Open AccessMaximizing and evaluating the impact of test-trace-isolate programs: A modeling study
Author(s) -
Kyra H. Grantz,
Elizabeth C. Lee,
Lucy D’Agostino McGowan,
Kyu Han Lee,
C. Jessica E. Metcalf,
Emily S. Gurley,
Justin Lessler
Publication year - 2021
Publication title -
plos medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.847
H-Index - 228
eISSN - 1549-1676
pISSN - 1549-1277
DOI - 10.1371/journal.pmed.1003585
Subject(s) - contact tracing , trace (psycholinguistics) , isolation (microbiology) , quarantine , computer science , covid-19 , test (biology) , tracing , infectious disease (medical specialty) , medicine , disease , biology , bioinformatics , ecology , philosophy , pathology , operating system , linguistics
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 ( R 0 : 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.