Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing

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Abstract
Mobile phone apps implementing algorithmic contact tracing can speed up the process of tracing newly diagnosed individuals, spreading information instantaneously back through a past contact network to inform them that they are at risk of being infected, and thus allow them to take appropriate social distancing and testing measures. The aim of non-pharmaceutical infection prevention is to move a population towards herd protection, a state where a population maintains R​0<​1, thus making it impossible for a pathogen to cause an epidemic. Here, we address epidemiological issues that affect the feasibility of an algorithmic approach to instantaneous contact tracing; ethical and implementation issues are addressed separately. First we quantify the parameters of COVID-19 in a framework that is consistent with the renewal equation formulation of epidemic spread. Second, we use an analytical solution to application of first-degree contact tracing in the renewal equation model to explore combinations of efficacy that can induce herd protection (R​0<​1). With the emergence of the novel viral pathogen SARS-CoV-2, of clear potential for a global pandemic with high fatality rates and incapacitated health systems, the question of prevention has critical priority. We come to the conclusion that isolating symptomatic cases and tracing their contacts in a classical manner is not sufficiently fast to stop the spread of the epidemic and needs to be accompanied by measures of social distancing that are disruptive to a wide number of people. We show that first-degree instantaneous contact tracing, informing users when they can move safely or when to seek medical help and avoid vulnerable individuals, has the potential to stop the spread of the epidemic if used by a sufficiently large number of people with reasonable fidelity.
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