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Computational Stochastic Modelling to Handle the Crisis Occurred During Community Epidemic

Author

Listed:
  • Ruchi Verma

    (Jaypee University of Information Technology)

  • Vivek Kumar Sehgal

    (Jaypee University of Information Technology)

  • Nitin

    (Jaypee Institute of Information Technology)

Abstract

Crisis can strike from anywhere at anyone and at any place. The unpredictability and inevitability of a crisis make it imminent that immediate and critical attention is paid to it so that it is managed and contained at the right time. Any crisis is a red alert situation so there is a widely felt need of it being handled with topmost priority and efficiency. A crisis, it may be a natural disaster, an organizational crisis, a political crisis or a product recall, brings a sudden and deep collapse in national output and a sharp increase in the income poverty. The key to handling a crisis successfully is the time required to bring it in controllable proportion. It is very important to predict the time required to handle the crucial situation during a crisis. Stochastic calculation of time is very important as the intensity of the situation goes higher. In the present paper, the whole event occurrence is the sum of specific information. The carrier of information are human beings and machines, which carry information through established communication networks. The degree of authenticity also depends upon the means of communication through human or machine interface. The proposed model is a stochastic model which contains information to be communicated one to one or broadcast one to many. This gives us estimated time to reach from one stage to another with the percentage of authenticity. In this model, it can be judged if the situation of a crisis is controllable or not, so that important inputs can be delivered to control the worst situation in the process.

Suggested Citation

  • Ruchi Verma & Vivek Kumar Sehgal & Nitin, 2016. "Computational Stochastic Modelling to Handle the Crisis Occurred During Community Epidemic," Annals of Data Science, Springer, vol. 3(2), pages 119-133, June.
  • Handle: RePEc:spr:aodasc:v:3:y:2016:i:2:d:10.1007_s40745-016-0075-y
    DOI: 10.1007/s40745-016-0075-y
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    References listed on IDEAS

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    1. Spillan, John & Hough, Michelle, 2003. "Crisis Planning in Small Businesses:: Importance, Impetus and Indifference," European Management Journal, Elsevier, vol. 21(3), pages 398-407, June.
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    Cited by:

    1. Shayan Frouzanfar & Maryam Omidi Najafabadi & Seyed Mehdi Mirdamadi, 2024. "Statistical Modelling for Pandemic Crisis Management in Universities," Annals of Data Science, Springer, vol. 11(4), pages 1459-1481, August.
    2. Manuel De la Sen & Asier Ibeas & Aitor J. Garrido, 2019. "Stage-Dependent Structured Discrete-Time Models for Mosquito Population Evolution with Survivability: Solution Properties, Equilibrium Points, Oscillations, and Population Feedback Controls," Mathematics, MDPI, vol. 7(12), pages 1-29, December.

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