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.

Trading contact tracing efficiency for finding patient zero

“…In more detail, for any two nodes with an edge between them, they come into contact every one to seven days according to a truncated power-law distribution with exponent 2.2. To model the infection we use a model by Rusu et al 13 , which at the moment of writing is the leading model of the COVID-19 disease. Even though it is slightly more complex than simple SIR or SEIR models, we selected it for a more accurate simulation of the disease.…”

“…While existing research on contact tracing was focused on using it to detect either current or future infections, we investigate the potential use of contact tracing to detect the source of infection, adding a new dimension to the utility of the contact tracing toolkit. To this end, we perform a wide variety of simulations on different kinds of temporal network structures, while using a spreading model of the COVID-19 disease 13 to recreate the dynamics of the epidemic. It is worth noting that many existing source detection methods would benefit from the information gathered using a contact tracing process, e.g., in order to recreate the structure of the social network under consideration.…”

“…We use a spreading model of the COVID-19 disease by Rusu et al 13 In this model, every node in the network is in one of the following states: susceptible S, exposed but not infectious E; infectious presymptomatic I p , infectious asymptomatic I a , infectious symptomatic I s , hospitalized H, recovered R, or dead D. The probabilities of transitions between states in a given round are presented in Table 1, while the descriptions of the model parameters and the values used in our simulations are summarized in Table 2.…”