IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v142y2021ics0960077920307700.html
   My bibliography  Save this article

Is spread of COVID-19 a chaotic epidemic?

Author

Listed:
  • Jones, Andrew
  • Strigul, Nikolay

Abstract

The COVID-19 epidemic challenges humanity in 2020. It has already taken an enormous number of human lives and had a substantial negative economic impact. Traditional compartmental epidemiological models demonstrated limited ability to predict the scale and dynamics of COVID-19 epidemic in different countries. In order to gain a deeper understanding of its behavior, we turn to chaotic dynamics, which proved fruitful in analyzing previous diseases such as measles. We hypothesize that the unpredictability of the pandemic could be a fundamental property if the disease spread is a chaotic dynamical system. Our mathematical examination of COVID-19 epidemic data in different countries reveals similarity of this dynamic to the chaotic behavior of many dynamics systems, such as logistic maps. We conclude that the data does suggest that the COVID-19 epidemic demonstrates chaotic behavior, which should be taken into account by public policy makers. Furthermore, the scale and behavior of the epidemic may be essentially unpredictable due to the properties of chaotic systems, rather than due to the limited data available for model parameterization.

Suggested Citation

  • Jones, Andrew & Strigul, Nikolay, 2021. "Is spread of COVID-19 a chaotic epidemic?," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    • Handle: RePEc:eee:chsofr:v:142:y:2021:i:c:s0960077920307700
    DOI: 10.1016/j.chaos.2020.110376
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077920307700
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2020.110376?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. Postnikov, Eugene B., 2020. "Estimation of COVID-19 dynamics “on a back-of-envelope”: Does the simplest SIR model provide quantitative parameters and predictions?," Chaos, Solitons & Fractals, Elsevier, vol. 135(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. Khatun, Mst Sebi & Das, Samhita & Das, Pritha, 2023. "Dynamics and control of an SITR COVID-19 model with awareness and hospital bed dependency," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    2. 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).
    3. Pájaro, Manuel & Fajar, Noelia M. & Alonso, Antonio A. & Otero-Muras, Irene, 2022. "Stochastic SIR model predicts the evolution of COVID-19 epidemics from public health and wastewater data in small and medium-sized municipalities: A one year study," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    4. Bukhari, Ayaz Hussain & Raja, Muhammad Asif Zahoor & Rafiq, Naila & Shoaib, Muhammad & Kiani, Adiqa Kausar & Shu, Chi-Min, 2022. "Design of intelligent computing networks for nonlinear chaotic fractional Rossler system," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    5. Sahri Aflah Ramadiansyah & Reza Safitri & Bambang Dwi Prasetyo, 2022. "The effect of self-efficacy and social influence on behavioral intention to use Zoom Cloud Meeting in implementing virtual learning for students of Brawijaya University," Technium Social Sciences Journal, Technium Science, vol. 33(1), pages 264-275, July.

    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. 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).
    2. Daniele Proverbio & Françoise Kemp & Stefano Magni & Andreas Husch & Atte Aalto & Laurent Mombaerts & Alexander Skupin & Jorge Gonçalves & Jose Ameijeiras-Alonso & Christophe Ley, 2021. "Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-21, May.
    3. Yeşilkanat, Cafer Mert, 2020. "Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. S. A. Trigger & A. M. Ignatov, 2022. "Strain-stream model of epidemic spread in application to COVID-19," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(11), pages 1-8, November.
    5. Hwang, Tienyu, 2021. "Coronavirus lockdown and virus suppression: An international analysis," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    6. Freire-Flores, Danton & Llanovarced-Kawles, Nyna & Sanchez-Daza, Anamaria & Olivera-Nappa, Álvaro, 2021. "On the heterogeneous spread of COVID-19 in Chile," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    7. Srivastava, H.M. & Saad, Khaled M. & Khader, M.M., 2020. "An efficient spectral collocation method for the dynamic simulation of the fractional epidemiological model of the Ebola virus," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    8. Adak, Debadatta & Majumder, Abhijit & Bairagi, Nandadulal, 2021. "Mathematical perspective of Covid-19 pandemic: Disease extinction criteria in deterministic and stochastic models," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    9. 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).
    10. Bagal, Dilip Kumar & Rath, Arati & Barua, Abhishek & Patnaik, Dulu, 2020. "Estimating the parameters of susceptible-infected-recovered model of COVID-19 cases in India during lockdown periods," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    11. Iván Area & Henrique Lorenzo & Pedro J. Marcos & Juan J. Nieto, 2021. "One Year of the COVID-19 Pandemic in Galicia: A Global View of Age-Group Statistics during Three Waves," IJERPH, MDPI, vol. 18(10), pages 1-14, May.
    12. Haiyue Chen & Benedikt Haus & Paolo Mercorelli, 2021. "Extension of SEIR Compartmental Models for Constructive Lyapunov Control of COVID-19 and Analysis in Terms of Practical Stability," Mathematics, MDPI, vol. 9(17), pages 1-25, August.
    13. Nathan H. Schumaker & Sydney M. Watkins, 2021. "Adding Space to Disease Models: A Case Study with COVID-19 in Oregon, USA," Land, MDPI, vol. 10(4), pages 1-13, April.
    14. Milad Haghani & Michiel C. J. Bliemer, 2020. "Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS, MERS and 2019-nCoV literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2695-2726, December.
    15. Arash Najmaei & Zahra Sadeghinejad, 2023. "Green and sustainable business models: historical roots, growth trajectory, conceptual architecture and an agenda for future research—A bibliometric review of green and sustainable business models," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 957-999, February.
    16. Qiu, Hong & Wang, Qian & Wu, Qun & Zhou, Hongyong, 2022. "Does flattening the curve make a difference? An investigation of the COVID-19 pandemic based on an SIR model," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 159-165.
    17. Consolini, Giuseppe & Materassi, Massimo, 2020. "A stretched logistic equation for pandemic spreading," Chaos, Solitons & Fractals, Elsevier, vol. 140(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:chsofr:v:142:y:2021:i:c:s0960077920307700. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.