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Article

A Hybrid Model for COVID-19 Monitoring and Prediction

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Grupo de Investigación Inteligencia Artificial, Departamento de Sistemas e Informática, Universidad de Caldas, Manizales 170004, Colombia
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BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain
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Grupo Investigación GITIR, Departamento de Sistemas e Informática, Universidad de Caldas, Manizales 170004, Colombia
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Air Institute, IoT Digital Innovation Hub, 37188 Salamanca, Spain
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Grupo de investigación CLEV, Universidad de Caldas, Manizales 170004, Colombia
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Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan
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Author to whom correspondence should be addressed.
Academic Editors: Nargis Khan and Lutful Karim
Electronics 2021, 10(7), 799; https://doi.org/10.3390/electronics10070799
Received: 22 February 2021 / Revised: 17 March 2021 / Accepted: 25 March 2021 / Published: 28 March 2021
(This article belongs to the Special Issue Deep Learning for Healthcare Data Analysis)
COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has a case-fatality rate of 2–3%, with higher rates among elderly patients and patients with comorbidities. Radiologically, COVID-19 is characterised by multifocal ground-glass opacities, even for patients with mild disease. Clinically, patients with COVID-19 present respiratory symptoms, which are very similar to other respiratory virus infections. Our knowledge regarding the SARS-CoV-2 virus is still very limited. These facts make it vitally important to establish mechanisms that allow to model and predict the evolution of the virus and to analyze the spread of cases under different circumstances. The objective of this article is to present a model developed for the evolution of COVID in the city of Manizales, capital of the Department of Caldas, Colombia, focusing on the methodology used to allow its application to other cases, as well as on the monitoring tools developed for this purpose. This methodology is based on a hybrid model which combines the population dynamics of the SIR model of differential equations with extrapolations based on recurrent neural networks. This combination provides self-explanatory results in terms of a coefficient that fluctuates with the restraint measures, which may be further refined by expert rules that capture the expected changes in such measures. View Full-Text
Keywords: COVID-19; recurrent neural network; LSTM; compartmental models; curve fitting; prediction COVID-19; recurrent neural network; LSTM; compartmental models; curve fitting; prediction
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MDPI and ACS Style

Castillo Ossa, L.F.; Chamoso, P.; Arango-López, J.; Pinto-Santos, F.; Isaza, G.A.; Santa-Cruz-González, C.; Ceballos-Marquez, A.; Hernández, G.; Corchado, J.M. A Hybrid Model for COVID-19 Monitoring and Prediction. Electronics 2021, 10, 799. https://doi.org/10.3390/electronics10070799

AMA Style

Castillo Ossa LF, Chamoso P, Arango-López J, Pinto-Santos F, Isaza GA, Santa-Cruz-González C, Ceballos-Marquez A, Hernández G, Corchado JM. A Hybrid Model for COVID-19 Monitoring and Prediction. Electronics. 2021; 10(7):799. https://doi.org/10.3390/electronics10070799

Chicago/Turabian Style

Castillo Ossa, Luis F., Pablo Chamoso, Jeferson Arango-López, Francisco Pinto-Santos, Gustavo A. Isaza, Cristina Santa-Cruz-González, Alejandro Ceballos-Marquez, Guillermo Hernández, and Juan M. Corchado. 2021. "A Hybrid Model for COVID-19 Monitoring and Prediction" Electronics 10, no. 7: 799. https://doi.org/10.3390/electronics10070799

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