Study population and setting
Using a stochastic compartmental model originally developed for smallpox vaccination, authors analyzed how delays in testing and contact tracing at the population level impact Re (effective reproductive number) and the proportion of transmissions that could be prevented per index case. To explore trade-offs within different components of a contact tracing program on Re, authors used their model to explore many sets of parameters. Among these, the authors defined a single conventional contact tracing scenario (80% testing coverage, 80% and 50% tracing coverage of close and casual contacts respectively, 4 day testing delay from symptom onset to receiving a test result, 3 day tracing delay from returning a case test result to contact quarantine) in relation to multiple mobile app contact tracing scenarios that had wider ranges in their assumed testing and tracing coverages and zero-day testing and tracing delays.
Summary of Main Findings
Assuming an app-based scenario where 80% of symptomatic persons were tested, 80% of their contacts traced, and testing and tracing delays were zero days, R was reduced from 1.2 to 0.8 (0.7-1.0) and 79.9% of transmission events were prevented. When the testing delay was three days or more, it was not possible to reduce R from 1.2 to below 1 (even when there was no tracing delay) . Mobile app-based tracing scenarios were more effective due to a combination of zero testing and tracing delays and greater tracing coverage for close and casual contacts. Sensitivity analysis showed, however, that high testing and tracing coverage were more important in reducing transmission than time delays. Even assuming a two-day testing delay, contact tracing via a mobile app could keep R below 1 as long as 80% symptomatic persons are tested and 80% of their contacts traced.
This study accounts for all individual components of contact tracing (e.g., timing of index case infection through quarantining of contacts). Model assumptions were also based on real-world experience from public health professionals working in contact tracing.
Considering the high testing coverage in the modeled scenarios, the reductions to R described in this study may be unrealistic in settings like the United States. Further, the authors use the terms “conventional” and “mobile app-based” contact tracing to refer to model assumptions. Thus, we urge caution in assuming these results would apply generically to a conventional or app-based contact tracing program, as their real-world performances could vary widely and even overlap.
Previous work indicates that contact tracing and isolation alone may not be sufficient to control outbreaks and that other interventions may be needed. This study assessed the degree to which each component of a contact tracing program contributes to its effectiveness.
This review was posted on: 21 September 2020