A Hybrid System For Pandemic Evolution Prediction

  1. Lilia Muñoz 23
  2. María Alonso García 1
  3. Vladimir Villarreal 23
  4. Guillermo Hernández González 4
  5. Mel Nielsen 2
  6. Francisco Pinto Santos
  7. Amilkar Abdiel Saavedra Suñé 2
  8. Mariana Areiza 2
  9. Juan Montenegro 2
  10. Inés Sittón Candanedo 3
  11. Yen Air Caballero González 3
  12. Saber Trabelsi 5
  13. Juan Manuel Corchado Rodríguez 1467
  1. 1 Air Institute, Salamanca, Spain
  2. 2 Grupo de Investigación en Tecnologías Computacionales Emergentes (GITCE), Universidad Tecnológica de Panamá, Panamá
  3. 3 Centro de Estudios Multidisciplinarios en Ciencia, Ingeniería y Tecnología (CEMCIT-AIP), Panamá
  4. 4 BISITE Research Group, University of Salamanca
  5. 5 Texas A&M University at Qatar, Qatar
  6. 6 Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka, Japan
  7. 7 Pusat Komputeran dan Informatik, Universiti Malaysia Kelantan, Kelantan, Malaysia
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

ISSN: 2255-2863

Year of publication: 2022

Volume: 11

Issue: 1

Pages: 111-128

Type: Article

DOI: 10.14201/ADCAIJ.28093 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal


Cited by

  • Scopus Cited by: 0 (29-11-2023)
  • Web of Science Cited by: 0 (20-10-2023)
  • Dimensions Cited by: 0 (27-03-2023)


  • Social Sciences: C

Scopus CiteScore

  • Year 2022
  • CiteScore of the Journal : 0.7
  • Area: Information Systems Percentile: 10
  • Area: Computer Science Applications Percentile: 9
  • Area: Artificial Intelligence Percentile: 9
  • Area: Computer Networks and Communications Percentile: 8

Journal Citation Indicator (JCI)

  • Year 2022
  • Journal Citation Indicator (JCI): 0.09
  • Best Quartile: Q4
  • Area: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Quartile: Q4 Rank in area: 190/192


(Data updated as of 27-03-2023)
  • Total citations: 0
  • Recent citations: 0


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.

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