This article is a Preprint
Preprints are preliminary research reports that have not been certified by peer review. They should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Preprints posted online allow authors to receive rapid feedback and the entire scientific community can appraise the work for themselves and respond appropriately. Those comments are posted alongside the preprints for anyone to read them and serve as a post publication assessment.
Maximizing and evaluating the impact of test-trace-isolate programs (preprint)
medrxiv; 2020.
Preprint
in English
| medRxiv | ID: ppzbmed-10.1101.2020.09.02.20186916
ABSTRACT
Background:
Test-trace-isolate programs are an essential part of COVID-19 control that offer a more targeted approach than many other non-pharmaceutical interventions. Effective use of such programs requires methods to estimate their current and anticipated impact. Methods andFindings:
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 case detection, speed of isolation, contact tracing completeness and speed of 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 to the proportion of infections 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. Formally framing the dynamical process also indicates 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 program performance are sensitive to assumptions about COVID-19 natural history, 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.
Full text:
Available
Collection:
Preprints
Database:
medRxiv
Main subject:
COVID-19
Language:
English
Year:
2020
Document Type:
Preprint
Similar
MEDLINE
...
LILACS
LIS