A Hybrid System For Pandemic Evolution Prediction
The areas of data science and data engineering have experienced strong advances in recent years. This has had a particular impact in 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...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Ediciones Universidad de Salamanca
2022-06-01
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| Series: | Advances in Distributed Computing and Artificial Intelligence Journal |
| Subjects: | |
| Online Access: | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/28093 |
| _version_ | 1797943681060175872 |
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| author | Lilia Muñoz María Alonso-García Vladimir Villarreal Guillermo Hernández Mel Nielsen Francisco Pinto-Santos Amilkar Saavedra Mariana Areiza Juan Montenegro Inés Sittón-Candanedo Yen Caballero-González Saber Trabelsi Juan M. Corchado |
| author_facet | Lilia Muñoz María Alonso-García Vladimir Villarreal Guillermo Hernández Mel Nielsen Francisco Pinto-Santos Amilkar Saavedra Mariana Areiza Juan Montenegro Inés Sittón-Candanedo Yen Caballero-González Saber Trabelsi Juan M. Corchado |
| author_sort | Lilia Muñoz |
| collection | DOAJ |
| description | The areas of data science and data engineering have experienced strong advances in recent years. This has had a particular impact in 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. This article proposes a hybrid model that combines the dynamics of epidemiological processes with the predictive capabilities of artificial neural networks to go beyond the prediction of the first ones. 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. |
| first_indexed | 2024-04-10T20:28:01Z |
| format | Article |
| id | doaj.art-f1871192fe3d4ea7a5060ccd2a4e0f1e |
| institution | Directory Open Access Journal |
| issn | 2255-2863 |
| language | English |
| last_indexed | 2024-04-10T20:28:01Z |
| publishDate | 2022-06-01 |
| publisher | Ediciones Universidad de Salamanca |
| record_format | Article |
| series | Advances in Distributed Computing and Artificial Intelligence Journal |
| spelling | doaj.art-f1871192fe3d4ea7a5060ccd2a4e0f1e2023-01-25T08:53:33ZengEdiciones Universidad de SalamancaAdvances in Distributed Computing and Artificial Intelligence Journal2255-28632022-06-0111111112810.14201/adcaij.2809333554A Hybrid System For Pandemic Evolution PredictionLilia Muñoz0https://orcid.org/0000-0002-4011-2715María Alonso-García1https://orcid.org/0000-0003-3185-9574Vladimir Villarreal2https://orcid.org/0000-0003-4678-5977Guillermo Hernández3https://orcid.org/0000-0002-7481-5961Mel Nielsen4https://orcid.org/0000-0003-4897-0973Francisco Pinto-Santos5https://orcid.org/0000-0002-9500-9336Amilkar Saavedra6https://orcid.org/0000-0002-8158-2107Mariana Areiza7https://orcid.org/0000-0003-2937-4383Juan Montenegro8https://orcid.org/0000-0001-9820-3794Inés Sittón-Candanedo9https://orcid.org/0000-0001-8953-7848Yen Caballero-González10https://orcid.org/0000-0002-7493-6683Saber Trabelsi11Juan M. Corchado12https://orcid.org/0000-0002-2829-1829Grupo de Investigación en Tecnologías Computacionales Emergentes (GITCE), Universidad Tecnológica de PanamáAir Institute, SalamancaGrupo de Investigación en Tecnologías Computacionales Emergentes (GITCE), Universidad Tecnológica de PanamáBISITE Research Group, University of Salamanca. Calle Espejo s/n. Edificio Multiusos I+D+i, 37007, SalamancaGrupo de Investigación en Tecnologías Computacionales Emergentes (GITCE), Universidad Tecnológica de PanamáBISITE Research Group, University of Salamanca. Calle Espejo s/n. Edificio Multiusos I+D+i, 37007, SalamancaGrupo de Investigación en Tecnologías Computacionales Emergentes (GITCE), Universidad Tecnológica de PanamáGrupo de Investigación en Tecnologías Computacionales Emergentes (GITCE), Universidad Tecnológica de PanamáGrupo de Investigación en Tecnologías Computacionales Emergentes (GITCE), Universidad Tecnológica de PanamáCentro de Estudios Multidisciplinarios en Ciencia, Ingeniería y Tecnología (CEMCIT-AIP), 0819 Panama CityCentro de Estudios Multidisciplinarios en Ciencia, Ingeniería y Tecnología (CEMCIT-AIP), 0819 Panama CityTexas A&M University at QatarAir Institute, SalamancaThe areas of data science and data engineering have experienced strong advances in recent years. This has had a particular impact in 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. This article proposes a hybrid model that combines the dynamics of epidemiological processes with the predictive capabilities of artificial neural networks to go beyond the prediction of the first ones. 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.https://revistas.usal.es/cinco/index.php/2255-2863/article/view/28093covid-19sir modelcompartmental modelspredictionlong short-term memory |
| spellingShingle | Lilia Muñoz María Alonso-García Vladimir Villarreal Guillermo Hernández Mel Nielsen Francisco Pinto-Santos Amilkar Saavedra Mariana Areiza Juan Montenegro Inés Sittón-Candanedo Yen Caballero-González Saber Trabelsi Juan M. Corchado A Hybrid System For Pandemic Evolution Prediction Advances in Distributed Computing and Artificial Intelligence Journal covid-19 sir model compartmental models prediction long short-term memory |
| title | A Hybrid System For Pandemic Evolution Prediction |
| title_full | A Hybrid System For Pandemic Evolution Prediction |
| title_fullStr | A Hybrid System For Pandemic Evolution Prediction |
| title_full_unstemmed | A Hybrid System For Pandemic Evolution Prediction |
| title_short | A Hybrid System For Pandemic Evolution Prediction |
| title_sort | hybrid system for pandemic evolution prediction |
| topic | covid-19 sir model compartmental models prediction long short-term memory |
| url | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/28093 |
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