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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/135514
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T10:16:29Zoai: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:34982025-09-29 10:16:29.928CONICET 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 |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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1844614110221697024 |
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13.070432 |