The impact of digital contact tracing on the SARS-CoV-2 pandemic—a comprehensive modelling study
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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 ( ${R_{0}}$ 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 $\mathcal{O}$ 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.
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SciScore for 10.1101/2020.09.13.20192682: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:5.1 Limitations: We assumed that all people, once they have recovered from the infection, are immune to a secondary infection. Whether and for how long a recovered person is immune remains to be answered. Studies show that neutralizing antibodies are produced during infection and to a higher degree in symptomatic carriers, but decline …
SciScore for 10.1101/2020.09.13.20192682: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:5.1 Limitations: We assumed that all people, once they have recovered from the infection, are immune to a secondary infection. Whether and for how long a recovered person is immune remains to be answered. Studies show that neutralizing antibodies are produced during infection and to a higher degree in symptomatic carriers, but decline significantly 2-3 months after recovery [54, 55]. The minimal antibody titer to confer protection is, however, still unclear. Furthermore, memory T cells to SARS-CoV-2 have been found in patients including asymptomatic and mildly symptomatic ones, which likely contribute to protective immunity as well [56]. In our models, everyone adheres to quarantine protocols. That is, every time someone is alerted by the DCT system to having been in proximity of a contagious individual, this person must follow the quarantining and testing procedure. This is crucial to suppress pre- and a-symptomatic transmission, but may be difficult to achieve in reality. We also assume that the fraction of symptomatic individuals who see a doctor/get tested do so the day they become symptomatic. The transmission probability in our models changes with the time since infection, but not between individuals. Current research, however, suggests that COVID-19 is overdispered, meaning some individuals spread the virus to many others, in so-called “superspreading events”, while most do not transmit the virus at all or only to very few people [57]. Part of this overdispersion is du...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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