The impact of digital contact tracing on the SARS-CoV-2 pandemic—a comprehensive modelling study

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.

Comparative effectiveness of contact tracing interventions in the context of the COVID-19 pandemic: a systematic review

“…For the ABM-based mathematical modelling studies, based on the period of the epidemic modelled we identified two broad groups of studies: (1) Studies modelling the COVID-19 epidemic in a context other than the 2020 lockdown reopening, either in the general population [ 36 – 41 , 46 , 47 , 54 , 55 , 57 , 61 , 64 , 66 – 68 ], in a population of workers [ 44 ] or in a hospital [ 53 ] , and (2) studies modelling the COVID-19 epidemic in the context of the 2020 lockdown reopening, either in the general population [ 7 , 12 , 34 , 35 , 39 , 45 , 48 , 49 , 51 , 52 , 56 , 58 – 60 , 62 , 65 , 69 ] or in educational institutions [ 42 , 43 , 50 , 63 ]. Within the first group, all studies modelled outbreaks over a variable time span (from 60 days [ 40 ] to 600 days [ 68 ]) from the first COVID-19 cases except three [ 38 , 46 , 54 ], which modelled the conditions of an ongoing epidemic, such as acquired immunity or vaccination. Within the second group of studies, all reproduced the conditions of specific 2020 lockdown and reopening scenarios in the modelling parameters except the studies set in educational institutions, which modelled outbreaks in the event of initiating at least some in-person teaching.…”

“…For example, Huamani et al [ 75 ] uses R 0 values of 2.7 and 3.5 for pre-lockdown and 1.5, 2.0 and 2.7 post-lockdown, based on estimates by Liu et al [ 94 ] and Chen et al [ 95 ]. Liu et al [ 94 ] is referenced as a source of R 0 in several studies, including Wallentin et al [ 7 ], Huamani et al [ 75 ], James et al [ 76 ] and Pollmann et al [ 54 ] - this paper reviews the first estimates of R 0 in China, concluding that the mean (median) value for this parameter is 3.28 (2.79). The incubation time is set to relatively similar values across most studies.…”

The Vaccination Process against the COVID-19: Opportunities, Problems and mHealth Support

“…The important role of this technology has been seen and is continuing to be seen in telemedical applications, such as in the activities related to the continuity of care in the various forms of teleconsultation, television, and tele-diagnosis [ 59 ]. We have also seen, during the pandemic, the leading role of these technologies in the most common applications of daily life, from teaching to e-banking [ 11 ], or in the innovative digital contact tracing applications [ 60 ].…”

Feasibility of digital contact tracing in low-income settings – pilot trial for a location-based DCT app

“…The reported findings on effectiveness were similar to our results, and they concluded that the app did not make a meaningful contribution to COVID-19 contact tracing. Explanations for why many countries struggle to exploit the potential of DCT as predicted by modeling studies [3,[16][17][18] are the focus of current research. Based on a review of the literature [9], privacy concerns over user data, low trust in government and third parties (big data analysis, malicious actors), security vulnerabilities (hacker attacks), ethical issues (discrimination of minorities), user behavior and participation (limited experience with mobile devices, reasons for adoption) have been prioritized in primary studies, in contrast to understanding technical constraints.…”

“…Given that more than 40% (one billion) of Android active users worldwide use version 6.0 or below and no longer receive updates, many Android devices may not benefit from updates to the new BTLE-based contact tracing system Google built in collaboration with Apple during the COVID-19 pandemic (Google/Apple API) [10]. High app uptake rates have been identified as a crucial factor in DCT effectiveness [3,17,18], and unsupported OS versions could prevent achieving the required uptake rates in low-income settings if BTLE is used. Of the participants in our study (urban population, hospital staff ), 20% were using Android 6 or below, whereas in the overall population, the number could be significantly higher.…”