dc.creator | Gómez Expósito, Antonio | es |
dc.creator | Rosendo Macías, José Antonio | es |
dc.creator | González Cagigal, Miguel Ángel | es |
dc.date.accessioned | 2022-07-29T10:05:19Z | |
dc.date.available | 2022-07-29T10:05:19Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Gómez Expósito, A., Rosendo Macías, J.A. y González Cagigal, M.Á. (2022). Monitoring and Tracking the Evolution of a Viral Epidemic Through Nonlinear Kalman Filtering: Application to the COVID-19 Case. IEEE Journal of Biomedical and health informatics, 26 (4), 1441-1452. | |
dc.identifier.issn | 2168-2194 (impreso) | es |
dc.identifier.issn | 2168-2208 (electrónico) | es |
dc.identifier.uri | https://hdl.handle.net/11441/136001 | |
dc.description.abstract | This work presents a novel methodology for
systematically processing the time series that report the
number of positive, recovered and deceased cases from a
viral epidemic, such as Covid-19. The main objective is to
unveil the evolution of the number of real infected people,
and consequently to predict the peak of the epidemic and
subsequent evolution. For this purpose, an original nonlinear model relating the raw data with the time-varying geometric ratio of infected people is elaborated, and a Kalman
Filter is used to estimate the involved state variables. A
hypothetical simulated case is used to show the adequacy
and limitations of the proposed method. Then, several
countries, including China, South Korea, Italy, Spain, U.K.
and the USA, are tested to illustrate its behavior when reallife data are processed. The results obtained clearly show
the beneficial effect of the severe lockdowns imposed by
many countries worldwide, but also that the softer social
distancing measures adopted afterwards have been almost
always insufficient to prevent the subsequent virus waves. | es |
dc.format | application/pdf | es |
dc.format.extent | 12 p. | es |
dc.language.iso | eng | es |
dc.publisher | IEEE Explore | es |
dc.relation.ispartof | IEEE Journal of Biomedical and health informatics, 26 (4), 1441-1452. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Nonlinear Kalman filtering | es |
dc.subject | Parameter estimation | es |
dc.subject | Covid-19 | es |
dc.subject | Geometric series | es |
dc.title | Monitoring and Tracking the Evolution of a Viral Epidemic Through Nonlinear Kalman Filtering: Application to the COVID-19 Case | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería Eléctrica | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9367270 | es |
dc.identifier.doi | 10.1109/JBHI.2021.3063106 | es |
dc.journaltitle | IEEE Journal of Biomedical and health informatics | es |
dc.publication.volumen | 26 | es |
dc.publication.issue | 4 | es |
dc.publication.initialPage | 1441 | es |
dc.publication.endPage | 1452 | es |