Table 2.

Results of empirical studies

Authors; Setting; Longitudinal/ cross-sectional	Intervention(s)/ comparator(s)	Results relevant to contact tracing with effect sizes in brackets	Quality score/ Comment (studies with score < 16)
Wymant et al. [30] England and Wales Cross-sectional study	Contact tracing using National Health Service (NHS) COVID-19 app. Comparator: no app	Cases/ deaths averted in the period between end of September and end of December 2020: - Statistical estimation (95% CI): 594,000 (317,000-914,000) / 8,700 (4,700 − 13,500) and - Modelling (sensitivity analysis exploring 2.5-97.5% of variability in modelling estimates): 284,000 (108,000-450,000)/ 4,200 (1,600-6,600) Approximately one case was averted for each case consenting to notification of their contacts. For every percentage point increase in app uptake, the number of cases could be reduced by 0.8% (sensitivity analysis exploring 2.5-97.5% of variability in modelling estimates: 0.37-1.10%) or 2.3% (95% CI: 1.5-3%) depending on the estimation procedure	17
Kendall et al. [24] Isle of Wight (UK) Longitudinal study	Manual contact tracing + contact tracing using automated app. Comparator: Manual contact tracing	Results are presented graphically. After the introduction of contact tracing in the Isle of Wight, between May 5 and June 29 there is a drop in R from 1.3 to 0.5; at the same point in time, R was lower than in the Upper Tier Local Health Authorities of the UK as a whole (p < 0.0001)	17
Pozo-Martin et al. [27] 37 OECD countries Longitudinal study	(1) School closing requirements; (2) Workplace closing requirements; (3) Public events cancelling requirements; (4) Restrictions on gatherings; (5) Public transport restrictions; (6) Stay-at-home requirements; (7) Restrictions on internal travel; (8) International travel controls; (9) Public health information campaigns; (10) Mask wearing requirements; (11) Testing policy; (12) Contact tracing policy. Interventions are compared with each other.	Impact of contact tracing is not significant (exact effect size not provided)	16
Haug et al. [22] 79 territories and 46 countries worldwide Longitudinal study	42,151 NPIs including contact tracing. Interventions are compared to each other	Impact of contact tracing is not significant (exact effect size not provided)	16
Liu et al. [26] 130 countries worldwide Longitudinal study	(1) Internal containment and closure (School and workplace closure, public event cancellation, limits on gathering sizes, public transport closure, stay-at-home requirement, internal movement restriction); (2) International travel restrictions; (3) Economic policies; (4) Health systems policies (Public information campaign, testing policy, contact tracing). Interventions are compared with each other	Weak evidence of an association between contact tracing and an increase in R at 10 days (exact effect size is not provided)	15 No inclusion of covariates
Vecino-Ortiz et al. [28] 32 departments and 5 districts in Colombia Longitudinal study	Contact tracing as implemented in different departments and districts	A 10% increase in the proportion of cases identified through contact tracing in the previous 3 to 8 weeks is associated with COVID-19 mortality reductions between 0.8% and 3.4%. Regression coefficients for 0.8% mortality reduction (SE), cases traced 3 weeks previously: -0.078 (0.034), p-value = 0.024; Regression coefficients for 3.5% mortality reduction (SE) cases traced 3/4/6/8 weeks prior: -0.203 (0.042), p-value < 0.001/ 0.012 (0.036), p-value = 0.737/ -0.062 (0.030), p-value = 0.04/ -0.080 (0.026), p-value = 0.002	15 Analytical methodology less flexible
Leffler et al. [25] 200 countries Longitudinal study	(1) School closing; (2) Workplace closing; (3) Cancelling of public events; (4) Restrictions on gatherings; (5) Public transport closure; (6) Stay-at-home requirements; (7) Internal movement restrictions; (8) International travel restrictions; (9) Income support; (10) Public information campaigns; (11) Testing policy; (12) Contact tracing policy; (13) Mask-wearing	Impact of contact tracing is not significant. Regression coefficient (95% CI): -0.176 (-0.357 to 0.006), p-value = 0.06	13 Number of data points low
Wibbens et al. [29] Growth rate in cases 40 territories: 17 countries and 23 US states Longitudinal study	(1) Closing of schools; (2) Closing of workplaces; (3) Public event cancelling; (4) Gathering bans; (5) Public transport closure; (6) Shelter-in-place orders and home confinement; (7) Restrictions on internal movement; (8) Restrictions on international travel; (9) Public information campaigns; (10) Testing access; (11) Contact tracing. Interventions are compared with each other.	Marginal effect of contact tracing on reducing weekly growth rates is weak (results presented graphically, exact effect size not provided)	13 No inclusion of covariates
Papadopoulos et al. [31] 137 countries worldwide Longitudinal study	(1) School closing; (2) Workplace closing; (3) Cancelling of public events; (4) Restriction on gatherings; (5) Closure of public transport; (6) Stay-at-home restrictions; (7) Domestic travel restrictions; (8) International travel restrictions; (9) Public information; (10) Testing framework; (11) Contact tracing. Interventions are compared with each other	No evidence of a consistent association between high intensity contact tracing and decreased health outcomes (cases and deaths). Regression coefficient (95% CI) for cases/ deaths: 0.167 (0.006–0.316), p-value = 0.041 / 0.09 (-0.096-0.276), p-value = 0.296. There was no evidence of an association between timing of contact tracing and health outcomes. Regression coefficient (95% CI) for cases/ deaths: -0.019 (-0.228-0.153), p-value = 0.820 / -0.12 (-0.179-0.144), p-value = 0.893	11 Number of data points low, analytical methodology less flexible
Hong et al. [23] 108 countries Cross-sectional study	Assembly Restrictions (A): School closures (A1), Workplace closures (A2), Cancel public events (A3), Gathering size restriction (A4); Movement Restrictions (M): Close public transport (M1), Stay at home requirement (M2), Internal movement restrictions (M3), International travel restrictions (M4); Privacy Restriction (P): Contact tracing (P1). Interventions compared with each other	School closing in combination with full contact tracing (i.e. contact tracing done for all cases, used as an interaction term) has an impact on the decrease rate of increase in cumulative confirmed cases, leading to lower COVID-19 growth rate. Regression coefficients (SE) school closures / contact tracing as an interaction term: -2.070 (0.833), p-value < 0.05 / 0.227 (1.033)	11 Number of data points low, analytical methodology less flexible
Malheiro et al. [32] Eastern Porto (Portugal) Longitudinal study	(1) Contact tracing and quarantine. Comparator: no contact tracing and quarantine	Impact tracing and quarantine did not have an impact. Secondary attack rate in intervention group (95% CI) = 12·1% (7·1–18·9]; Secondary attack rate in control group (95% CI): 9·2% (7·8–10·8), p-value = 0.13	Acceptable Risk of performance bias
Park et al. [33] Seoul (South Korea) Longitudinal study	(1) Tracing the contacts of all COVID-19 case clusters and symptomatic individuals, testing them and placing all those testing positive in quarantine. Comparator: Testing only symptomatic, tracing and testing their contacts and quarantining all those testing positive	With tracing and testing the contacts of COVID-19 case clusters / symptomatic individuals and placing all those testing positive in quarantine the effective reproduction number was reduced from 1.3 to 0.6 (effect size not provided)	Acceptable Risk of selection bias