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

  1. Candel, Francisco Javier 1
  2. Viayna, Elisabet 2
  3. Callejo, Daniel 3
  4. Ramos Lobo, Raúl 4
  5. San Román Montero, Jesús 5
  6. Barreiro, Pablo 6
  7. Carretero, María Del Mar 7
  8. Kolipiński, Adam 3
  9. Canora, Jesús 7
  10. Zapatero, Antonio 7
  11. Runken, M. Chris 2
  1. 1 (Hospital Clínico San Carlos)
  2. 2 (Grífols)
  3. 3 IQVIA
    info

    IQVIA

    Madrid, España

  4. 4 (Universitat de Barcelona)
  5. 5 (Universidad Rey Juan Carlos)
  6. 6 (Universidad Europea)
  7. 7 (Consejería de Sanidad, Comunidad de Madrid)
Erakutsi afiliazioak +

Argitaratzaile: CORA.Repositori de Dades de Recerca

Argitalpen urtea: 2021

Mota: Dataset

DOI: 10.34810/DATA111 lock_openSarbide irekia editor

Laburpena

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.