IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-03130063.html

Using COVID-19 mortality to select among hospital plant capacity models: An exploratory empirical application to Hubei province

Author

Listed:
  • Kristiaan Kerstens

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Zhiyang Shen

Abstract

This contribution defines short- and long-run output- and input-oriented plant capacity measures and evaluates them relative to convex and nonconvex technologies. By applying these different plant capacity concepts, the authors seek to measure the use of existing capacities, as well as the evolution and build-up of extra hospital capacity in the Chinese province of Hubei during the outbreak of the COVID-19 epidemic in early 2020. Furthermore, medical literature has established that mortality rates increase with high capacity utilization rates, an insight that this study leverages to select the most plausible of eight plant capacity concepts. The preliminary results indicate that a relatively new, input-oriented plant capacity concept correlates best with mortality.

Suggested Citation

  • Kristiaan Kerstens & Zhiyang Shen, 2021. "Using COVID-19 mortality to select among hospital plant capacity models: An exploratory empirical application to Hubei province," Post-Print hal-03130063, HAL.
    • Handle: RePEc:hal:journl:hal-03130063
    DOI: 10.1016/j.techfore.2020.120535
    Note: View the original document on HAL open archive server: https://hal.science/hal-03130063v1
    as

    Download full text from publisher

    File URL: https://hal.science/hal-03130063v1/document
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.techfore.2020.120535?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    Citations

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


    Cited by:

    1. Kristiaan Kerstens & Jafar Sadeghi & Ignace Woestyne & Linjia Zhang, 2024. "Correction to: Malmquist productivity indices and plant capacity utilisation: new proposals and empirical application," Annals of Operations Research, Springer, vol. 332(1), pages 1187-1187, January.
    2. Song, Malin & Zhou, Wenzhuo & Upadhyay, Arvind & Shen, Zhiyang, 2023. "Evaluating hospital performance with plant capacity utilization and machine learning," Journal of Business Research, Elsevier, vol. 159(C).
    3. Huang, Chuanli & Wang, Min & Rafaqat, Warda & Shabbir, Salman & Lian, Liping & Zhang, Jun & Lo, Siuming & Song, Weiguo, 2022. "Data-driven test strategy for COVID-19 using machine learning: A study in Lahore, Pakistan," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    4. Kristiaan Kerstens & Jafar Sadeghi, 2021. "Plant Capacity Notions: Existence Results at Firm and Industry Levels," Working Papers 2021-EQM-04, IESEG School of Management.
    5. Kristiaan KERSTENS & Xiaoqing CHEN, 2022. "Evaluating Horizontal Mergers in Swedish District Courts Using Plant Capacity Concepts: With a Focus on Nonconvexity," Working Papers 2022-EQM-02, IESEG School of Management.
    6. Songul Cinaroglu, 2024. "Efficiency effects of public hospital closures in the context of public hospital reform: a multistep efficiency analysis," Health Care Management Science, Springer, vol. 27(1), pages 88-113, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:hal:journl:hal-03130063. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.