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Using awareness to Z-control a SEIR model with overexposure: Insights on Covid-19 pandemic

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  • Lacitignola, Deborah
  • Diele, Fasma

Abstract

In this paper, we use the Z-control approach to get further insight on the role of awareness in the management of epidemics that, just like Covid-19, display a high rate of overexposure because of the large number of asymptomatic people. We focus on a SEIR model including a overexposure mechanism and consider awareness as a time-dependent variable whose dynamics is not assigned a priori. Exploiting the potential of awareness to produce social distancing and self-isolation among susceptibles, we use it as an indirect control on the class of infective individuals and apply the Z-control approach to detect what trend must awareness display over time in order to eradicate the disease. To this aim, we generalize the Z-control procedure to appropriately treat an uncontrolled model with more than two governing equations. Analytical and numerical investigations on the resulting Z-controlled system show its capability in controlling some representative dynamics within both the backward and the forward scenarios. The awareness variable is qualitatively compared to Google Trends data on Covid-19 that are discussed in the perspective of the Z-control approach, inferring qualitative indications in view of the disease control. The cases of Italy and New Zealand in the first phase of the pandemic are analyzed in detail. The theoretical framework of the Z-control approach can hence offer the chance to reflect on the use of Google Trends as a possible indicator of good management of the epidemic.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:chsofr:v:150:y:2021:i:c:s0960077921004173
    DOI: 10.1016/j.chaos.2021.111063
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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. Senapati, Abhishek & Panday, Pijush & Samanta, Sudip & Chattopadhyay, Joydev, 2020. "Disease control through removal of population using Z-control approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    3. 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).
    4. Alzahrani, Abdullah K. & Alshomrani, Ali Saleh & Pal, Nikhil & Samanta, Sudip, 2018. "Study of an eco-epidemiological model with Z-type control," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 197-208.
    5. Xiaolong Chen & Quanhui Liu & Ruijie Wang & Qing Li & Wei Wang, 2020. "Self-Awareness-Based Resource Allocation Strategy for Containment of Epidemic Spreading," Complexity, Hindawi, vol. 2020, pages 1-12, May.
    6. Kabir, K.M. Ariful & Kuga, Kazuki & Tanimoto, Jun, 2019. "Analysis of SIR epidemic model with information spreading of awareness," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 118-125.
    7. Mandal, Dibyendu Sekhar & Chekroun, Abdennasser & Samanta, Sudip & Chattopadhyay, Joydev, 2021. "A mathematical study of a crop-pest–natural enemy model with Z-type control," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 187(C), pages 468-488.
    8. 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).
    9. 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.
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    Cited by:

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    4. 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).

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