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Dynamic model of infected population due to spreading of pandemic COVID-19 considering both intra and inter zone mobilization factors with rate of detection

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  • Ghosh, Mousam
  • Ghosh, Swarnankur
  • Ghosh, Suman
  • Panda, Goutam Kumar
  • Saha, Pradip Kumar

Abstract

Most of the widely populated countries across the globe have been observing vicious spread and detrimental effects of pandemic COVID-19 since its inception on December 19. Therefore to restrict the spreading of pandemic COVID-19, various researches are going on in both medical and administrative sectors. The focus has been given in this research keeping an administrative point of view in mind. In this paper a dynamic model of infected population due to spreading of pandemic COVID-19 considering both intra and inter zone mobilization factors with rate of detection has been proposed. Few factors related to intra zone mobilization; inter zone mobilization and rate of detection are the key points in the proposed model. Various remedial steps are taken into consideration in the form of operating procedures. Further such operating procedures are applied over the model in standalone or hybridized mode and responses are reported in this paper in a case-studies manner. Further zone-wise increase in infected population due to the spreading of pandemic COVID-19 has been studied and reported in this paper. Also the proposed model has been applied over the real world data considering three states of India and the predicted responses are compared with real data and reported with bar chart representation in this paper.

Suggested Citation

  • Ghosh, Mousam & Ghosh, Swarnankur & Ghosh, Suman & Panda, Goutam Kumar & Saha, Pradip Kumar, 2021. "Dynamic model of infected population due to spreading of pandemic COVID-19 considering both intra and inter zone mobilization factors with rate of detection," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    • Handle: RePEc:eee:chsofr:v:142:y:2021:i:c:s0960077920307712
    DOI: 10.1016/j.chaos.2020.110377
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    1. Choujun Zhan & Chi K Tse & Yuxia Fu & Zhikang Lai & Haijun Zhang, 2020. "Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-17, October.
    2. Nita H. Shah & Ankush H. Suthar & Ekta N. Jayswal, 2020. "Control Strategies to Curtail Transmission of COVID-19," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2020, pages 1-12, May.
    3. Tagliazucchi, E. & Balenzuela, P. & Travizano, M. & Mindlin, G.B. & Mininni, P.D., 2020. "Lessons from being challenged by COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    4. Altan, Aytaç & Karasu, Seçkin & Bekiros, Stelios, 2019. "Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 325-336.
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    Cited by:

    1. Marcin Mazur & Jerzy Bański & Wioletta Kamińska, 2024. "The Geographical Conditioning of Regional Differentiation Characterising the COVID-19 Pandemic in European Countries," IJERPH, MDPI, vol. 21(10), pages 1-20, October.

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