IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i5p2497-d509716.html
   My bibliography  Save this article

A Two-Phase Stochastic Dynamic Model for COVID-19 Mid-Term Policy Recommendations in Greece: A Pathway towards Mass Vaccination

Author

Listed:
  • Nikolaos P. Rachaniotis

    (Department of Industrial Management and Technology, University of Piraeus, 18534 Piraeus, Greece)

  • Thomas K. Dasaklis

    (Department of Industrial Management and Technology, University of Piraeus, 18534 Piraeus, Greece)

  • Filippos Fotopoulos

    (Department of Informatics, University of Piraeus, 18534 Piraeus, Greece)

  • Platon Tinios

    (Department of Statistics and Insurance Science, University of Piraeus, 18534 Piraeus, Greece)

Abstract

From 7 November 2020, Greece adopted a second nationwide lockdown policy to mitigate the transmission of SARS-CoV-2 (the first took place from 23 March to 4 May 2020), just as the second wave of COVID-19 was advancing, as did other European countries. To secure the full benefits of mass vaccination, which started in early January 2021, it is of utmost importance to complement it with mid-term non-pharmaceutical interventions (NPIs). The objective was to minimize human losses and to limit social and economic costs. In this paper a two-phase stochastic dynamic network compartmental model (a pre-vaccination SEIR until 15 February 2021 and a post-vaccination SVEIR from 15 February 2021 to 30 June 2021) is developed. Three scenarios are assessed for the first phase: (a) A baseline scenario, which lifts the national lockdown and all NPIs in January 2021; (b) a “semi-lockdown” scenario with school opening, partial retail sector operation, universal mask wearing, and social distancing/teleworking in January 2021; and (c) a “rolling lockdown” scenario combining a partial lifting of measures in January 2021 followed by a third nationwide lockdown in February 2021. In the second phase three scenarios with different vaccination rates are assessed. Publicly available data along with some first results of the SHARE COVID-19 survey conducted in Greece are used as input. The results regarding the first phase indicate that the “semi-lockdown” scenario clearly outperforms the third lockdown scenario (5.7% less expected fatalities); the second phase is extremely sensitive on the availability of sufficient vaccine supplies and high vaccination rates.

Suggested Citation

  • Nikolaos P. Rachaniotis & Thomas K. Dasaklis & Filippos Fotopoulos & Platon Tinios, 2021. "A Two-Phase Stochastic Dynamic Model for COVID-19 Mid-Term Policy Recommendations in Greece: A Pathway towards Mass Vaccination," IJERPH, MDPI, vol. 18(5), pages 1-21, March.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:5:p:2497-:d:509716
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/5/2497/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/18/5/2497/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Boukanjime, Brahim & Caraballo, Tomás & El Fatini, Mohamed & El Khalifi, Mohamed, 2020. "Dynamics of a stochastic coronavirus (COVID-19) epidemic model with Markovian switching," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    2. Din, Anwarud & Khan, Amir & Baleanu, Dumitru, 2020. "Stationary distribution and extinction of stochastic coronavirus (COVID-19) epidemic model," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    3. Zindoga Mukandavire & Farai Nyabadza & Noble J Malunguza & Diego F Cuadros & Tinevimbo Shiri & Godfrey Musuka, 2020. "Quantifying early COVID-19 outbreak transmission in South Africa and exploring vaccine efficacy scenarios," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-11, July.
    4. Hojeong Park & Songhee H. Kim, 2020. "A Study on Herd Immunity of COVID-19 in South Korea: Using a Stochastic Economic-Epidemiological Model," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 665-670, August.
    5. Joël Mossong & Niel Hens & Mark Jit & Philippe Beutels & Kari Auranen & Rafael Mikolajczyk & Marco Massari & Stefania Salmaso & Gianpaolo Scalia Tomba & Jacco Wallinga & Janneke Heijne & Malgorzata Sa, 2008. "Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 5(3), pages 1-1, March.
    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. Gong, Jiangyue & Gujjula, Krishna Reddy & Ntaimo, Lewis, 2023. "An integrated chance constraints approach for optimal vaccination strategies under uncertainty for COVID-19," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    2. Xiang Ren & Clifford P. Weisel & Panos G. Georgopoulos, 2021. "Modeling Effects of Spatial Heterogeneities and Layered Exposure Interventions on the Spread of COVID-19 across New Jersey," IJERPH, MDPI, vol. 18(22), pages 1-25, November.

