IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i23p12627-d691696.html
   My bibliography  Save this article

The Effect of Local and Global Interventions on Epidemic Spreading

Author

Listed:
  • Jiarui Fan

    (School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an 710049, China)

  • Haifeng Du

    (School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an 710049, China)

  • Yang Wang

    (School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an 710049, China)

  • Xiaochen He

    (School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

Epidemic spreading causes severe challenges to the global public health system, and global and local interventions are considered an effective way to contain such spreading, including school closures (local), border control (global), etc. However, there is little study on comparing the efficiency of global and local interventions on epidemic spreading. Here, we develop a new model based on the Susceptible-Exposed-Infectious-Recovered (SEIR) model with an additional compartment called “quarantine status”. We simulate various kinds of outbreaks and interventions. Firstly, we predict, consistent with previous studies, interventions reduce epidemic spreading to 16% of its normal level. Moreover, we compare the effect of global and local interventions and find that local interventions are more effective than global ones. We then study the relationships between incubation period and interventions, finding that early implementation of rigorous intervention significantly reduced the scale of the epidemic. Strikingly, we suggest a Pareto optimal in the intervention when resources were limited. Finally, we show that combining global and local interventions is the most effective way to contain the pandemic spreading if initially infected individuals are concentrated in localized regions. Our work deepens our understandings of the role of interventions on the pandemic, and informs an actionable strategy to contain it.

Suggested Citation

  • Jiarui Fan & Haifeng Du & Yang Wang & Xiaochen He, 2021. "The Effect of Local and Global Interventions on Epidemic Spreading," IJERPH, MDPI, vol. 18(23), pages 1-13, November.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:23:p:12627-:d:691696
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/23/12627/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/18/23/12627/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    2. Weihsueh A. Chiu & Rebecca Fischer & Martial L. Ndeffo-Mbah, 2020. "State-level needs for social distancing and contact tracing to contain COVID-19 in the United States," Nature Human Behaviour, Nature, vol. 4(10), pages 1080-1090, October.
    3. Zhang, Xiaolei & Ma, Renjun & Wang, Lin, 2020. "Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    4. Per Block & Marion Hoffman & Isabel J. Raabe & Jennifer Beam Dowd & Charles Rahal & Ridhi Kashyap & Melinda C. Mills, 2020. "Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world," Nature Human Behaviour, Nature, vol. 4(6), pages 588-596, June.
    Full references (including those not matched with items on IDEAS)

    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. Singhal, Amit & Singh, Pushpendra & Lall, Brejesh & Joshi, Shiv Dutt, 2020. "Modeling and prediction of COVID-19 pandemic using Gaussian mixture model," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    2. Luca Bonacini & Giovanni Gallo & Fabrizio Patriarca, 2021. "Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 275-301, January.
    3. Swapnarekha, H. & Behera, Himansu Sekhar & Nayak, Janmenjoy & Naik, Bighnaraj, 2020. "Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    4. Koutsellis, Themistoklis & Nikas, Alexandros, 2020. "A predictive model and country risk assessment for COVID-19: An application of the Limited Failure Population concept," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    5. Pelinovsky, Efim & Kurkin, Andrey & Kurkina, Oxana & Kokoulina, Maria & Epifanova, Anastasia, 2020. "Logistic equation and COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    6. Michał Wieczorek & Jakub Siłka & Dawid Połap & Marcin Woźniak & Robertas Damaševičius, 2020. "Real-time neural network based predictor for cov19 virus spread," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-18, December.
    7. Ribeiro, Matheus Henrique Dal Molin & da Silva, Ramon Gomes & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2020. "Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    8. František Božek & Irena Tušer, 2021. "Measures for Ensuring Sustainability during the Current Spreading of Coronaviruses in the Czech Republic," Sustainability, MDPI, vol. 13(12), pages 1-22, June.
    9. Wang, Richard & Ye, Zhongnan & Lu, Miaojia & Hsu, Shu-Chien, 2022. "Understanding post-pandemic work-from-home behaviours and community level energy reduction via agent-based modelling," Applied Energy, Elsevier, vol. 322(C).
    10. Shahadat Uddin & Arif Khan & Haohui Lu & Fangyu Zhou & Shakir Karim, 2022. "Suburban Road Networks to Explore COVID-19 Vulnerability and Severity," IJERPH, MDPI, vol. 19(4), pages 1-9, February.
    11. Oude Groeniger, Joost & Noordzij, Kjell & van der Waal, Jeroen & de Koster, Willem, 2021. "Dutch COVID-19 lockdown measures increased trust in government and trust in science: A difference-in-differences analysis," Social Science & Medicine, Elsevier, vol. 275(C).
    12. Dennis Opoku Boadu & Justice Kwame Appati & Joseph Agyapong Mensah, 2024. "Exploring the Effectiveness of Graph-based Computational Models in COVID-19 Research," SN Operations Research Forum, Springer, vol. 5(3), pages 1-41, September.
    13. Zahra Dehghan Shabani & Rouhollah Shahnazi, 2020. "Spatial distribution dynamics and prediction of COVID‐19 in Asian countries: spatial Markov chain approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(6), pages 1005-1025, December.
    14. 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).
    15. 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).
    16. Valerio Basile & Francesco Cauteruccio & Giorgio Terracina, 2021. "How Dramatic Events Can Affect Emotionality in Social Posting: The Impact of COVID-19 on Reddit," Future Internet, MDPI, vol. 13(2), pages 1-32, January.
    17. Viktoriia Shubina & Sylvia Holcer & Michael Gould & Elena Simona Lohan, 2020. "Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era," Data, MDPI, vol. 5(4), pages 1-40, September.
    18. Song, Jialu & Xie, Hujin & Gao, Bingbing & Zhong, Yongmin & Gu, Chengfan & Choi, Kup-Sze, 2021. "Maximum likelihood-based extended Kalman filter for COVID-19 prediction," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    19. Khan, Syed Abdul Rehman & Razzaq, Asif & Yu, Zhang & Shah, Adeel & Sharif, Arshian & Janjua, Laeeq, 2022. "Disruption in food supply chain and undernourishment challenges: An empirical study in the context of Asian countries," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    20. Huang, Yubo & Wu, Yan & Zhang, Weidong, 2020. "Comprehensive identification and isolation policies have effectively suppressed the spread of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 139(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:gam:jijerp:v:18:y:2021:i:23:p:12627-:d:691696. 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.