dc.creator | Alamo, Teodoro | es |
dc.creator | Gutiérrez Reina, Daniel | es |
dc.creator | Millán Gata, Pablo | es |
dc.creator | Preciado, Víctor M. | es |
dc.creator | Giordano, Giulia | es |
dc.date.accessioned | 2021-09-13T14:11:03Z | |
dc.date.available | 2021-09-13T14:11:03Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Álamo Cantarero, T., Gutiérrez Reina, D., Millán Gata, P., Preciado, V.M. y Giordano, G. (2021). Data-driven methods for present and future pandemics: Monitoring, modelling and managing. Annual Reviews in Control | |
dc.identifier.issn | 1367-5788 | es |
dc.identifier.uri | https://hdl.handle.net/11441/125654 | |
dc.description | Article in press | es |
dc.description.abstract | This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a
roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the
available methodologies and discuss the challenges in the development of data-driven strategies to combat the
spreading of infectious diseases. Our aim is to bring together several different disciplines required to provide
a holistic approach to epidemic analysis, such as data science, epidemiology, and systems-and-control theory.
A 3M-analysis is presented, whose three pillars are: Monitoring, Modelling and Managing. The focus is on the
potential of data-driven schemes to address three different challenges raised by a pandemic: (i) monitoring
the epidemic evolution and assessing the effectiveness of the adopted countermeasures; (ii) modelling and
forecasting the spread of the epidemic; (iii) making timely decisions to manage, mitigate and suppress the
contagion. For each step of this roadmap, we review consolidated theoretical approaches (including data-driven
methodologies that have been shown to be successful in other contexts) and discuss their application to past
or present epidemics, such as Covid-19, as well as their potential application to future epidemics. | es |
dc.description.sponsorship | Agencia Estatal de Investigación(AEI) (España) PID2019-106212RB-C41/AEI/10.13039/501100011033 | es |
dc.description.sponsorship | Fundación Nacional de Ciencias de EE. UU. CAREER-ECCS-1651433 | es |
dc.description.sponsorship | Fundación Nacional de Ciencias de EE. UU. NSF-III-200884556 | es |
dc.format | application/pdf | es |
dc.format.extent | 17 p. | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier Ltd | es |
dc.relation.ispartof | Annual Reviews in Control | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Pandemic control | es |
dc.subject | Epidemiological models | es |
dc.subject | Machine learning | es |
dc.subject | Forecasting | es |
dc.subject | Surveillance systems | es |
dc.subject | Epidemic control | es |
dc.subject | Optimal control | es |
dc.subject | Model predictive control | es |
dc.title | Data-driven methods for present and future pandemics: Monitoring, modelling and managing | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
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 de Sistemas y Automática | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería Electrónica | es |
dc.relation.projectID | PID2019-106212RB-C41/AEI/10.13039/501100011033 | es |
dc.relation.projectID | CAREER-ECCS-1651433 | es |
dc.relation.projectID | NSF-III-200884556 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S1367578821000419 | es |
dc.identifier.doi | 10.1016/j.arcontrol.2021.05.003 | es |
dc.journaltitle | Annual Reviews in Control | es |
dc.contributor.funder | Universidad de Trento (Italia) | es |