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PSIR: a novel phase-wise diffusion model for lockdown analysis of COVID-19 pandemic in India

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  • Adwitiya Sinha

    (Jaypee Institute of Information Technology)

Abstract

The coronavirus pandemic has damaged economies and hampered human lives with fatalities and socio-economic losses. The emergency intervention measures imposed in several nations during the primary stage of the contagion had stalled the deadly virus from spreading explosively. One such preventive measures include the nation-wide lockdown process that became a significant step towards mitigation of COVID-19. In the proposed work, a diffusion-based system is proposed to analyze the impact of lockdown in India. The COVID spread data was obtained from the aggregated situation reports made available by the World Health Organization. The infection and recovery rate, being phase-dependent parameters, are computed from lockdown analysis. The reproduction number of coronavirus is also estimated for each phase, which was found to decrease consistently from initial to final phase during the lockdown. Further, the Phase-wise Susceptible-Infected-Recovered network diffusion system is proposed to model the phase-wise evolution of coronavirus spread in India is assessed. The proposed system simulated the lockdown that was imposed in India into five phases for an initial duration of 98 days. The infection and recovery rate during lockdown were used to be an important parameter in diffusion to measure the spreading potential of the deadly virus. Finally, the research highlights the benefits of re-imposing lockdown in India for controlling the devastating COVID-19 situations, based on pre and post lockdown situation.

Suggested Citation

  • Adwitiya Sinha, 2022. "PSIR: a novel phase-wise diffusion model for lockdown analysis of COVID-19 pandemic in India," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1356-1369, June.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:3:d:10.1007_s13198-021-01477-1
    DOI: 10.1007/s13198-021-01477-1
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    References listed on IDEAS

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    1. Ndaïrou, Faïçal & Area, Iván & Nieto, Juan J. & Torres, Delfim F.M., 2020. "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    2. S. P. Zemtsov & V. L. Baburin, 2020. "COVID-19: Spatial Dynamics and Diffusion Factors across Russian Regions," Regional Research of Russia, Springer, vol. 10(3), pages 273-290, July.
    3. Jain, Somya & Sinha, Adwitiya, 2020. "Identification of influential users on Twitter: A novel weighted correlated influence measure for Covid-19," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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