{"id":12314,"date":"2021-01-29T09:25:51","date_gmt":"2021-01-29T15:25:51","guid":{"rendered":"https:\/\/saluddigital.com\/?p=12314"},"modified":"2025-10-21T13:06:31","modified_gmt":"2025-10-21T19:06:31","slug":"combatiendo-la-desinformacion-sobre-covid-19-con-analisis-predictivos","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/comunidades-conectadas\/combatiendo-la-desinformacion-sobre-covid-19-con-analisis-predictivos\/","title":{"rendered":"Combating COVID-19 misinformation with predictive analytics"},"content":{"rendered":"
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University students in the United States developed an educational strategy for populations at high risk for COVID-19 based on a population vulnerability map.<\/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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Predictive models have been of great importance in the fight against the pandemic. They have been developed by different academic and health organizations, becoming a valuable tool for planning and decision making in the fight against COVID-19. For example, for the resource management in hospitals, as well as the administration of health personnel.<\/p>

Predictive analytics provide insight through data into the pandemic behavior and make estimates, for example, on mortality and incidence of COVID-19. \"Predictive analytics is statistical analysis that uses data mining, machine learning and algorithms based on historical data sets to identify patterns of behavior and trends to predict future scenarios,\" explains the Pan American Health Organization in a document on Predictive Modeling.<\/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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Currently, thanks to the resources available, such as technology that allows the processing of millions of data, the use of Big Data and other resources, have boosted the use of analytical mechanisms of this kind.<\/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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However, in the current pandemic scenario, predictive analytics does not only work for epidemiological estimates or for resource management in health systems. Recently, students at the Medical University of South Carolina (MUSC) created an educational program based on predictive analytics techniques, targeting populations at increased risk for COVID-19 complications. To develop the strategy, they used the Jvion COVID-19 Community Vulnerability Map, a predictive analytics-based tool that specializes in identifying at-risk populations.<\/p>

\u201cWe decided that patients over age 65 and about 80 census tracks, which were labeled extremely high risk, would be our first priority. Our next tier would be patients over 65 and high-risk areas as labeled by the vulnerability map. That left us with about 28,000 people\u201d Alan Snyder, a medical student at MUSC, explained to a specialized media outlet.<\/p>

According to the estimates made through the map, they found approximately 28,000 people who met the high-risk labels for COVID-19, so they were people over the age of 65.<\/p>

After involving volunteers, Snyder explained that the project approach consisted of three levels. First was the educational and information strategy about the virus that looked at information available from the CDC. \u201cIn the modern era, there's a lot of polarization of opinions and beliefs. We wanted to ensure that we share information that\u2019s based in science and has proven to be helpful. Even if we're only contacting one person, let's say over the phone, that person may talk to their family and that information will spread,\u201d Snyder explained.<\/p>

The second level included a social determinants of health questionnaire to assess other patient risk factors. After the assessment, the third step was to guide patients to social services.<\/p>

\u201cOf course, we would love to inform everybody. And to an extent we did \u2013 we created a subject matter expert COVID-19 YouTube channel tied to MUSC, and we created an email so we could communicate with all 28,000 people we contacted. But we believed those personalized interventions would have the greatest impact on those who we knew had the highest risk of poor outcomes,\u201d he concluded.<\/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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\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t BIBLIOGRAPHY<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t

HEALTH IT ANALYTICS<\/strong><\/p>

https:\/\/healthitanalytics.com\/news\/combating-covid-19-misinformation-with-predictive-analytics<\/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":"

