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Managing awareness can avoid hysteresis in disease spread: an application to coronavirus Covid-19

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  • Lacitignola, Deborah
  • Saccomandi, Giuseppe

Abstract

A SEIR-type model is investigated to evaluate the effects of awareness campaigns in the presence of factors that can induce overexposure to disease. We find that high levels of overexposure can drive system dynamics towards a backward phenomenology and that increasing people awareness through balanced and aware information can be crucial to avoid dangerous dynamical transitions as hysteresis or transient oscillations before disease eradication. Investigations in the time dependent regimes are provided to support the results. Google Trends data in the context of Covid19 are also used to stress how low levels of awareness, combined with high overexposure, can be related to recent episodes of epidemic resurgence in Europe. Our results suggest that the interplay between overexposure and awareness is a point that should not be underestimated both in the current and future management of the Covid19 emergency.

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  • Lacitignola, Deborah & Saccomandi, Giuseppe, 2021. "Managing awareness can avoid hysteresis in disease spread: an application to coronavirus Covid-19," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    • Handle: RePEc:eee:chsofr:v:144:y:2021:i:c:s0960077921000928
    DOI: 10.1016/j.chaos.2021.110739
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    References listed on IDEAS

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    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. Das, Dhiraj Kumar & Khajanchi, Subhas & Kar, T.K., 2020. "The impact of the media awareness and optimal strategy on the prevalence of tuberculosis," Applied Mathematics and Computation, Elsevier, vol. 366(C).
    3. Arora, Vishal S. & McKee, Martin & Stuckler, David, 2019. "Google Trends: Opportunities and limitations in health and health policy research," Health Policy, Elsevier, vol. 123(3), pages 338-341.
    4. Kar, T.K. & Nandi, Swapan Kumar & Jana, Soovoojeet & Mandal, Manotosh, 2019. "Stability and bifurcation analysis of an epidemic model with the effect of media," Chaos, Solitons & Fractals, Elsevier, vol. 120(C), pages 188-199.
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    Cited by:

    1. Yongdong Shi & Rongsheng Huang & Hanwen Cui, 2021. "Prediction and Analysis of Tourist Management Strategy Based on the SEIR Model during the COVID-19 Period," IJERPH, MDPI, vol. 18(19), pages 1-12, October.
    2. 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).
    3. Gaeta, Giuseppe, 2022. "Mass vaccination in a roaring pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    4. Kumar, Arjun & Dubey, Uma S. & Dubey, Balram, 2024. "The impact of social media advertisements and treatments on the dynamics of infectious diseases with optimal control strategies," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 219(C), pages 50-86.
    5. Zhu, Xuzhen & Liu, Yuxin & Wang, Shengfeng & Wang, Ruijie & Chen, Xiaolong & Wang, Wei, 2021. "Allocating resources for epidemic spreading on metapopulation networks," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    6. Lacitignola, Deborah & Diele, Fasma, 2021. "Using awareness to Z-control a SEIR model with overexposure: Insights on Covid-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    7. Deborah Lacitignola & Fasma Diele & Carmela Marangi & Angela Monti & Teresa Serini & Simonetta Vernocchi, 2023. "Effects of Vitamin D Supplementation and Degradation on the Innate Immune System Response: Insights on SARS-CoV-2," Mathematics, MDPI, vol. 11(17), pages 1-19, August.
    8. Buonomo, Bruno & Giacobbe, Andrea, 2023. "Oscillations in SIR behavioural epidemic models: The interplay between behaviour and overexposure to infection," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    9. Deborah Lacitignola, 2021. "Handling Hysteresis in a Referral Marketing Campaign with Self-Information. Hints from Epidemics," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
    10. Pires, Marcelo A. & Sampaio Filho, Cesar I.N. & Herrmann, Hans J. & Andrade, José S., 2023. "Tricritical behavior in epidemic dynamics with vaccination," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    11. Wang, Xueli & Zhang, Suxia, 2024. "Coupling media coverage and susceptibility for modeling epidemic dynamics: An application to COVID-19," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 217(C), pages 374-394.

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