Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study

Abstract

Background: In countries with declining numbers of confirmed cases of COVID-19, lockdown measures are gradually being lifted. However, even if most physical distancing measures are continued, other public health measures will be needed to control the epidemic. Contact tracing via conventional methods or mobile app technology is central to control strategies during de-escalation of physical distancing. We aimed to identify key factors for a contact tracing strategy to be successful.

Methods: We evaluated the impact of timeliness and completeness in various steps of a contact tracing strategy using a stochastic mathematical model with explicit time delays between time of infection and symptom onset, and between symptom onset, diagnosis by testing, and isolation (testing delay). The model also includes tracing of close contacts (eg, household members) and casual contacts, followed by testing regardless of symptoms and isolation if testing positive, with different tracing delays and coverages. We computed effective reproduction numbers of a contact tracing strategy (R_CTS) for a population with physical distancing measures and various scenarios for isolation of index cases and tracing and quarantine of their contacts.

Findings: For the most optimistic scenario (testing and tracing delays of 0 days and tracing coverage of 100%), and assuming that around 40% of transmissions occur before symptom onset, the model predicts that the estimated effective reproduction number of 1·2 (with physical distancing only) will be reduced to 0·8 (95% CI 0·7-0·9) by adding contact tracing. The model also shows that a similar reduction can be achieved when testing and tracing coverage is reduced to 80% (R_CTS 0·8, 95% CI 0·7-1·0). A testing delay of more than 1 day requires the tracing delay to be at most 1 day or tracing coverage to be at least 80% to keep R_CTS below 1. With a testing delay of 3 days or longer, even the most efficient strategy cannot reach R_CTS values below 1. The effect of minimising tracing delay (eg, with app-based technology) declines with decreasing coverage of app use, but app-based tracing alone remains more effective than conventional tracing alone even with 20% coverage, reducing the reproduction number by 17·6% compared with 2·5%. The proportion of onward transmissions per index case that can be prevented depends on testing and tracing delays, and given a 0-day tracing delay, ranges from up to 79·9% with a 0-day testing delay to 41·8% with a 3-day testing delay and 4·9% with a 7-day testing delay.

Interpretation: In our model, minimising testing delay had the largest impact on reducing onward transmissions. Optimising testing and tracing coverage and minimising tracing delays, for instance with app-based technology, further enhanced contact tracing effectiveness, with the potential to prevent up to 80% of all transmissions. Access to testing should therefore be optimised, and mobile app technology might reduce delays in the contact tracing process and optimise contact tracing coverage.

Funding: ZonMw, Fundação para a Ciência e a Tecnologia, and EU Horizon 2020 RECOVER.

Figure 1

Schematic of the contact tracing process and its time delays T₀=time of infection of index case. T₁=onset of infectiousness. T₂=symptom onset. T₃=time of positive diagnosis. T₄=time of tracing and quarantining of contacts.

Figure 2

Comparison of conventional and mobile app contact tracing strategies For parameter values, see table 1. The isolation only strategy is shown in green for comparison. We assumed that testing coverage is 80% for the conventional contact tracing strategy and 60%, 80%, and 100% for the mobile app contact tracing strategy. For the mobile app strategy, it is assumed that the tracing coverage equals the testing coverage—ie, it is 60%, 80%, and 100%, respectively. Expected reproduction numbers are shown as a function of testing delay D₁. R_e=effective reproduction number.

Figure 3

Estimated reduction of the effective reproduction number for various contact tracing strategies (A) R_CTS is shown as a percentage of R_e, where only physical distancing is implemented. For the isolation scenario and conventional contact tracing scenario, we assumed a 4-day delay between symptom onset and isolation of the index case. For mobile app contact tracing, testing delay was assumed to be 0 days. Testing coverage was assumed to be 80% in the isolation and conventional contact tracing scenarios; app use prevalence was assumed to be 60%, 80%, and 100% in the mobile app contact tracing scenario. (B) Distributions of individual reproduction numbers for 1000 individuals in the same scenarios as in described in panel A. Each boxplot shows the mean (diamond, where the height of the diamond indicates the CI of the mean) IQR, and upper fence (75% quartile + 1·5 × IQR) of the distribution. The dots are outliers, where darker dots contain more datapoints than lighter dots. All datapoints are integers. R_e=effective reproduction number. R_CTS=effective reproduction number with contact tracing.

Figure 4

Impact of varying levels of mobile app use on R_CTS In panels A and B, we assume that there is also testing (at 80% coverage) of those who do not use the mobile app, so app use only is used for tracing contacts. In panels C and D, only app users, who develop symptoms, are tested. Panels A and C show percentage reductions of R_e achieved by the mobile app contact tracing strategy; panels B and D show the impact of various contact tracing strategies on distributions of individual reproduction numbers, R_CTS. Each boxplot shows the mean (diamond, where the height of the diamond indicates the CI of the mean) IQR, and upper fence (75% quartile + 1·5 × IQR) of the distribution. The dots are outliers, where darker dots contain more datapoints than lighter dots. All datapoints are integers. R_e=effective reproduction number. R_CTS=effective reproduction number with contact tracing.