Anatomy of digital contact tracing: Role of age, transmission setting, adoption, and case detection

López, Jesús A. Moreno; García, Beatriz Arregui; Bentkowski, Piotr; Bioglio, Livio; Pinotti, Francesco; Boëlle, Pierre‐Yves; Barrat, Alain; Colizza, Vittoria; Poletto, Chiara

doi:10.1126/sciadv.abd8750

Cited by 66 publications

(41 citation statements)

References 65 publications

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“…We have then simulated contact tracing, both manual (MCT) and digital (DCT), and shown that both can yield a strong decrease in the final epidemic size. The relative impact of contact tracing is actually larger for lower reproduction numbers, and its cost lower: lower R 0 means indeed a slower epidemic reaching fewer individuals even without interventions, so that contact tracing needs to be applied to fewer cases, and fewer people need to be quarantined to mitigate the epidemic (this is in agreement with the results of [30]). Contact tracing thus becomes more efficient in a situation where the epidemic is already partially mitigated by other measures that keep R 0 as low as possible, showing the importance of combining contact tracing with other measures such as masks or hand hygiene, and that contact tracing might lose efficiency at too large R 0 .…”

Section: Discussionsupporting

confidence: 72%

“…As traditional MCT is labour intensive and limited by people's ability to correctly remember contacts, app-based DCT is seen as a potentially useful complement and has been deployed in several countries [18,21,47]. Its actual efficiency, however, has been debated, in particular with respect to the level of adoption needed for it to make a difference [18,29,30,47,48].…”

Section: Discussionmentioning

confidence: 99%

“…We note here that these datasets were collected in non-pandemic situations. No such data are currently available to investigate how contact patterns have changed owing to restrictions and individual risk perception [43], and this has thus typically been modelled by effectively reduced transmission rates [4,29,30]. We consider here post-lockdown scenarios in which contact patterns and, behaviours could return to (almost) normal, and for completeness, we will also consider both reduced effective transmission rates as well as other ways to take such potential changes into account, such as random reduction of contacts or suppression of fleeting contacts.…”

Section: Introductionmentioning

confidence: 99%

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Effect of manual and digital contact tracing on COVID-19 outbreaks: a study on empirical contact data

Barrat

Cattuto

Kivelä

et al. 2021

J. R. Soc. Interface.

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Non-pharmaceutical interventions are crucial to mitigate the COVID-19 pandemic and contain re-emergence phenomena. Targeted measures such as case isolation and contact tracing can alleviate the societal cost of lock-downs by containing the spread where and when it occurs. To assess the relative and combined impact of manual contact tracing (MCT) and digital (app-based) contact tracing, we feed a compartmental model for COVID-19 with high-resolution datasets describing contacts between individuals in several contexts. We show that the benefit (epidemic size reduction) is generically linear in the fraction of contacts recalled during MCT and quadratic in the app adoption, with no threshold effect. The cost (number of quarantines) versus benefit curve has a characteristic parabolic shape, independent of the type of tracing, with a potentially high benefit and low cost if app adoption and MCT efficiency are high enough. Benefits are higher and the cost lower if the epidemic reproductive number is lower, showing the importance of combining tracing with additional mitigation measures. The observed phenomenology is qualitatively robust across datasets and parameters. We moreover obtain analytically similar results on simplified models.

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Section: Discussionsupporting

confidence: 72%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of manual and digital contact tracing on COVID-19 outbreaks: a study on empirical contact data

Barrat

Cattuto

Kivelä

et al. 2021

J. R. Soc. Interface.

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“…Numerous studies have examined the effectiveness and limitations of TTIS on transmission of SARS-CoV-2 and other pathogens [14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Many studies have shown that TTIS can substantially reduce the pathogen reproductive number, R t , but its efficacy depends on the importance of pre-symptomatic and asymptomatic transmission, delays between symptom onset and being tested, and the fraction of infections that are tested and traced [14,19,22,24].…”

