Monitoreo y Predicción de Enfermedades Infecciosas a través del Análisis de Redes Sociales

Pedro Wences; Alicia ●	Martínez; Hugo Estrada; Sabino Miranda

doi:10.17488/RMIB.47.SI-TAIH.1529

Autores/as

Pedro Wences Centro Nacional de Investigación y Desarrollo Tecnlógico https://orcid.org/0000-0001-5622-2831
Alicia ● Martínez https://orcid.org/0000-0002-1071-8599
Hugo Estrada https://orcid.org/0000-0002-1466-7581
Sabino Miranda

DOI:

https://doi.org/10.17488/RMIB.47.SI-TAIH.1529

Palabras clave:

COVID-19, Redes sociales, BERT, Gompertz, Vigilancia epidemiológica

Resumen

Este artículo presenta un enfoque integral para el monitoreo y la predicción de enfermedades infecciosas a partir de datos de redes sociales, centrándose principalmente en la pandemia de COVID-19. A través de la adquisición y el análisis de tweets georreferenciados se capturan señales tempranas de contagio, complementando de este modo los métodos tradicionales de vigilancia epidemiológica. El núcleo del sistema de clasificación se basa en el modelo BERT, el cual permite identificar automáticamente afirmaciones de infección, mientras que la función de Gompertz se utiliza para estimar el crecimiento de los contagios en horizontes de 1 a 5 días. La correlación significativa entre los datos extraídos de Twitter y las cifras oficiales reportadas por la Secretaría de Salud demuestra la solidez del método, además de evidenciar el potencial de los datos de las redes sociales para anticipar brotes y optimizar la toma de decisiones. Asimismo, la inclusión de datos geoespaciales posibilita la identificación de zonas con mayor nivel de riesgo, aportando un recurso valioso para las autoridades de salud y la población en general. Con ello se promueve una respuesta más rápida y coordinada ante futuras amenazas sanitarias.

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