Data set for a predictive model for Spain on the economic impact of the COVID-19 crisis

DOI

The global COVID-19 spread has forced countries to implement non-pharmacological interventions (NPI) to preserve health systems. Spain is one of the most severely impacted countries, both clinically and economically. In an effort to support policy decision-making, Candel et al.(2021) [https://dx.doi.org/10.2139/ssrn.3745801] have developed a modified Susceptible-Exposed-Infectious-Removed (SEIR) epidemiological model to simulate the pandemic evolution. Its output was used to populate an economic model to quantify healthcare costs and GDP variation, through a regression model which correlates NPI and GDP change from 42 countries. The dataset contains information on the main variables used in order to specify and estimate this predictive model.

Identifier
DOI https://doi.org/10.34810/data111
Related Identifier IsCitedBy https://doi.org/10.2139/ssrn.3745801
Metadata Access https://dataverse.csuc.cat/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.34810/data111
Provenance
Creator Candel, Francisco Javier ORCID logo; Viayna, Elisabet ORCID logo; Callejo, Daniel ORCID logo; Ramos Lobo, Raúl ORCID logo; San Román Montero, Jesús; Barreiro, Pablo; Carretero, María del Mar ORCID logo; Kolipiński, Adam ORCID logo; Canora, Jesús ORCID logo; Zapatero, Antonio ORCID logo; Runken, M. Chris
Publisher CORA.Repositori de Dades de Recerca
Contributor Ramos Lobo, Raúl
Publication Year 2021
Rights CC BY 4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by/4.0
OpenAccess true
Contact Ramos Lobo, Raúl (Universitat de Barcelona)
Representation
Resource Type Other; Dataset
Format text/tab-separated-values; text/plain
Size 21034; 5568; 608
Version 1.0
Discipline Agriculture, Forestry, Horticulture, Aquaculture; Agriculture, Forestry, Horticulture, Aquaculture and Veterinary Medicine; Economics; Life Sciences; Medicine; Social Sciences; Social and Behavioural Sciences; Soil Sciences