[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v115y2023ics0305048322001608.html
   My bibliography  Save this article

A two-level policy for controlling an epidemic and its dynamics

Author

Listed:
  • Li, Xiaoming
Abstract
Governments and organizations must implement appropriate countermeasures at proper times to control an epidemic and its dynamics. This paper provides a framework for implementing both constant and temporary countermeasures. We show that imposing constant countermeasures (e.g., wearing face masks and keeping social distances till the end of an epidemic cycle) will reduce the total size, and the earlier the more total size reduction. We should implement constant countermeasures as early as possible. Next, temporary countermeasures (e.g., closing businesses in a short period) can always reduce the total size. But implementing temporary countermeasures earlier does not necessarily reduce the total size more. Rather, we should carry out temporary countermeasures around when infectious are high. Based on empirical data and analytical models, we then present a 2-level control policy for restraining infectious peaks and for reducing the total size. The upper control level is a target we try to curb the current infectious below, whereas the lower control level is when we switch back to normal. A tighter control level requires longer closing periods with a more total size reduction, but the total size reduction per closing period becomes less. Implementing a heavier temporary countermeasure (e.g., lockdown vs. only school closing) does not always reduce the total size more because the infectious will bounce back higher when reopen. Dynamic lax-tight policies (lax control early and tight control late) are better than their corresponding tight-lax policies. The crucial reason is a tailing effect: higher infectious lingering around in the late stages.

Suggested Citation

  • Li, Xiaoming, 2023. "A two-level policy for controlling an epidemic and its dynamics," Omega, Elsevier, vol. 115(C).
    • Handle: RePEc:eee:jomega:v:115:y:2023:i:c:s0305048322001608
    DOI: 10.1016/j.omega.2022.102753
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305048322001608
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.omega.2022.102753?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Choi, Tsan-Ming & Shi, Xiutian, 2022. "Reducing supply risks by supply guarantee deposit payments in the fashion industry in the “new normal after COVID-19”," Omega, Elsevier, vol. 109(C).
    2. Ramani, Vinay & Ghosh, Debabrata & Sodhi, ManMohan S., 2022. "Understanding systemic disruption from the Covid-19-induced semiconductor shortage for the auto industry," Omega, Elsevier, vol. 113(C).
    3. Seth Flaxman & Swapnil Mishra & Axel Gandy & H. Juliette T. Unwin & Thomas A. Mellan & Helen Coupland & Charles Whittaker & Harrison Zhu & Tresnia Berah & Jeffrey W. Eaton & Mélodie Monod & Azra C. Gh, 2020. "Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe," Nature, Nature, vol. 584(7820), pages 257-261, August.
    4. Rozhkov, Maxim & Ivanov, Dmitry & Blackhurst, Jennifer & Nair, Anand, 2022. "Adapting supply chain operations in anticipation of and during the COVID-19 pandemic," Omega, Elsevier, vol. 110(C).
    5. Shengjie Lai & Nick W. Ruktanonchai & Liangcai Zhou & Olivia Prosper & Wei Luo & Jessica R. Floyd & Amy Wesolowski & Mauricio Santillana & Chi Zhang & Xiangjun Du & Hongjie Yu & Andrew J. Tatem, 2020. "Effect of non-pharmaceutical interventions to contain COVID-19 in China," Nature, Nature, vol. 585(7825), pages 410-413, September.
    6. Fang, Xin & Zhang, Cheng & Robb, David J. & Blackburn, Joseph D., 2013. "Decision support for lead time and demand variability reduction," Omega, Elsevier, vol. 41(2), pages 390-396.
    7. Tang, Lianhua & Li, Yantong & Bai, Danyu & Liu, Tao & Coelho, Leandro C., 2022. "Bi-objective optimization for a multi-period COVID-19 vaccination planning problem," Omega, Elsevier, vol. 110(C).
    8. Sawik, Tadeusz, 2022. "Stochastic optimization of supply chain resilience under ripple effect: A COVID-19 pandemic related study," Omega, Elsevier, vol. 109(C).
    9. Ben-Ammar, Oussama & Dolgui, Alexandre & Wu, Desheng Dash, 2018. "Planned lead times optimization for multi-level assembly systems under uncertainties," Omega, Elsevier, vol. 78(C), pages 39-56.
    10. Mohammadi, Mehrdad & Dehghan, Milad & Pirayesh, Amir & Dolgui, Alexandre, 2022. "Bi‐objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID‐19 pandemic," Omega, Elsevier, vol. 113(C).
    11. Gilani, Hani & Sahebi, Hadi, 2022. "A data-driven robust optimization model by cutting hyperplanes on vaccine access uncertainty in COVID-19 vaccine supply chain," Omega, Elsevier, vol. 110(C).
    12. Radboud J. Duintjer Tebbens & Kimberly M. Thompson, 2009. "Priority Shifting and the Dynamics of Managing Eradicable Infectious Diseases," Management Science, INFORMS, vol. 55(4), pages 650-663, April.
    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. Zou, Hao & Sheng, Dian & Jiang, Jiehui, 2024. "Impact of trans-regional travel-related testing on epidemic spreading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 643(C).
    2. Dijkstra, Sander & Baas, Stef & Braaksma, Aleida & Boucherie, Richard J., 2023. "Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy," Omega, Elsevier, vol. 116(C).
    3. Esma Akgun & Sibel A. Alumur & F. Safa Erenay, 2023. "Determining optimal COVID-19 testing center locations and capacities," Health Care Management Science, Springer, vol. 26(4), pages 748-769, December.

