La Estadística y la Investigación Operativa en la lucha contra la COVID-19

  1. Ramos, A.G. 1
  2. Abad, R.C. 2
  1. 1 Universidad de Valladolid
    info

    Universidad de Valladolid

    Valladolid, España

    ROR https://ror.org/01fvbaw18

  2. 2 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

Revista:
BEIO, Boletín de Estadística e Investigación Operativa

ISSN: 1889-3805

Año de publicación: 2020

Volumen: 36

Número: 2

Páginas: 201-224

Tipo: Artículo

Otras publicaciones en: BEIO, Boletín de Estadística e Investigación Operativa

Resumen

This article presents the personal view of the authors, two statisticians, about the role of Statistics and Operations Research in the fight against COVID-19. © 2020 SEIO

Referencias bibliográficas

  • [1] Abadías, L., Estrada-Rodr´ıguez, G., and Estrada, E. (2020). Fractionalorder susceptible-infected model: definition and applications to the study of COVID-19 main protease. Disponible en https://arxiv.org/abs/2004.11260
  • [2] Aleta, A., Hu, Q., Ye, J., Ji, P. and Moreno. Y. (2020). A data-driven assessment of early travel restrictions related to the spreading of the novel COVID-19 within mainland China. Doi: https://doi.org/10.1101/2020.03.05.20031740 Disponible en https://www.medrxiv.org/content/10.1101/2020.03.05.20031740v2
  • [3] Arenas, A., Cota, W., G´omez-Garde˜nes, J., G´omez, S., Granell, C., Matamalas, J.T., Soriano-Panos, D. and Steinegger, B. (2020). A mathematical model for the spatiotemporal epidemic spreading of COVID19. Preprint. doi: https://doi.org/10.1101/2020.03.21.20040022. Disponible en https://www.medrxiv.org/content/10.1101/2020.03.21.20040022v1
  • [4] Bates, J.M. and Granger, C.W.J (1969). The combination of forecasts. Operations Research Quarterly, 20, 451-468.
  • [5] Claeskens, G., Magnus, J.R., Vasnev, A.L. and Wang, W. (2016). The forecast combination puzzle: A simple theoretical explanation. International Journal of Forecasting, 32, 754-762.
  • [6] Clements, M.P., Hendry, D.F., Aiolfi, M., Capistr´an, C. and Timmermann, A. (2012). Forecast Combinations. Oxford University Press.
  • [7] Estrada, E. (2020). Analyzing the impact of SARS CoV-2 on the human proteome. Disponible en https://www.biorxiv.org/content/biorxiv/early/2020/05/21/2020.05.- 21.107912.full.pdf
  • [8] Ivorra, B. , Ferr´andez, M.R., Vela-P´erez, M., Ramos, A.M. (2020). Mathematical modeling of the spread of the coronavirus disease 2019 (COVID19) taking into account the undetected infections. The case of China. Communications in Nonlinear Science and Numerical Simulation, 88, 105303, DOI link: https://doi.org/10.1016/j.cnsns.2020.105303. Disponible en https://doi.org/10.13140/RG.2.2.21543.29604.
  • [9] Nda¨ırou, F., Area, I., Nieto, J.J., Torres, D.F.M. (2020). Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan. Chaos, Solitons & Fractals, 135, 109846 https://doi.org/10.1016/j.chaos.2020.109846
  • [10] Timmermann, A. (2006). Forecast combinations. Handbook of economic forecasting, 1, 135-196
  • [11] Vallejo, J.A., Rumbo-Feal, S., Conde-P´erez, K., Lopez-Oriona, A., Tarr´ıo, J., Reif, R., Ladra, S., Rodino-Janeiro, B.K., Nasser, M., Cid, A., Veiga, M.C., Acevedo, A., Lamora, C., Bou, G., Cao, R. and Poza, M. (2020). Highly predictive regression model of active cases of COVID-19 in a population by screening wastewater viral load. doi: https://doi.org/10.1101/2020.07.02.20144865 Disponible