@Article{cmc.2020.011243,
AUTHOR = {G. N. Baltas, F. A. Prieto, M. Frantzi, C. R. Garcia-Alonso, P. Rodriguez},
TITLE = {Data Driven Modelling of Coronavirus Spread in Spain},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {3},
PAGES = {1343--1357},
URL = {http://www.techscience.com/cmc/v64n3/39433},
ISSN = {1546-2226},
ABSTRACT = {During the late months of last year, a novel coronavirus was detected in Hubei, 
China. The virus, since then, has spread all across the globe forcing Word Health 
Organization (WHO) to declare COVID-19 outbreak a pandemic. In Spain, the virus 
started infecting the country slowly until rapid growth of infected people occurred in 
Madrid, Barcelona and other major cities. The government in an attempt to stop the rapssid 
spread of the virus and ensure that health system will not reach its capacity, implement 
strict measures by putting the entire country in quarantine. The duration of these measures, 
depends on the evolution of the virus in Spain. In this study, a Deep Neural Network 
approach using Monte Carlo is proposed for generating a database to train networks for 
estimating the optimal parameters of a SIR epidemiology model. The number of total 
infected people as of April 7 in Spain is considered as input to the Deep Neural Network. 
The adaptability of the model was evaluated using the latest data upon completion of this 
paper, i.e., April 14. The date range for the peak of infected people (i.e., active cases) based 
on the new information is estimated to be within 74 to 109 days after the first recorded case 
of COVID-19 in Spain. In addition, a curve fitting measure based on the squared Euclidean 
distance indicates that according to the current data the peak might occur before the 86th
day. Collectively, Deep Neural Networks have proven accurate and useful tools in handling 
big epidemiological data and for peak prediction estimates.},
DOI = {10.32604/cmc.2020.011243}
}