    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. Ivanov, Dmitry & Keskin, Burcu B., 2023. "Post-pandemic adaptation and development of supply chain viability theory," Omega, Elsevier, vol. 116(C).
    2. Shaker Ardakani, Elham & Gilani Larimi, Niloofar & Oveysi Nejad, Maryam & Madani Hosseini, Mahsa & Zargoush, Manaf, 2023. "A resilient, robust transformation of healthcare systems to cope with COVID-19 through alternative resources," Omega, Elsevier, vol. 114(C).
    3. Rey, David & Hammad, Ahmed W. & Saberi, Meead, 2023. "Vaccine allocation policy optimization and budget sharing mechanism using reinforcement learning," Omega, Elsevier, vol. 115(C).
    4. Rozhkov, Maxim & Ivanov, Dmitry & Blackhurst, Jennifer & Nair, Anand, 2022. "Adapting supply chain operations in anticipation of and during the COVID-19 pandemic," Omega, Elsevier, vol. 110(C).
    5. Alikhani, Reza & Ranjbar, Amirhossein & Jamali, Amir & Torabi, S. Ali & Zobel, Christopher W., 2023. "Towards increasing synergistic effects of resilience strategies in supply chain network design," Omega, Elsevier, vol. 116(C).
    6. Maleki, Abolfazl & Hemmati, Vahid & Reza Abazari, Seyed & Aghsami, Amir & Rabbani, Masoud, 2024. "Optimal distribution and waste management of Covid-19 vaccines from vaccination centers’ satisfaction perspective – A fuzzy time window-based VRP," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    7. Aldrighetti, Riccardo & Battini, Daria & Ivanov, Dmitry, 2023. "Efficient resilience portfolio design in the supply chain with consideration of preparedness and recovery investments," Omega, Elsevier, vol. 117(C).
    8. Karatas, Mumtaz & Eriskin, Levent, 2023. "Linear and piecewise linear formulations for a hierarchical facility location and sizing problem," Omega, Elsevier, vol. 118(C).
    9. Lin Chen & Ting Dong & Jin Peng & Dan Ralescu, 2023. "Uncertainty Analysis and Optimization Modeling with Application to Supply Chain Management: A Systematic Review," Mathematics, MDPI, vol. 11(11), pages 1-45, May.
    10. Wang, Peipei & Liu, Haiyan & Zheng, Xinqi & Ma, Ruifang, 2023. "A new method for spatio-temporal transmission prediction of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    11. Pelagatti, Matteo & Maranzano, Paolo, 2021. "Assessing the effectiveness of the Italian risk-zones policy during the second wave of COVID-19," Health Policy, Elsevier, vol. 125(9), pages 1188-1199.
    12. Ivanov, Dmitry & Dolgui, Alexandre & Sokolov, Boris, 2022. "Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    13. Gilani, Hani & Sahebi, Hadi, 2022. "A data-driven robust optimization model by cutting hyperplanes on vaccine access uncertainty in COVID-19 vaccine supply chain," Omega, Elsevier, vol. 110(C).
    14. Shoufeng Ji & Pengyun Zhao & Tingting Ji, 2023. "A Hybrid Optimization Method for Sustainable and Flexible Design of Supply–Production–Distribution Network in the Physical Internet," Sustainability, MDPI, vol. 15(7), pages 1-34, April.
    15. Liang, Zhenglin & Jiang, Chen & Sun, Muxia & Xue, Zongqi & Li, Yan-Fu, 2023. "Resilience analysis for confronting the spreading risk of contagious diseases," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    16. Famiglietti, Matthew & Leibovici, Fernando, 2022. "The impact of health and economic policies on the spread of COVID-19 and economic activity," European Economic Review, Elsevier, vol. 144(C).
    17. Vahdani, Behnam & Mohammadi, Mehrdad & Thevenin, Simon & Gendreau, Michel & Dolgui, Alexandre & Meyer, Patrick, 2023. "Fair-split distribution of multi-dose vaccines with prioritized age groups and dynamic demand: The case study of COVID-19," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1249-1272.
    18. Sen, Anindya & Baker, John David & Zhang, Qihuang & Agarwal, Rishav Raj & Lam, Jean-Paul, 2023. "Do more stringent policies reduce daily COVID-19 case counts? Evidence from Canadian provinces," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 225-242.
    19. Ivanov, Dmitry, 2023. "Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability," International Journal of Production Economics, Elsevier, vol. 263(C).
    20. Dijkstra, Sander & Baas, Stef & Braaksma, Aleida & Boucherie, Richard J., 2023. "Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy," Omega, Elsevier, vol. 116(C).

    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:eee:jomega:v:115:y:2023:i:c:s0305048322001608. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    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.