IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v166y2023ics0960077922011808.html
   My bibliography  Save this article

Unpredictability in seasonal infectious diseases spread

Author

Listed:
  • 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, Antonio M.

Abstract

In this work, we study the unpredictability of seasonal infectious diseases considering a SEIRS model with seasonal forcing. To investigate the dynamical behaviour, we compute bifurcation diagrams type hysteresis and their respective Lyapunov exponents. Our results from bifurcations and the largest Lyapunov exponent show bistable dynamics for all the parameters of the model. Choosing the inverse of latent period as control parameter, over 70% of the interval comprises the coexistence of periodic and chaotic attractors, bistable dynamics. Despite the competition between these attractors, the chaotic ones are preferred. The bistability occurs in two wide regions. One of these regions is limited by periodic attractors, while periodic and chaotic attractors bound the other. As the boundary of the second bistable region is composed of periodic and chaotic attractors, it is possible to interpret these critical points as tipping points. In other words, depending on the latent period, a periodic attractor (predictability) can evolve to a chaotic attractor (unpredictability). Therefore, we show that unpredictability is associated with bistable dynamics preferably chaotic, and, furthermore, there is a tipping point associated with unpredictable dynamics.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:chsofr:v:166:y:2023:i:c:s0960077922011808
    DOI: 10.1016/j.chaos.2022.113001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077922011808
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2022.113001?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. 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).
    2. Gabrick, Enrique C. & Protachevicz, Paulo R. & Batista, Antonio M. & Iarosz, Kelly C. & de Souza, Silvio L.T. & Almeida, Alexandre C.L. & Szezech, José D. & Mugnaine, Michele & Caldas, Iberê L., 2022. "Effect of two vaccine doses in the SEIR epidemic model using a stochastic cellular automaton," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    3. Sharma, Natasha & Verma, Atul Kumar & Gupta, Arvind Kumar, 2021. "Spatial network based model forecasting transmission and control of COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    4. Mugnaine, Michele & Gabrick, Enrique C. & Protachevicz, Paulo R. & Iarosz, Kelly C. & de Souza, Silvio L.T. & Almeida, Alexandre C.L. & Batista, Antonio M. & Caldas, Iberê L. & Szezech Jr, José D. & V, 2022. "Control attenuation and temporary immunity in a cellular automata SEIR epidemic model," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    5. Samuel V. Scarpino & Giovanni Petri, 2019. "On the predictability of infectious disease outbreaks," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    6. Francomano, Elisa & Hilker, Frank M. & Paliaga, Marta & Venturino, Ezio, 2018. "Separatrix reconstruction to identify tipping points in an eco-epidemiological model," Applied Mathematics and Computation, Elsevier, vol. 318(C), pages 80-91.
    7. de Souza, Silvio L.T. & Batista, Antonio M. & Caldas, Iberê L. & Iarosz, Kelly C. & Szezech Jr, José D., 2021. "Dynamics of epidemics: Impact of easing restrictions and control of infection spread," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    8. Jones, Andrew & Strigul, Nikolay, 2021. "Is spread of COVID-19 a chaotic epidemic?," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    9. Ventura, Paulo Cesar & Aleta, Alberto & Rodrigues, Francisco A. & Moreno, Yamir, 2022. "Epidemic spreading in populations of mobile agents with adaptive behavioral response," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    10. Cheng, Guanghui & Gui, Rong, 2022. "Bistable chaotic family and its chaotic mechanism," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    11. Wei, Wei & Xu, Wei & Song, Yi & Liu, Jiankang, 2021. "Bifurcation and basin stability of an SIR epidemic model with limited medical resources and switching noise," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    12. Carol Y. Lin, 2008. "Modeling Infectious Diseases in Humans and Animals by KEELING, M. J. and ROHANI, P," Biometrics, The International Biometric Society, vol. 64(3), pages 993-993, September.
    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. Gabrick, Enrique C. & Protachevicz, Paulo R. & Batista, Antonio M. & Iarosz, Kelly C. & de Souza, Silvio L.T. & Almeida, Alexandre C.L. & Szezech, José D. & Mugnaine, Michele & Caldas, Iberê L., 2022. "Effect of two vaccine doses in the SEIR epidemic model using a stochastic cellular automaton," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    2. 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).
    3. Khan, Junaid Iqbal & Ullah, Farman & Lee, Sungchang, 2022. "Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    4. Zheng, Qianqian & Shen, Jianwei & Xu, Yong & Pandey, Vikas & Guan, Linan, 2022. "Pattern mechanism in stochastic SIR networks with ER connectivity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    5. Meng, Xueyu & Lin, Jianhong & Fan, Yufei & Gao, Fujuan & Fenoaltea, Enrico Maria & Cai, Zhiqiang & Si, Shubin, 2023. "Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    6. Tyagi, Swati & Martha, Subash C. & Abbas, Syed & Debbouche, Amar, 2021. "Mathematical modeling and analysis for controlling the spread of infectious diseases," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    7. Kimberly M. Thompson, 2016. "Evolution and Use of Dynamic Transmission Models for Measles and Rubella Risk and Policy Analysis," Risk Analysis, John Wiley & Sons, vol. 36(7), pages 1383-1403, July.
    8. Cheng, Guanghui & Li, Dan & Yao, Yuangen & Gui, Rong, 2023. "Multi-scroll chaotic attractors with multi-wing via oscillatory potential wells," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    9. 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.
    10. De Martino, Giuseppe & Spina, Serena, 2015. "Exploiting the time-dynamics of news diffusion on the Internet through a generalized Susceptible–Infected model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 634-644.
    11. 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.
    12. Yulan Li & Kun Ma, 2022. "A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting," IJERPH, MDPI, vol. 19(19), pages 1-17, September.
    13. Christel Kamp & Mathieu Moslonka-Lefebvre & Samuel Alizon, 2013. "Epidemic Spread on Weighted Networks," PLOS Computational Biology, Public Library of Science, vol. 9(12), pages 1-10, December.
    14. Mohamed M. Mousa & Fahad Alsharari, 2021. "A Comparative Numerical Study and Stability Analysis for a Fractional-Order SIR Model of Childhood Diseases," Mathematics, MDPI, vol. 9(22), pages 1-12, November.
    15. Guido M. Kuersteiner & Ingmar R. Prucha, 2020. "Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity," Econometrica, Econometric Society, vol. 88(5), pages 2109-2146, September.
    16. Moritz Kersting & Andreas Bossert & Leif Sörensen & Benjamin Wacker & Jan Chr. Schlüter, 2021. "Predicting effectiveness of countermeasures during the COVID-19 outbreak in South Africa using agent-based simulation," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-15, December.
    17. Ofosuhene O Apenteng & Noor Azina Ismail, 2014. "The Impact of the Wavelet Propagation Distribution on SEIRS Modeling with Delay," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-9, June.
    18. Talal Daghriri & Michael Proctor & Sarah Matthews, 2022. "Evolution of Select Epidemiological Modeling and the Rise of Population Sentiment Analysis: A Literature Review and COVID-19 Sentiment Illustration," IJERPH, MDPI, vol. 19(6), pages 1-20, March.
    19. Miguel Navascués & Costantino Budroni & Yelena Guryanova, 2021. "Disease control as an optimization problem," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-32, September.
    20. Frank Daumann & Florian Follert & Werner Gleißner & Endre Kamarás & Chantal Naumann, 2021. "Political Decision Making in the COVID-19 Pandemic: The Case of Germany from the Perspective of Risk Management," IJERPH, MDPI, vol. 19(1), pages 1-23, 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:eee:chsofr:v:166:y:2023:i:c:s0960077922011808. 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.