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Prediction of Infectious Disease to Reduce the Computation Stress on Medical and Health Care Facilitators

Author

Listed:
  • Shalini Shekhawat

    (Department of Mathematics, Swami Keshvanand Institute of Technology, Management and Gramothan, Jaipur 302017, Rajasthan, India)

  • Akash Saxena

    (School of Computing Science and Engineering, VIT Bhopal University, Bhopal- Indore Highway, Kothrikalan, Sehore 466116, Madhya Pradesh, India)

  • Ramadan A. Zeineldin

    (Deanship of Scientific Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Ali Wagdy Mohamed

    (Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt
    Department of Mathematics and Actuarial Science, School of Sciences Engineering, The American University in Cairo, Cairo 12613, Egypt)

Abstract

Prediction of the infectious disease is a potential research area from the decades. With the progress in medical science, early anticipation of the disease spread becomes more meaningful when the resources are limited. Also spread prediction with limited data pose a deadly challenge to the practitioners. Hence, the paper presents a case study of the Corona virus (COVID-19). COVID-19 has hit the major parts of the world and implications of this virus, is life threatening. Research community has contributed significantly to understand the spread of virus with time, along with meteorological conditions and other parameters. Several forecasting techniques have already been deployed for this. Considering the fact, the paper presents a proposal of two Rolling horizon based Cubic Grey Models (RCGMs). First, the mathematical details of Cubic Polynomial based simple grey model is presented than two models based on time series rolling are proposed. The models are developed with the time series data of different locations, considering diverse overlap period and rolling values. It is observed that the proposed models yield satisfactory results as compared with the conventional and advanced grey models. The comparison of the performance has been carried out with calculation of standard error indices. At the end, some recommendations are also framed for the authorities, that can be helpful for decision making in tough time.

Suggested Citation

  • Shalini Shekhawat & Akash Saxena & Ramadan A. Zeineldin & Ali Wagdy Mohamed, 2023. "Prediction of Infectious Disease to Reduce the Computation Stress on Medical and Health Care Facilitators," Mathematics, MDPI, vol. 11(2), pages 1-18, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:2:p:490-:d:1037846
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    References listed on IDEAS

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    1. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    2. Peng-Yu Chen & Hong-Ming Yu, 2014. "Foundation Settlement Prediction Based on a Novel NGM Model," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-8, March.
    3. Ping Jiang & Qingping Zhou & Haiyan Jiang & Yao Dong, 2014. "An Optimized Forecasting Approach Based on Grey Theory and Cuckoo Search Algorithm: A Case Study for Electricity Consumption in New South Wales," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-13, June.
    4. Zhang, Yagang & Xu, Yan & Wang, Zengping, 2009. "GM(1,1) grey prediction of Lorenz chaotic system," Chaos, Solitons & Fractals, Elsevier, vol. 42(2), pages 1003-1009.
    5. Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    6. Mahboubeh Faghih Mohammadi Jalali & Hanif Heidari, 2020. "Predicting changes in Bitcoin price using grey system theory," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-12, December.
    7. Saad Ahmed Javed & Sifeng Liu, 2018. "Predicting the research output/growth of selected countries: application of Even GM (1, 1) and NDGM models," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 395-413, April.
    8. Lifeng Wu & Sifeng Liu & Ding Chen & Ligen Yao & Wei Cui, 2014. "Using gray model with fractional order accumulation to predict gas emission," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(3), pages 2231-2236, April.
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    Cited by:

    1. Kun Zhang & Xing Huo & Kun Shao, 2023. "Temperature Time Series Prediction Model Based on Time Series Decomposition and Bi-LSTM Network," Mathematics, MDPI, vol. 11(9), pages 1-16, April.

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