A Hybrid System For Pandemic Evolution Prediction

  1. Lilia Muñoz 23
  2. María Alonso-García 1
  3. Vladimir Villarreal 23
  4. Guillermo Hernández 4
  5. Mel Nielsen 2
  6. Francisco Pinto-Santos
  7. Amilkar Saavedra 2
  8. Mariana Areiza 2
  9. Juan Montenegro 2
  10. Inés Sittón-Candanedo 3
  11. Yen Caballero-González 3
  12. Saber Trabelsi 5
  13. Juan M. Corchado 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
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Journal:
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

Sustainable development goals

Abstract

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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