Section: Introductionmentioning

confidence: 99%

Contact tracing efficiency, transmission heterogeneity, and accelerating COVID-19 epidemics

Gardner

Kilpatrick

2021

PLoS Comput Biol

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Simultaneously controlling COVID-19 epidemics and limiting economic and societal impacts presents a difficult challenge, especially with limited public health budgets. Testing, contact tracing, and isolating/quarantining is a key strategy that has been used to reduce transmission of SARS-CoV-2, the virus that causes COVID-19 and other pathogens. However, manual contact tracing is a time-consuming process and as case numbers increase a smaller fraction of cases’ contacts can be traced, leading to additional virus spread. Delays between symptom onset and being tested (and receiving results), and a low fraction of symptomatic cases being tested and traced can also reduce the impact of contact tracing on transmission. We examined the relationship between increasing cases and delays and the pathogen reproductive number Rt, and the implications for infection dynamics using deterministic and stochastic compartmental models of SARS-CoV-2. We found that Rt increased sigmoidally with the number of cases due to decreasing contact tracing efficacy. This relationship results in accelerating epidemics because Rt initially increases, rather than declines, as infections increase. Shifting contact tracers from locations with high and low case burdens relative to capacity to locations with intermediate case burdens maximizes their impact in reducing Rt (but minimizing total infections may be more complicated). Contact tracing efficacy decreased sharply with increasing delays between symptom onset and tracing and with lower fraction of symptomatic infections being tested. Finally, testing and tracing reductions in Rt can sometimes greatly delay epidemics due to the highly heterogeneous transmission dynamics of SARS-CoV-2. These results demonstrate the importance of having an expandable or mobile team of contact tracers that can be used to control surges in cases. They also highlight the synergistic value of high capacity, easy access testing and rapid turn-around of testing results, and outreach efforts to encourage symptomatic cases to be tested immediately after symptom onset.

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“…The success of such large-scale digital mitigation strategies mostly depends, for a voluntary and decentralized approach, on acceptance in the general population, proper usage of the application, and technical properties [13]. Numerous studies have already been conducted regarding benefits and limitations of DigCT concerning its use during the COVID-19 pandemic [14][15][16][17][18][19][20][21][22][23][24][25][26][27]. It was found that strong mitigation effects could only be observed with high app participation.…”

Section: Introductionmentioning

confidence: 99%

Digital contact tracing contributes little to COVID-19 outbreak containment

Burdinski

Brockmann

Maier

2021

Preprint

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Digital contact tracing applications have been introduced in many countries to aid in the containment of COVID-19 outbreaks. Initially, enthusiasm was high regarding their implementation as a non-pharmaceutical intervention (NPI). Yet, no country was able to prevent larger outbreaks without falling back to harsher NPIs, and the total effect of digital contact tracing remains elusive. Based on the results of empirical studies and modeling efforts, we show that digital contact tracing apps might have prevented cases on the order of single-digit percentages up until now, at best. We show that this poor impact can be attributed to a combination of low participation rates, a non-flexible reliance on symptom-based testing, low engagement of participants, and delays between testing and test result upload. We find that contact tracing does not change the epidemic threshold and exclusively prevents more cases during the supercritical phase of an epidemic, making it unfit as a tool to prevent outbreaks. Locally clustered contact structures may increase the intervention’s efficacy, but only if the number of contacts per individual is homogeneously distributed, a condition usually not found in contact networks. Our results suggest that policy makers cannot rely on digital contact tracing to contain outbreaks of COVID-19 or similar diseases.

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Anatomy of digital contact tracing: Role of age, transmission setting, adoption, and case detection

Cited by 66 publications

References 65 publications

Effect of manual and digital contact tracing on COVID-19 outbreaks: a study on empirical contact data

Effect of manual and digital contact tracing on COVID-19 outbreaks: a study on empirical contact data

Contact tracing efficiency, transmission heterogeneity, and accelerating COVID-19 epidemics

Digital contact tracing contributes little to COVID-19 outbreak containment

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