IDEAS home Printed from https://ideas.repec.org/a/gam/jecnmx/v9y2021i4p38-d655735.html
   My bibliography  Save this article

Modeling Hospital Resource Management during the COVID-19 Pandemic: An Experimental Validation

Author

Listed:
  • J. M. Calabuig

    (Instituto Universitario de Matemática Pura y Aplicada, IUMPA-UPV, Universitat Politècnica de València, Camí de Vera, s/n, 46022 Valencia, Spain)

  • E. Jiménez-Fernández

    (Departamento de Teoría e Historía Económica, Universidad de Granada, Campus Cartuja, s/n, 18071 Granada, Spain)

  • E. A. Sánchez-Pérez

    (Instituto Universitario de Matemática Pura y Aplicada, IUMPA-UPV, Universitat Politècnica de València, Camí de Vera, s/n, 46022 Valencia, Spain)

  • S. Manzanares

    (Dirección Médica, Hospital Virgen de las Nieves, Av. de las Fuerzas Armadas, 2, 18014 Granada, Spain)

Abstract

One of the main challenges posed by the healthcare crisis generated by COVID-19 is to avoid hospital collapse. The occupation of hospital beds by patients diagnosed by COVID-19 implies the diversion or suspension of their use for other specialities. Therefore, it is useful to have information that allows efficient management of future hospital occupancy. This article presents a robust and simple model to show certain characteristics of the evolution of the dynamic process of bed occupancy by patients with COVID-19 in a hospital by means of an adaptation of Kaplan-Meier survival curves. To check this model, the evolution of the COVID-19 hospitalization process of two hospitals between 11 March and 15 June 2020 is analyzed. The information provided by the Kaplan-Meier curves allows forecasts of hospital occupancy in subsequent periods. The results shows an average deviation of 2.45 patients between predictions and actual occupancy in the period analyzed.