Estudiantes de universidad en Estados Unidos realizaron una estrategia educativa para poblaci\u00f3n de alto riesgo ante COVID-19 tomando como base un mapa sobre la vulnerabilidad de la poblaci\u00f3n. Los modelos predictivos, han sido de gran importancia en el combate contra la pandemia. Han sido desarrollados por diferentes organizaciones acad\u00e9micas y de salud, logrando ser un apoyo para la planeaci\u00f3n y la toma de decisiones en la lucha contra el COVID-19. Por ejemplo, para la gesti\u00f3n de los recursos en hospitales, as\u00ed como la administraci\u00f3n del personal de salud. Los an\u00e1lisis predictivos permiten conocer a trav\u00e9s de los datos c\u00f3mo es el comportamiento de la pandemia y realizar estimaciones, por ejemplo, sobre la mortalidad y la incidencia de COVID-19. \u201cLa anal\u00edtica predictiva es un an\u00e1lisis estad\u00edstico que utiliza la miner\u00eda de datos, el aprendizaje autom\u00e1tico y los algoritmos basados en series de datos hist\u00f3ricos para identificar patrones de comportamiento y tendencias para predecir escenarios futuros\u201d, explica la Organizaci\u00f3n Panamericana de la Salud en un documento sobre Modelos predictivos. Actualmente gracias a los recursos disponibles, como la tecnolog\u00eda que permite el procesamiento de millones de datos, el uso de Big Data y entre otros recursos, han potenciado el uso de mecanismo anal\u00edticos de esta clase. Sin embargo, en el escenario actual de la pandemia, el an\u00e1lisis predictivo no solamente funciona para realizar estimaciones epidemiol\u00f3gicas o para la gesti\u00f3n de recursos en sistemas de salud. Recientemente estudiantes de la Universidad M\u00e9dica de Carolina del Sur (MUSC, por sus siglas en ingl\u00e9s) crearon un programa educativo basado en t\u00e9cnicas de an\u00e1lisis predictivo, orientado a poblaci\u00f3n con mayor riesgo de sufrir complicaciones por COVID-19. Para desarrollar la estrategia, utilizaron el mapa Jvion COVID-19 Community Vulnerability Map, herramienta basada en el an\u00e1lisis predictivo especializada en identificar a la poblaci\u00f3n de riesgo. \u201cDecidimos que los pacientes mayores de 65 a\u00f1os y alrededor de 80 pistas censales, que estaban etiquetadas como de riesgo extremadamente alto, ser\u00edan nuestra primera prioridad.\u00a0Nuestro siguiente nivel ser\u00edan los pacientes mayores de 65 a\u00f1os y las \u00e1reas de alto riesgo seg\u00fan lo etiquetado por el mapa de vulnerabilidad.\u00a0Eso nos dej\u00f3 con unas 28.000 personas\u201d, explic\u00f3 Alan Snyder, estudiante de medicina de la MUSC a un medio especializado. De acuerdo a las estimaciones realizada a trav\u00e9s del mapa, encontraron 28 mil personas aproximadamente, que cumplieron con las etiquetas de riesgo alto ante COVID-19, por lo que se trataron de personas mayores de 65 a\u00f1os. Posteriormente, tras involucrar a voluntarios, Snyder explic\u00f3 que el enfoque del proyecto consist\u00eda en tres niveles. Primero la estrategia educativa y de informaci\u00f3n sobre el virus que contempl\u00f3 la informaci\u00f3n disponible por parte de los CDC. \u201cEn la era moderna, hay mucha polarizaci\u00f3n de opiniones y creencias.\u00a0Quer\u00edamos asegurarnos de compartir informaci\u00f3n basada en la ciencia y que ha demostrado ser \u00fatil.\u00a0Incluso si solo nos contactamos con una persona, digamos por tel\u00e9fono, esa persona puede hablar con su familia y esa informaci\u00f3n se difundir\u00e1\u201d, explic\u00f3 Snyder. El segundo nivel incluy\u00f3 un cuestionario de determinantes sociales de la salud, para evaluar otros factores de riesgo de los pacientes. Despu\u00e9s de la evaluaci\u00f3n, el tercer paso fue guiar a los pacientes a servicios sociales. \u201cPor supuesto, nos encantar\u00eda informar a todos.\u00a0Y hasta cierto punto lo hicimos: creamos un canal de YouTube experto en la materia, COVID-19, vinculado a MUSC, y creamos un correo electr\u00f3nico para poder comunicarnos con las 28,000 personas con las que contactamos.\u00a0Pero cre\u00edamos que esas intervenciones personalizadas tendr\u00edan el mayor impacto en aquellos que sab\u00edamos que ten\u00edan el mayor riesgo de malos resultados\u201d, concluy\u00f3. BIBLIOGRAF\u00cdA HEALTH IT ANALYTICS https:\/\/healthitanalytics.com\/news\/combating-covid-19-misinformation-with-predictive-analytics<\/p>","protected":false},"author":1,"featured_media":12315,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[156,154],"tags":[145],"class_list":["post-12314","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data","category-comunidades-conectadas","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/12314","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=12314"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/12314\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/12315"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=12314"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=12314"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=12314"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}