{"id":24091,"date":"2022-02-17T09:40:56","date_gmt":"2022-02-17T15:40:56","guid":{"rendered":"https:\/\/saluddigital.com\/?p=24091"},"modified":"2025-10-21T10:19:47","modified_gmt":"2025-10-21T16:19:47","slug":"programa-de-pruebas-basadas-en-datos-mejoro-la-deteccion-de-covid-19-y-redujo-la-transmision-comunitaria","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/programa-de-pruebas-basadas-en-datos-mejoro-la-deteccion-de-covid-19-y-redujo-la-transmision-comunitaria\/","title":{"rendered":"Data-driven testing program improved detection of COVID-19 and reduced community transmission"},"content":{"rendered":"
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Study published in Nature analyzed a COVID-19 testing program based on data and machine learning; was implemented in a university with favorable results. <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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At the University of Notre Dame, a group of researchers implemented a COVID-19 testing program based on two easy-to-interpret machine learning models, which sought to predict which students were at high risk of being infected and should be tested.<\/p>

Both models used a social network representation of the university community in which nodes represented a student and each edge represented a connection between two people, whether it was a common class, teammates on sports teams, or roommates.<\/p>

The first model is an algorithm that was created to directly predict each student's individual high or low risk of suffering from COVID-19. And the second was trained to predict which pairs of students were most likely to be close contacts, using the contact tracing system used a semester earlier.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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"The success of this program suggests that machine learning strategies can improve the effectiveness of surveillance testing or other efforts to efficiently allocate testing resources and reduce community transmission," the authors explain.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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In this sense, both models were a useful tool for reducing community transmission, since of more than 115 thousand tests applied at the time the study was carried out (March 3 to April 30, 2021), 18.1% corresponded to tests dictated by these prediction models.<\/p>

In addition, the tests carried out on students through the predictions of this model had a higher percentage of positivity than the general tests 0.53 against 0.38%.<\/p>

In this sense, this test showed that, based on machine learning models, universities and workplaces with a large number of employees can make better use of the resources they allocate to COVID-19 tests and not only generate savings, but reduce mass infections.<\/p>

Check the full study at the following link:<\/p>

https:\/\/www.nature.com\/articles\/s41746-022-00562-4<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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NATURE<\/strong><\/p>

https:\/\/www.nature.com\/articles\/s41746-022-00562-4<\/a><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

Estudio publicado en Nature analiz\u00f3 un programa de pruebas de COVID-19 basadas en datos y en aprendizaje autom\u00e1tico; fue implementado en una universidad con resultados favorables. En la Universidad de Notre Dame un grupo de investigadores implementaron un programa de pruebas de COVID-19 basado en dos modelos de aprendizaje autom\u00e1tico f\u00e1ciles de interpretar, que buscaron predecir que estudiantes ten\u00edan riesgo elevado de estar contagiados y deb\u00edan realizarse la prueba. Los dos modelos utilizaron una representaci\u00f3n de red social de la comunidad universitaria en la que los nodos representaban a un estudiante y cada borde representaba una conexi\u00f3n entre dos personas, ya fuera una clase en com\u00fan, compa\u00f1eros en equipos deportivos, o compa\u00f1eros de cuarto. El primer modelo es un algoritmo que fue creado para predecir directamente el riesgo individual alto o bajo, de cada estudiante de padecer COVID-19. Y el segundo fue entrenado para predecir qu\u00e9 pares de estudiantes ten\u00edan m\u00e1s probabilidades de ser contactos cercanos, utilizando el sistema de rastreo de contactos utilizado un semestre antes. \u201cEl \u00e9xito de este programa sugiere que las estrategias de aprendizaje autom\u00e1tico pueden mejorar la efectividad de las pruebas de vigilancia u otros esfuerzos para distribuir eficientemente los recursos de prueba y reducir la transmisi\u00f3n comunitaria\u201d, explican los autores. En este sentido ambos modelos fueron una herramienta \u00fatil para la reducci\u00f3n de la transmisi\u00f3n comunitaria, pues de m\u00e1s de 115 mil pruebas aplicadas en el tiempo que se realiz\u00f3 el estudio (3 de marzo al 30 de abril de 2021), 18.1% correspondieron a pruebas dictadas por estos modelos de predicci\u00f3n. Adem\u00e1s, las pruebas realizadas a estudiantes a trav\u00e9s de las predicciones de este modelo tuvieron un porcentaje de positividad m\u00e1s alto que las pruebas generales 0.53 contra 0.38%. En este sentido, esta prueba demostr\u00f3 que, apoy\u00e1ndose en los modelos de aprendizaje autom\u00e1tico, las universidades y centros de trabajo con gran cantidad de empleados, pueden aprovechar mejor los recursos que destinan a pruebas de COVID-19 y no solo generar un ahorro, sino reducir los contagios masivos. Consulta el estudio completo en el siguiente enlace: https:\/\/www.nature.com\/articles\/s41746-022-00562-4 BIBLIOGRAF\u00cdA NATURE https:\/\/www.nature.com\/articles\/s41746-022-00562-4<\/p>","protected":false},"author":1,"featured_media":24092,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3399,156,3393,160,1418],"tags":[145],"class_list":["post-24091","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-analitica","category-big-data","category-inteligencia-artificial-y-ciencia","category-noticias","category-resena-de-publicaciones-cientificas","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/24091","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/comments?post=24091"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/24091\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/24092"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=24091"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=24091"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=24091"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}