Suggested Citation

  • J. M. Calabuig & E. Jiménez-Fernández & E. A. Sánchez-Pérez & S. Manzanares, 2021. "Modeling Hospital Resource Management during the COVID-19 Pandemic: An Experimental Validation," Econometrics, MDPI, vol. 9(4), pages 1-16, October.
    • Handle: RePEc:gam:jecnmx:v:9:y:2021:i:4:p:38-:d:655735
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2225-1146/9/4/38/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2225-1146/9/4/38/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "A SIR model assumption for the spread of COVID-19 in different communities," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Costase Ndayishimiye & Christoph Sowada & Patrycja Dyjach & Agnieszka Stasiak & John Middleton & Henrique Lopes & Katarzyna Dubas-Jakóbczyk, 2022. "Associations between the COVID-19 Pandemic and Hospital Infrastructure Adaptation and Planning—A Scoping Review," IJERPH, MDPI, vol. 19(13), pages 1-22, July.
    2. Chiara Barchielli & Milena Vainieri & Chiara Seghieri & Eleonora Salutini & Paolo Zoppi, 2023. "The Function of Bed Management in Pandemic Times—A Case Study of Reaction Time and Bed Reconversion," IJERPH, MDPI, vol. 20(12), pages 1-8, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohamed M. Mousa & Fahad Alsharari, 2021. "A Comparative Numerical Study and Stability Analysis for a Fractional-Order SIR Model of Childhood Diseases," Mathematics, MDPI, vol. 9(22), pages 1-12, November.
    2. Talal Daghriri & Michael Proctor & Sarah Matthews, 2022. "Evolution of Select Epidemiological Modeling and the Rise of Population Sentiment Analysis: A Literature Review and COVID-19 Sentiment Illustration," IJERPH, MDPI, vol. 19(6), pages 1-20, March.
    3. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "Dynamic tracking with model-based forecasting for the spread of the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    4. Lin William Cong & Ke Tang & Bing Wang & Jingyuan Wang, 2021. "An AI-assisted Economic Model of Endogenous Mobility and Infectious Diseases: The Case of COVID-19 in the United States," Papers 2109.10009, arXiv.org.
    5. Ghanbari, Behzad, 2021. "On detecting chaos in a prey-predator model with prey’s counter-attack on juvenile predators," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    6. Wan, Jinming & Ichinose, Genki & Small, Michael & Sayama, Hiroki & Moreno, Yamir & Cheng, Changqing, 2022. "Multilayer networks with higher-order interaction reveal the impact of collective behavior on epidemic dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    7. Junseok Kim, 2024. "Influence of Fractional Order on the Behavior of a Normalized Time-Fractional SIR Model," Mathematics, MDPI, vol. 12(19), pages 1-9, October.
    8. Miguel Casares & Hashmat Khan, 2020. "The Timing and Intensity of Social Distancing to Flatten the COVID-19 Curve: The Case of Spain," IJERPH, MDPI, vol. 17(19), pages 1-14, October.
    9. Aditya Goenka & Lin Liu & Manh-Hung Nguyen, 2021. "Modeling optimal quarantines with waning immunity," Discussion Papers 21-10, Department of Economics, University of Birmingham.
    10. Wu, Yue & Li, Linjiao & Yu, Qiannan & Gan, Jiaxin & Zhang, Yi, 2023. "Strategies for reducing polarization in social networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    11. Samuel O. M. Manda & Timotheus Darikwa & Tshifhiwa Nkwenika & Robert Bergquist, 2021. "A Spatial Analysis of COVID-19 in African Countries: Evaluating the Effects of Socio-Economic Vulnerabilities and Neighbouring," IJERPH, MDPI, vol. 18(20), pages 1-15, October.
    12. Guihua Wang, 2022. "Stay at home to stay safe: Effectiveness of stay‐at‐home orders in containing the COVID‐19 pandemic," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2289-2305, May.
    13. Qu, Hongbo & Song, Yu-Rong & Li, Ruqi & Li, Min, 2023. "GNR: A universal and efficient node ranking model for various tasks based on graph neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P2).
    14. Lee, Chaeyoung & Kwak, Soobin & Kim, Sangkwon & Hwang, Youngjin & Choi, Yongho & Kim, Junseok, 2021. "Robust optimal parameter estimation for the susceptible-unidentified infected-confirmed model," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    15. Rabih Ghostine & Mohamad Gharamti & Sally Hassrouny & Ibrahim Hoteit, 2021. "An Extended SEIR Model with Vaccination for Forecasting the COVID-19 Pandemic in Saudi Arabia Using an Ensemble Kalman Filter," Mathematics, MDPI, vol. 9(6), pages 1-16, March.
    16. Martínez-Guerra, Rafael & Flores-Flores, Juan Pablo, 2021. "An algorithm for the robust estimation of the COVID-19 pandemic’s population by considering undetected individuals," Applied Mathematics and Computation, Elsevier, vol. 405(C).
    17. Alberto Olivares & Ernesto Staffetti, 2021. "Optimal Control Applied to Vaccination and Testing Policies for COVID-19," Mathematics, MDPI, vol. 9(23), pages 1-22, December.
    18. Mat Daud, Auni Aslah, 2021. "Comment on “Poorly known aspects of flattening the curve of COVID-19″," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    19. Gabrick, Enrique C. & Sayari, Elaheh & Protachevicz, Paulo R. & Szezech, José D. & Iarosz, Kelly C. & de Souza, Silvio L.T. & Almeida, Alexandre C.L. & Viana, Ricardo L. & Caldas, Iberê L. & Batista, , 2023. "Unpredictability in seasonal infectious diseases spread," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    20. Jose M. Martin-Moreno & Antoni Alegre-Martinez & Victor Martin-Gorgojo & Jose Luis Alfonso-Sanchez & Ferran Torres & Vicente Pallares-Carratala, 2022. "Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(9), pages 1-16, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jecnmx:v:9:y:2021:i:4:p:38-:d:655735. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.