Título Data Driven Modelling of Coronavirus Spread in Spain
Publicación externa No
Medio Comput. Mater. Continua
Alcance Article
Naturaleza Científica
Cuartil JCR 2
Cuartil SJR 1
Impacto JCR 3.77200
Impacto SJR 0.78800
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090855958&doi=10.32604%2fcmc.2020.011243&partnerID=40&md5=40b273000ea80cdf02361475f70863e1
Fecha de publicacion 01/03/2020
ISI 000557868500002
Scopus Id 2-s2.0-85090855958
DOI 10.32604/cmc.2020.011243
Abstract During the late months of last year, a novel coronavirus was detected in\n Hubei, China. The virus, since then, has spread all across the globe\n forcing Word Health Organization (WHO) to declare COVID-19 outbreak a\n pandemic. In Spain, the virus started infecting the country slowly until\n rapid growth of infected people occurred in Madrid, Barcelona and other\n major cities. The government in an attempt to stop the rapssid spread of\n the virus and ensure that health system will not reach its capacity,\n implement strict measures by putting the entire country in quarantine.\n The duration of these measures, depends on the evolution of the virus in\n Spain. In this study, a Deep Neural Network approach using Monte Carlo\n is proposed for generating a database to train networks for estimating\n the optimal parameters of a SIR epidemiology model. The number of total\n infected people as of April 7 in Spain is considered as input to the\n Deep Neural Network. The adaptability of the model was evaluated using\n the latest data upon completion of this paper, i.e., April 14. The date\n range for the peak of infected people (i.e., active cases) based on the\n new information is estimated to be within 74 to 109 days after the first\n recorded case of COVID-19 in Spain. In addition, a curve fitting measure\n based on the squared Euclidean distance indicates that according to the\n current data the peak might occur before the 86 th day. Collectively,\n Deep Neural Networks have proven accurate and useful tools in handling\n big epidemiological data and for peak prediction estimates.
Palabras clave Coronavirus; deep neural network; machine learning; Monte Carlo simulation; SIR model
Miembros de la Universidad Loyola

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