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About:
Controlling COVID-19 via test-trace-quarantine
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research paper
schema:ScholarlyArticle
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type
Academic Article
research paper
schema:ScholarlyArticle
isDefinedBy
Covid-on-the-Web dataset
title
Controlling COVID-19 via test-trace-quarantine
Creator
Duchin, Jeffrey
Famulare, Michael
Klein, Daniel
Hart, Gregory
Hagedorn, Brittany
»more»
source
MedRxiv
abstract
COVID-19 containment efforts in the United States so far have largely focused on physical distancing, including school and workplace closures. However, these interventions have come at an enormous societal and economic cost. Here, we use an agent-based model, calibrated to detailed demographic, mobility, and epidemiological data for the Seattle region, to investigate the feasibility of alternative control strategies, focusing on %22test-trace-quarantine%22: a combination of (a) routine testing of primarily symptomatic individuals, (b) tracing and testing their known contacts, and (c) placing their contacts in quarantine. We assess the requirements for implementing this strategy, including its robustness to low compliance, delays, and other factors such as variability in overall transmission rates. We find that for the Seattle setting, if mask compliance remains high and schools remain closed, realistic levels of testing and tracing are sufficient to maintain epidemic control under a return to full workplace and community mobility.
has issue date
2020-07-16
(
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bibo:doi
10.1101/2020.07.15.20154765
has license
medrxiv
sha1sum (hex)
cc257ab7c71cc7a728e2e260a7a4a02c6ce6b444
schema:url
https://doi.org/10.1101/2020.07.15.20154765
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Controlling COVID-19 via test-trace-quarantine
resource representing a document's body
covid:cc257ab7c71cc7a728e2e260a7a4a02c6ce6b444#body_text
is
schema:about
of
named entity 'COVID Tracking Project'
named entity 'disease transmission'
named entity 'numerical computing'
named entity 'SARS-CoV-2'
named entity 'quarantine'
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