Optimizing COVID-19 control with asymptomatic surveillance testing in a university environment

The high proportion of transmission events derived from asymptomatic or presymptomatic infections make SARS-CoV-2, the causative agent in COVID-19, difficult to control through the traditional non-pharmaceutical interventions (NPIs) of symptom-based isolation and contact tracing. As a consequence, m...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Cara E. Brook, Graham R. Northrup, Alexander J. Ehrenberg, Jennifer A. Doudna, Mike Boots
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	COVID-19 Asymptomatic surveillance testing Branching process model University control Infectious and parasitic diseases RC109-216
Acceso en línea:	https://doaj.org/article/ff101cfbcf304bc69f268905fabdb3cb
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ff101cfbcf304bc69f268905fabdb3cb
record_format	dspace
spelling	oai:doaj.org-article:ff101cfbcf304bc69f268905fabdb3cb2021-11-22T04:19:56ZOptimizing COVID-19 control with asymptomatic surveillance testing in a university environment1755-436510.1016/j.epidem.2021.100527https://doaj.org/article/ff101cfbcf304bc69f268905fabdb3cb2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1755436521000712https://doaj.org/toc/1755-4365The high proportion of transmission events derived from asymptomatic or presymptomatic infections make SARS-CoV-2, the causative agent in COVID-19, difficult to control through the traditional non-pharmaceutical interventions (NPIs) of symptom-based isolation and contact tracing. As a consequence, many US universities developed asymptomatic surveillance testing labs, to augment NPIs and control outbreaks on campus throughout the 2020–2021 academic year (AY); several of those labs continue to support asymptomatic surveillance efforts on campus in AY2021–2022. At the height of the pandemic, we built a stochastic branching process model of COVID-19 dynamics at UC Berkeley to advise optimal control strategies in a university environment. Our model combines behavioral interventions in the form of group size limits to deter superspreading, symptom-based isolation, and contact tracing, with asymptomatic surveillance testing. We found that behavioral interventions offer a cost-effective means of epidemic control: group size limits of six or fewer greatly reduce superspreading, and rapid isolation of symptomatic infections can halt rising epidemics, depending on the frequency of asymptomatic transmission in the population. Surveillance testing can overcome uncertainty surrounding asymptomatic infections, with the most effective approaches prioritizing frequent testing with rapid turnaround time to isolation over test sensitivity. Importantly, contact tracing amplifies population-level impacts of all infection isolations, making even delayed interventions effective. Combination of behavior-based NPIs and asymptomatic surveillance also reduces variation in daily case counts to produce more predictable epidemics. Furthermore, targeted, intensive testing of a minority of high transmission risk individuals can effectively control the COVID-19 epidemic for the surrounding population. Even in some highly vaccinated university settings in AY2021–2022, asymptomatic surveillance testing offers an effective means of identifying breakthrough infections, halting onward transmission, and reducing total caseload. We offer this blueprint and easy-to-implement modeling tool to other academic or professional communities navigating optimal return-to-work strategies.Cara E. BrookGraham R. NorthrupAlexander J. EhrenbergJennifer A. DoudnaMike BootsElsevierarticleCOVID-19Asymptomatic surveillance testingBranching process modelUniversity controlInfectious and parasitic diseasesRC109-216ENEpidemics, Vol 37, Iss , Pp 100527- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 Asymptomatic surveillance testing Branching process model University control Infectious and parasitic diseases RC109-216
spellingShingle	COVID-19 Asymptomatic surveillance testing Branching process model University control Infectious and parasitic diseases RC109-216 Cara E. Brook Graham R. Northrup Alexander J. Ehrenberg Jennifer A. Doudna Mike Boots Optimizing COVID-19 control with asymptomatic surveillance testing in a university environment
description	The high proportion of transmission events derived from asymptomatic or presymptomatic infections make SARS-CoV-2, the causative agent in COVID-19, difficult to control through the traditional non-pharmaceutical interventions (NPIs) of symptom-based isolation and contact tracing. As a consequence, many US universities developed asymptomatic surveillance testing labs, to augment NPIs and control outbreaks on campus throughout the 2020–2021 academic year (AY); several of those labs continue to support asymptomatic surveillance efforts on campus in AY2021–2022. At the height of the pandemic, we built a stochastic branching process model of COVID-19 dynamics at UC Berkeley to advise optimal control strategies in a university environment. Our model combines behavioral interventions in the form of group size limits to deter superspreading, symptom-based isolation, and contact tracing, with asymptomatic surveillance testing. We found that behavioral interventions offer a cost-effective means of epidemic control: group size limits of six or fewer greatly reduce superspreading, and rapid isolation of symptomatic infections can halt rising epidemics, depending on the frequency of asymptomatic transmission in the population. Surveillance testing can overcome uncertainty surrounding asymptomatic infections, with the most effective approaches prioritizing frequent testing with rapid turnaround time to isolation over test sensitivity. Importantly, contact tracing amplifies population-level impacts of all infection isolations, making even delayed interventions effective. Combination of behavior-based NPIs and asymptomatic surveillance also reduces variation in daily case counts to produce more predictable epidemics. Furthermore, targeted, intensive testing of a minority of high transmission risk individuals can effectively control the COVID-19 epidemic for the surrounding population. Even in some highly vaccinated university settings in AY2021–2022, asymptomatic surveillance testing offers an effective means of identifying breakthrough infections, halting onward transmission, and reducing total caseload. We offer this blueprint and easy-to-implement modeling tool to other academic or professional communities navigating optimal return-to-work strategies.
format	article
author	Cara E. Brook Graham R. Northrup Alexander J. Ehrenberg Jennifer A. Doudna Mike Boots
author_facet	Cara E. Brook Graham R. Northrup Alexander J. Ehrenberg Jennifer A. Doudna Mike Boots
author_sort	Cara E. Brook
title	Optimizing COVID-19 control with asymptomatic surveillance testing in a university environment
title_short	Optimizing COVID-19 control with asymptomatic surveillance testing in a university environment
title_full	Optimizing COVID-19 control with asymptomatic surveillance testing in a university environment
title_fullStr	Optimizing COVID-19 control with asymptomatic surveillance testing in a university environment
title_full_unstemmed	Optimizing COVID-19 control with asymptomatic surveillance testing in a university environment
title_sort	optimizing covid-19 control with asymptomatic surveillance testing in a university environment
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/ff101cfbcf304bc69f268905fabdb3cb
work_keys_str_mv	AT caraebrook optimizingcovid19controlwithasymptomaticsurveillancetestinginauniversityenvironment AT grahamrnorthrup optimizingcovid19controlwithasymptomaticsurveillancetestinginauniversityenvironment AT alexanderjehrenberg optimizingcovid19controlwithasymptomaticsurveillancetestinginauniversityenvironment AT jenniferadoudna optimizingcovid19controlwithasymptomaticsurveillancetestinginauniversityenvironment AT mikeboots optimizingcovid19controlwithasymptomaticsurveillancetestinginauniversityenvironment
_version_	1718418260691517440

Optimizing COVID-19 control with asymptomatic surveillance testing in a university environment

Ejemplares similares