dc.creator | Alamo, Teodoro | es |
dc.creator | Gutiérrez Reina, Daniel | es |
dc.creator | Mammarella, Martina | es |
dc.creator | Abella, Alberto | es |
dc.date.accessioned | 2020-05-18T18:15:15Z | |
dc.date.available | 2020-05-18T18:15:15Z | |
dc.date.issued | 2020-05 | |
dc.identifier.citation | Alamo, T., Gutiérrez Reina, D., Mammarella, M. y Abella, A. (2020). Covid-19: Open-Data Resources for Monitoring, Modeling, and Forecasting the Epidemic. Electronics, 9 (5), 827. | |
dc.identifier.issn | 2079-9292 | es |
dc.identifier.uri | https://hdl.handle.net/11441/96875 | |
dc.description.abstract | We provide an insight into the open-data resources pertinent to the study of the spread of
the Covid-19 pandemic and its control. We identify the variables required to analyze fundamental
aspects like seasonal behavior, regional mortality rates, and effectiveness of government measures.
Open-data resources, along with data-driven methodologies, provide many opportunities to improve
the response of the different administrations to the virus. We describe the present limitations
and difficulties encountered in most of the open-data resources. To facilitate the access to the
main open-data portals and resources, we identify the most relevant institutions, on a global scale,
providing Covid-19 information and/or auxiliary variables (demographics, mobility, etc.). We also
describe several open resources to access Covid-19 datasets at a country-wide level (i.e., China, Italy,
Spain, France, Germany, US, etc.). To facilitate the rapid response to the study of the seasonal behavior
of Covid-19, we enumerate the main open resources in terms of weather and climate variables. We
also assess the reusability of some representative open-data sources. | es |
dc.description.sponsorship | Plan Propio de la Universidad de Sevilla | es |
dc.format | application/pdf | es |
dc.format.extent | 28 p. | es |
dc.language.iso | eng | es |
dc.publisher | MDPI AG | es |
dc.relation.ispartof | Electronics, 9 (5), 827. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Covid-19 | es |
dc.subject | Coronavirus | es |
dc.subject | SARS-CoV-2 | es |
dc.subject | Open data | es |
dc.subject | Data-driven methods | es |
dc.subject | Machine learning | es |
dc.subject | Seasonal behavior | es |
dc.subject | Government measures | es |
dc.title | Covid-19: Open-Data Resources for Monitoring, Modeling, and Forecasting the Epidemic | 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.publisherversion | https://www.mdpi.com/2079-9292/9/5/827 | es |
dc.identifier.doi | 10.3390/electronics9050827 | es |
dc.contributor.group | Universidad de Sevilla. TEP950: Estimación, Predicción, Optimización y Control | es |
dc.journaltitle | Electronics | es |
dc.publication.volumen | 9 | es |
dc.publication.issue | 5 | es |
dc.publication.initialPage | 827 | es |