Fuzzy association analysis for identifying climatic and socio-demographic factors impacting the spread of COVID-19.

Chatterjee, Sujoy; Chakrabarty, Deepmala; Mukhopadhyay, Anirban

Chatterjee, Sujoy; Chakrabarty, Deepmala; Mukhopadhyay, Anirban.

Afiliación

Chatterjee S; Department of Informatics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India.
Chakrabarty D; Prasanta Chandra Mahalanobis Mahavidyalaya, West Bengal State University, India.
Mukhopadhyay A; Department of Computer Science and Engineering, University of Kalyani, India. Electronic address: anirban@klyuniv.ac.in.

Methods ; 203: 511-522, 2022 07.

Article en En | MEDLINE | ID: mdl-34433092

Asunto(s)

COVID-19; COVID-19/epidemiología; Demografía; Brotes de Enfermedades; Humanos; Pandemias; SARS-CoV-2

Palabras clave

Association rule mining; COVID-19; Climatic factors; Fuzzy association rules; Socio-demographic factors

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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Methods Asunto de la revista: BIOQUIMICA Año: 2022 Tipo del documento: Article País de afiliación: India

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