Optimising SARS-CoV-2 pooled testing strategies on social networks for low-resource settings

Autores
Mazzitello, Karina Irma; Jiang, Yi; Arizmendi, Constancio Miguel
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Controlling the COVID-19 pandemic is an urgent global challenge. The rapid geographic spread of SARS-CoV-2 directly reflects the social structure. Before effective vaccines and treatments are widely available, we have to rely on alternative, non-pharmaceutical interventions, including frequent testing, contact tracing, social distancing, mask wearing, and hand-washing, as public health practises to slow down the spread of the disease. However, frequent testing is the key in the absence of any alternative. We propose a network approach to determine the optimal low resources setting oriented pool testing strategies that identifies infected individuals in a smallnumber of tests and few rounds of testing, at low prevalence of the virus. We simulate stochastic infection curves on societies under quarantine. Allowing some social interaction is possible to keep the COVID-19 curve flat. However, similar results can be strategically obtained searching and isolating infected persons to preserve a healthier social structure. Here, we analyze which are the best strategies to contain the virus applying an algorithm that combine samples and testing them in groups [1]. A relevant parameter to keep infection curves flat using this algorithm is the daily frequency of testing at zones where a high infection rate is reported. On the other hand, thealgorithm efficiency is low for random search of infected people.
Fil: Mazzitello, Karina Irma. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; Argentina
Fil: Jiang, Yi. Department Of Math & Stat, Georgia State University; Estados Unidos
Fil: Arizmendi, Constancio Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; Argentina
Materia
POOL TESTING
SOCIAL NETWORKS
COVID-19
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/135514
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Optimising SARS-CoV-2 pooled testing strategies on social networks for low-resource settingsMazzitello, Karina IrmaJiang, YiArizmendi, Constancio MiguelPOOL TESTINGSOCIAL NETWORKSCOVID-19https://purl.org/becyt/ford/3.3https://purl.org/becyt/ford/3Controlling the COVID-19 pandemic is an urgent global challenge. The rapid geographic spread of SARS-CoV-2 directly reflects the social structure. Before effective vaccines and treatments are widely available, we have to rely on alternative, non-pharmaceutical interventions, including frequent testing, contact tracing, social distancing, mask wearing, and hand-washing, as public health practises to slow down the spread of the disease. However, frequent testing is the key in the absence of any alternative. We propose a network approach to determine the optimal low resources setting oriented pool testing strategies that identifies infected individuals in a smallnumber of tests and few rounds of testing, at low prevalence of the virus. We simulate stochastic infection curves on societies under quarantine. Allowing some social interaction is possible to keep the COVID-19 curve flat. However, similar results can be strategically obtained searching and isolating infected persons to preserve a healthier social structure. Here, we analyze which are the best strategies to contain the virus applying an algorithm that combine samples and testing them in groups [1]. A relevant parameter to keep infection curves flat using this algorithm is the daily frequency of testing at zones where a high infection rate is reported. On the other hand, thealgorithm efficiency is low for random search of infected people.Fil: Mazzitello, Karina Irma. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Jiang, Yi. Department Of Math & Stat, Georgia State University; Estados UnidosFil: Arizmendi, Constancio Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaCornell University2020-12-31info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/135514Mazzitello, Karina Irma; Jiang, Yi; Arizmendi, Constancio Miguel; Optimising SARS-CoV-2 pooled testing strategies on social networks for low-resource settings; Cornell University; Physics and Society; 2020; 31-12-2020; 1-132331-8422CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/2012.15702info:eu-repo/semantics/altIdentifier/url/https://iopscience.iop.org/article/10.1088/1751-8121/ac039b/metainfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:13:49Zoai:ri.conicet.gov.ar:11336/135514instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 15:13:49.307CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Optimising SARS-CoV-2 pooled testing strategies on social networks for low-resource settings
title Optimising SARS-CoV-2 pooled testing strategies on social networks for low-resource settings
spellingShingle Optimising SARS-CoV-2 pooled testing strategies on social networks for low-resource settings
Mazzitello, Karina Irma
POOL TESTING
SOCIAL NETWORKS
COVID-19
title_short Optimising SARS-CoV-2 pooled testing strategies on social networks for low-resource settings