    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. Zhang, Ge & Li, Zhiming & Din, Anwarud & Chen, Tao, 2024. "Dynamic analysis and optimal control of a stochastic COVID-19 model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 215(C), pages 498-517.
    2. Zai-Yin He & Abderrahmane Abbes & Hadi Jahanshahi & Naif D. Alotaibi & Ye Wang, 2022. "Fractional-Order Discrete-Time SIR Epidemic Model with Vaccination: Chaos and Complexity," Mathematics, MDPI, vol. 10(2), pages 1-18, January.
    3. Ichino, Andrea & Favero, Carlo A. & Rustichini, Aldo, 2020. "Restarting the economy while saving lives under Covid-19," CEPR Discussion Papers 14664, C.E.P.R. Discussion Papers.
    4. M. Hashem Pesaran & Cynthia Fan Yang, 2022. "Matching theory and evidence on Covid‐19 using a stochastic network SIR model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1204-1229, September.
    5. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    6. Houštecká, Anna & Koh, Dongya & Santaeulàlia-Llopis, Raül, 2021. "Contagion at work: Occupations, industries and human contact," Journal of Public Economics, Elsevier, vol. 200(C).
    7. Kuchler, Theresa & Russel, Dominic & Stroebel, Johannes, 2022. "JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook," Journal of Urban Economics, Elsevier, vol. 127(C).
    8. John M Drake & Tobias S Brett & Shiyang Chen & Bogdan I Epureanu & Matthew J Ferrari & Éric Marty & Paige B Miller & Eamon B O’Dea & Suzanne M O’Regan & Andrew W Park & Pejman Rohani, 2019. "The statistics of epidemic transitions," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-14, May.
    9. S. M. Niaz Arifin & Christoph Zimmer & Caroline Trotter & Anaïs Colombini & Fati Sidikou & F. Marc LaForce & Ted Cohen & Reza Yaesoubi, 2019. "Cost-Effectiveness of Alternative Uses of Polyvalent Meningococcal Vaccines in Niger: An Agent-Based Transmission Modeling Study," Medical Decision Making, , vol. 39(5), pages 553-567, July.
    10. Mayer Alvo & Jingrui Mu, 2023. "COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
    11. Bisin, Alberto & Moro, Andrea, 2022. "Spatial‐SIR with network structure and behavior: Lockdown rules and the Lucas critique," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 370-388.
    12. Mirjam Kretzschmar & Rafael T Mikolajczyk, 2009. "Contact Profiles in Eight European Countries and Implications for Modelling the Spread of Airborne Infectious Diseases," PLOS ONE, Public Library of Science, vol. 4(6), pages 1-8, June.
    13. Elisabetta De Cao & Alessia Melegaro & Rogier Klok & Maarten Postma, 2014. "Optimising Assessments of the Epidemiological Impact in the Netherlands of Paediatric Immunisation with 13-Valent Pneumococcal Conjugate Vaccine Using Dynamic Transmission Modelling," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-9, April.
    14. Gillis, Melissa & Urban, Ryley & Saif, Ahmed & Kamal, Noreen & Murphy, Matthew, 2021. "A simulation–optimization framework for optimizing response strategies to epidemics," Operations Research Perspectives, Elsevier, vol. 8(C).
    15. Richard Pitman & David Fisman & Gregory S. Zaric & Maarten Postma & Mirjam Kretzschmar & John Edmunds & Marc Brisson, 2012. "Dynamic Transmission Modeling," Medical Decision Making, , vol. 32(5), pages 712-721, September.
    16. Wiriya Mahikul & Somkid Kripattanapong & Piya Hanvoravongchai & Aronrag Meeyai & Sopon Iamsirithaworn & Prasert Auewarakul & Wirichada Pan-ngum, 2020. "Contact Mixing Patterns and Population Movement among Migrant Workers in an Urban Setting in Thailand," IJERPH, MDPI, vol. 17(7), pages 1-11, March.
    17. Valentina Marziano & Giorgio Guzzetta & Alessia Mammone & Flavia Riccardo & Piero Poletti & Filippo Trentini & Mattia Manica & Andrea Siddu & Antonino Bella & Paola Stefanelli & Patrizio Pezzotti & Ma, 2021. "The effect of COVID-19 vaccination in Italy and perspectives for living with the virus," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    18. Fatima-Zahra Jaouimaa & Daniel Dempsey & Suzanne Van Osch & Stephen Kinsella & Kevin Burke & Jason Wyse & James Sweeney, 2021. "An age-structured SEIR model for COVID-19 incidence in Dublin, Ireland with framework for evaluating health intervention cost," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-25, December.
    19. Hammoumi, Aayah & Qesmi, Redouane, 2020. "Impact assessment of containment measure against COVID-19 spread in Morocco," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    20. Thomas Ash & Antonio M. Bento & Daniel Kaffine & Akhil Rao & Ana I. Bento, 2022. "Disease-economy trade-offs under alternative epidemic control strategies," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

    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:jijerp:v:18:y:2021:i:5:p:2497-:d:509716. 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.