A Hybrid System For Pandemic Evolution Prediction - Muñoz, Lilia; Alonso-García, María; Villarreal, Vladimir; Hernández, Guillermo; Nielsen, Mel; Pinto-Santos, Francisco; Saavedra, Amilkar; Areiza, Mariana; Montenegro, Juan; Sittón-Candanedo, Inés; Caballero-González, Yen; Trabelsi, Saber; et al. (ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 2022)
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.