual contact tracing for COVID-19 Are low uptakes and missed contacts deal-breakers?

ABSTRACT Comprehensive testing schemes, followed by adequate contact tracing and isolation, represent the best public health interventions we can employ to reduce the impact of an ongoing epidemic when no or limited vaccines are available and the implications of a full lockdown are to be avoided. However, the process of tracing can prove feckless for highly-contagious viruses such as SARS-Cov-2. The interview-based approaches often miss contacts and involve significant delays, while digital solutions can suffer from insufficient adoption rates or inadequate usage patterns. Here we present a novel way of modelling different contact tracing strategies using a generalized multi-site mean-field model, which can naturally assess the impact of both manual and digital approaches. Our methodology can readily be applied to any compartmental formulation, thus enabling the study of several complex pathogens. We use this technique to simulate a new epidemiological model, SEIR-T, and show that, given the right conditions, tracing in a COVID-19 epidemic can be effective even when digital uptakes are sub-optimal or interviewers miss a fair proportion of the contacts.