title_full Optimising SARS-CoV-2 pooled testing strategies on social networks for low-resource settings
title_fullStr Optimising SARS-CoV-2 pooled testing strategies on social networks for low-resource settings
title_full_unstemmed Optimising SARS-CoV-2 pooled testing strategies on social networks for low-resource settings
title_sort Optimising SARS-CoV-2 pooled testing strategies on social networks for low-resource settings
dc.creator.none.fl_str_mv Mazzitello, Karina Irma
Jiang, Yi
Arizmendi, Constancio Miguel
author Mazzitello, Karina Irma
author_facet Mazzitello, Karina Irma
Jiang, Yi
Arizmendi, Constancio Miguel
author_role author
author2 Jiang, Yi
Arizmendi, Constancio Miguel
author2_role author
author
dc.subject.none.fl_str_mv POOL TESTING
SOCIAL NETWORKS
COVID-19
topic POOL TESTING
SOCIAL NETWORKS
COVID-19
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.3
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Controlling the COVID-19 pandemic is an urgent global challenge. The rapid geographic spread of SARS-CoV-2 directly reflects the social structure. Before effective vaccines and treatments are widely available, we have to rely on alternative, non-pharmaceutical interventions, including frequent testing, contact tracing, social distancing, mask wearing, and hand-washing, as public health practises to slow down the spread of the disease. However, frequent testing is the key in the absence of any alternative. We propose a network approach to determine the optimal low resources setting oriented pool testing strategies that identifies infected individuals in a smallnumber of tests and few rounds of testing, at low prevalence of the virus. We simulate stochastic infection curves on societies under quarantine. Allowing some social interaction is possible to keep the COVID-19 curve flat. However, similar results can be strategically obtained searching and isolating infected persons to preserve a healthier social structure. Here, we analyze which are the best strategies to contain the virus applying an algorithm that combine samples and testing them in groups [1]. A relevant parameter to keep infection curves flat using this algorithm is the daily frequency of testing at zones where a high infection rate is reported. On the other hand, thealgorithm efficiency is low for random search of infected people.
Fil: Mazzitello, Karina Irma. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; Argentina
Fil: Jiang, Yi. Department Of Math & Stat, Georgia State University; Estados Unidos
Fil: Arizmendi, Constancio Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; Argentina
description Controlling the COVID-19 pandemic is an urgent global challenge. The rapid geographic spread of SARS-CoV-2 directly reflects the social structure. Before effective vaccines and treatments are widely available, we have to rely on alternative, non-pharmaceutical interventions, including frequent testing, contact tracing, social distancing, mask wearing, and hand-washing, as public health practises to slow down the spread of the disease. However, frequent testing is the key in the absence of any alternative. We propose a network approach to determine the optimal low resources setting oriented pool testing strategies that identifies infected individuals in a smallnumber of tests and few rounds of testing, at low prevalence of the virus. We simulate stochastic infection curves on societies under quarantine. Allowing some social interaction is possible to keep the COVID-19 curve flat. However, similar results can be strategically obtained searching and isolating infected persons to preserve a healthier social structure. Here, we analyze which are the best strategies to contain the virus applying an algorithm that combine samples and testing them in groups [1]. A relevant parameter to keep infection curves flat using this algorithm is the daily frequency of testing at zones where a high infection rate is reported. On the other hand, thealgorithm efficiency is low for random search of infected people.
publishDate 2020
dc.date.none.fl_str_mv 2020-12-31
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/135514
Mazzitello, Karina Irma; Jiang, Yi; Arizmendi, Constancio Miguel; Optimising SARS-CoV-2 pooled testing strategies on social networks for low-resource settings; Cornell University; Physics and Society; 2020; 31-12-2020; 1-13
2331-8422
CONICET Digital
CONICET
url http://hdl.handle.net/11336/135514
identifier_str_mv Mazzitello, Karina Irma; Jiang, Yi; Arizmendi, Constancio Miguel; Optimising SARS-CoV-2 pooled testing strategies on social networks for low-resource settings; Cornell University; Physics and Society; 2020; 31-12-2020; 1-13
2331-8422
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/2012.15702
info:eu-repo/semantics/altIdentifier/url/https://iopscience.iop.org/article/10.1088/1751-8121/ac039b/meta
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Cornell University
publisher.none.fl_str_mv Cornell University
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846083285318369280
score 13.22299

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