A Hybrid System For Pandemic Evolution Prediction

• Lilia Muñoz ^{[1] [7]} ; María Alonso-García ^[2] ; Vladimir Villarreal ^{[1] [7]} ; Guillermo Hernández ^[3] ; Mel Nielsen ^[1] ; Francisco Pinto-Santos ; Amilkar Saavedra ^[1] ; Mariana Areiza ^[1] ; Juan Montenegro ^[1] ; Inés Sittón-Candanedo ^[7] ; Yen Caballero-González ^[7] ; Saber Trabelsi ^[4] ; Juan M. Corchado ^{[2] [3] [5] [6]}
  1. [1] Universidad Tecnológica de Panamá

     Universidad Tecnológica de Panamá

     Panamá

     
  2. [2] AIR Institute

     AIR Institute

     Carbajosa de la Sagrada, España

     
  3. [3] Universidad de Salamanca

     Universidad de Salamanca

     Salamanca, España

     
  4. [4] Texas A&M University at Qatar

     Texas A&M University at Qatar

     Catar

     
  5. [5] Osaka Institute of Technology

     Osaka Institute of Technology

     Kita Ku, Japón

     
  6. [6] Universiti Malaysia Kelantan

     Universiti Malaysia Kelantan

     Malasia

     
  7. [7] Centro de Estudios Multidisciplinarios en Ciencia, Ingeniería y Tecnología (CEMCIT-AIP), Panamá

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• Localización: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, ISSN-e 2255-2863, Vol. 11, Nº. 1, 2022, págs. 111-128
• Idioma: inglés
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• Resumen

  • The areas of data science and data engineering have experienced strong advances in recent years. This has had a particular impact on areas such as healthcare, where, as a result of the pandemic caused by the COVID-19 virus, technological development has accelerated. This has led to a need to produce solutions that enable the collection, integration and efficient use of information for decision making scenarios. This is evidenced by the proliferation of monitoring, data collection, analysis, and prediction systems aimed at controlling the pandemic. To go beyond current epidemic prediction possibilities, this article proposes a hybrid model that combines the dynamics of epidemiological processes with the predictive capabilities of artificial neural networks. In addition, the system allows for the introduction of additional information through an expert system, thus allowing the incorporation of additional hypotheses on the adoption of containment measures.