Maximizing and evaluating the impact of test-trace-isolate programs: A modeling study

PLoS Med. 2021 Apr 30;18(4):e1003585. doi: 10.1371/journal.pmed.1003585. eCollection 2021 Apr.

Abstract

Background: Test-trace-isolate programs are an essential part of coronavirus disease 2019 (COVID-19) control that offer a more targeted approach than many other nonpharmaceutical interventions. Effective use of such programs requires methods to estimate their current and anticipated impact.

Methods and findings: We present a mathematical modeling framework to evaluate the expected reductions in the reproductive number, R, from test-trace-isolate programs. This framework is implemented in a publicly available R package and an online application. We evaluated the effects of completeness in case detection and contact tracing and speed of isolation and quarantine using parameters consistent with COVID-19 transmission (R0: 2.5, generation time: 6.5 days). We show that R is most sensitive to changes in the proportion of cases detected in almost all scenarios, and other metrics have a reduced impact when case detection levels are low (<30%). Although test-trace-isolate programs can contribute substantially to reducing R, exceptional performance across all metrics is needed to bring R below one through test-trace-isolate alone, highlighting the need for comprehensive control strategies. Results from this model also indicate that metrics used to evaluate performance of test-trace-isolate, such as the proportion of identified infections among traced contacts, may be misleading. While estimates of the impact of test-trace-isolate are sensitive to assumptions about COVID-19 natural history and adherence to isolation and quarantine, our qualitative findings are robust across numerous sensitivity analyses.

Conclusions: Effective test-trace-isolate programs first need to be strong in the "test" component, as case detection underlies all other program activities. Even moderately effective test-trace-isolate programs are an important tool for controlling the COVID-19 pandemic and can alleviate the need for more restrictive social distancing measures.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19 / diagnosis
  • COVID-19 / prevention & control*
  • Contact Tracing* / methods
  • Disease Outbreaks / prevention & control*
  • Humans
  • Models, Theoretical*
  • Quarantine
  • SARS-CoV-2 / pathogenicity

Grant support

This work was supported by funding from State of California Institute of Technology (19-13081) (KHG, ECL, JL), Johns Hopkins Hospital (ECL, JL), and Bloomberg Philanthropies (KHG, ECL, LDM, KHL, ESG, JL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.