Abstract
Contact tracing is one of several strategies employed in many countries to curb the spread of SARS-CoV-2. Digital contact tracing (DCT) uses tools such as cell-phone applications to improve tracing speed and reach. We model the impact of DCT on the spread of the virus for a large epidemiological parameter space consistent with current literature on SARS-CoV-2. We also model DCT in combination with random testing (RT) and social distancing (SD).Modelling is done with two independently developed individual-based (stochastic) models that use the Monte Carlo technique, benchmarked against each other and against two types of deterministic models.For current best estimates of the number of asymptomatic SARS-CoV-2 carriers (approximately 40%), their contagiousness (similar to that of symptomatic carriers), the reproductive number before interventions ({R_{0}} at least 3) we find that DCT must be combined with other interventions such as SD and/or RT to push the reproductive number below one. At least 60% of the population would have to use the DCT system for its effect to become significant. On its own, DCT cannot bring the reproductive number below 1 unless nearly the entire population uses the DCT system and follows quarantining and testing protocols strictly. For lower uptake of the DCT system, DCT still reduces the number of people that become infected.When DCT is deployed in a population with an ongoing outbreak where mathcal{O} (0.1%) of the population have already been infected, the gains of the DCT intervention come at the cost of requiring up to 15% of the population to be quarantined (in response to being traced) on average each day for the duration of the epidemic, even when there is sufficient testing capability to test every traced person.
Highlights
Tracing and isolation of people who were in contact with an infectious person can be used to control the spread of communicable diseases [1, 2]
We developed individual-based models with the Monte Carlo (MC) simulation technique, flanked and cross-checked by deterministic models, to evaluate the dynamics of a COVID-19 outbreak under different intervention protocols, focusing on digital contact tracing (DCT) and DCT combined with random testing and social distancing
We note that when we model with ηas < 1, we apply the scaling to both preand asymptomatic phases; since there most likely is no difference in viral load, if asymptomatic transmission is suppressed, this is likely due to circumstantial factors, like lack of coughing, which apply to the pre-symptomatic phase as well
Summary
Tracing and isolation of people who were in contact with an infectious person (contact tracing) can be used to control the spread of communicable diseases [1, 2]. In the traditional understanding of contact tracing (CT), public health employees interview known (2021) 10:37 carriers (index cases) of the disease and track down people who had the type of close contact with the index case necessary to transmit the disease. This implementation is only suited for infections that spread relatively slowly, and where cases can be diagnosed [3]. Recent studies indicate that an outbreak of SARS-CoV-2 could be controlled using fast and efficient digital contact tracing (DCT) [4, 5]. Automated CT systems were in use during the Ebola outbreak 2014–2016 [16] and it turned out that the technical difficulties that come with that approach must not be underestimated
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