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A hybrid approach to forecast the COVID-19 epidemic trend

Author

Listed:
  • Saqib Ali Nawaz
  • Jingbing Li
  • Uzair Aslam Bhatti
  • Sibghat Ullah Bazai
  • Asmat Zafar
  • Mughair Aslam Bhatti
  • Anum Mehmood
  • Qurat ul Ain
  • Muhammad Usman Shoukat

Abstract

Studying the progress and trend of the novel coronavirus pneumonia (COVID-19) transmission mode will help effectively curb its spread. Some commonly used infectious disease prediction models are introduced. The hybrid model is proposed, which overcomes the disadvantages of the logistic model’s inability to predict the number of confirmed diagnoses and the drawbacks of too many tuning parameters of the SEIR (Susceptible, Exposed, Infectious, Recovered) model. The realization and superiority of the prediction of the proposed model are proven through experiments. At the same time, the influence of different initial values of the parameters that need to be debugged on the hybrid model is further studied, and the mean error is used to quantify the prediction effect. By forecasting epidemic size and peak time and simulating the effects of public health interventions, this paper aims to clarify the transmission dynamics of COVID-19 and recommend operation suggestions to slow down the epidemic. It is suggested that the quick detection of cases, sufficient implementation of quarantine and public self-protection behaviours are critical to slow down the epidemic.

Suggested Citation

  • Saqib Ali Nawaz & Jingbing Li & Uzair Aslam Bhatti & Sibghat Ullah Bazai & Asmat Zafar & Mughair Aslam Bhatti & Anum Mehmood & Qurat ul Ain & Muhammad Usman Shoukat, 2021. "A hybrid approach to forecast the COVID-19 epidemic trend," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-16, October.
    • Handle: RePEc:plo:pone00:0256971
    DOI: 10.1371/journal.pone.0256971
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    References listed on IDEAS

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    1. Mohamed Abd Elaziz & Khalid M Hosny & Ahmad Salah & Mohamed M Darwish & Songfeng Lu & Ahmed T Sahlol, 2020. "New machine learning method for image-based diagnosis of COVID-19," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-18, June.
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