Contact Tracing for Disease Containment: a Network-Based Analysis

Abstract

Since the outbreak of the COVID-19 pandemic in spring 2020, the concept of test, trace, and isolate (TTI) was used as a non-pharmaceutical intervention against further spreading of the disease. Hereby, recent contact partners of newly confirmed SARS-CoV-2 infected persons were identified and isolated along with the originally detected case to avoid potential secondary infections. While the policy is, given the compliance of the traced persons, generally deemed efficient, not much is known about network-specific impact factors. In this work, we aim to evaluate the effectiveness of the TTI strategy when used (1) for diseases with different infectiousness levels and (2) on different contact networks. For the prior, we vary the infection probability per contact, for the latter, we analyse different clustering coefficients. Our goal is to test the validity of two hypotheses: First, we expect the policy to be more efficient if the infectiousness of the disease is small, since the time delay for isolating persons is crucial. Second, due to the implications of the friendship paradox, we expect the policy to be more effective if the clustering coefficient of the underlying contact network is high. We make use of an agent-based network model consisting of three intertwined model parts: an epidemiological SEIR model, a quarantine model and a contact-tracing model. To test the hypotheses, the disease parameters and the clustering coefficient of the underlying contact network are varied. The simulation results show that, indeed, tracing seems to have a slightly larger containment impact for networks with higher clustering, in particular for fast-spreading diseases. Yet, the effects are small compared to the impact of the infectiousness of the disease. Therefore, we find a significant decrease of the policy effectiveness the higher the transmission probability. The latter implies that the containment impact of tracing and isolating contacts becomes more efficient, if supported by additional measures that limit the infection probability or if applied in periods with low negative seasonality effects.

Keywords: COVID-19; SARS-CoV-2; agent-based modelling; clustering; contact-tracing; discrete-event simulation; epidemiology; network modelling.