tetano
Editor, Senior Moderator
PLoS Med
. 2021 Apr 30;18(4):e1003585.
doi: 10.1371/journal.pmed.1003585. Online ahead of print.
Maximizing and evaluating the impact of test-trace-isolate programs: A modeling study
Kyra H Grantz[SUP] 1 [/SUP], Elizabeth C Lee[SUP] 1 [/SUP], Lucy D'Agostino McGowan[SUP] 2 [/SUP], Kyu Han Lee[SUP] 1 [/SUP], C Jessica E Metcalf[SUP] 3 4 [/SUP], Emily S Gurley[SUP] 1 [/SUP], Justin Lessler[SUP] 1 [/SUP]
Affiliations
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.
. 2021 Apr 30;18(4):e1003585.
doi: 10.1371/journal.pmed.1003585. Online ahead of print.
Maximizing and evaluating the impact of test-trace-isolate programs: A modeling study
Kyra H Grantz[SUP] 1 [/SUP], Elizabeth C Lee[SUP] 1 [/SUP], Lucy D'Agostino McGowan[SUP] 2 [/SUP], Kyu Han Lee[SUP] 1 [/SUP], C Jessica E Metcalf[SUP] 3 4 [/SUP], Emily S Gurley[SUP] 1 [/SUP], Justin Lessler[SUP] 1 [/SUP]
Affiliations
- PMID: 33930019
- DOI: 10.1371/journal.pmed.1003585
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.