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Forecasting COVID-19 new cases through the Mixed Generalized Inverse Weibull Distribution and time series model

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  • Chang, Yiming
  • Tao, YinYing
  • Shan, Wei
  • Yu, Xiangyuan

Abstract

In this paper, the MGIW-ARIMA model is proposed to predict the newly diagnosed cases of COVID-19. The MGIW model is used to predict the trend components, and the ARIMA model is used to predict the random components. Finally, the prediction results of trend components and random components are added together to get the estimation results. The data of COVID-19 in the United States is used to verify and evaluate the model. In the short-term prediction, the combined model of MGIW-ARIMA is better than the single MGIW model. In the long-term prediction, the prediction accuracy of the MGIW model is slightly higher than that of the MGIW-ARIMA model. The MGIW-ARIMA model proposed in this paper makes up for the shortcoming of the lack of randomness in the estimated values of the MGIW model. The combination model can effectively capture the short-term changes of the epidemic while grasping the long-term development trend.

Suggested Citation

  • Chang, Yiming & Tao, YinYing & Shan, Wei & Yu, Xiangyuan, 2023. "Forecasting COVID-19 new cases through the Mixed Generalized Inverse Weibull Distribution and time series model," Chaos, Solitons & Fractals, Elsevier, vol. 175(P2).
    • Handle: RePEc:eee:chsofr:v:175:y:2023:i:p2:s0960077923009165
    DOI: 10.1016/j.chaos.2023.114015
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    References listed on IDEAS

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    3. Bhardwaj, Rashmi & Bangia, Aashima, 2020. "Data driven estimation of novel COVID-19 transmission risks through hybrid soft-computing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Sultan, K.S. & Ismail, M.A. & Al-Moisheer, A.S., 2007. "Mixture of two inverse Weibull distributions: Properties and estimation," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5377-5387, July.
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