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Identifying diagnosis and mortality of COVID-19 by learning a sequence-to-sequence ARIMA-based model

Author

Listed:
  • You-Shyang Chen
  • Jerome Chih Lung Chou
  • Naiying Hsu
  • Ting Yi Kuo

Abstract

COVID-19 impacted the overall economy and social order in any country from 2019, and Taiwan firstly setup a control centre which turned out to an excellent policy for the prevention and stemmed the spread of the disease by strengthening the publicity of patients' health to prevent the pandemic. Thus, the study is motivated to identify COVID-19 and Taiwan as research subjects. This study utilises the pandemic data (from January 2020 to May 2020) of five countries and proposes a hybrid time series-based method to analyse the diagnosis rates and mortality rates. Consequently, the USA, Russia, Spain, and Taiwan's forecast results fall within the confidence interval; Brazil's forecast results exceed the confidence interval. Despite the limitations, the proposed model can still be used as a viable alternative for predicting future pandemics. The empirical results of this study benefit researchers by avoiding the prodigality of medical resources from proper forecasting.

Suggested Citation

  • You-Shyang Chen & Jerome Chih Lung Chou & Naiying Hsu & Ting Yi Kuo, 2024. "Identifying diagnosis and mortality of COVID-19 by learning a sequence-to-sequence ARIMA-based model," International Journal of Applied Systemic Studies, Inderscience Enterprises Ltd, vol. 11(2), pages 138-158.
    • Handle: RePEc:ids:ijassi:v:11:y:2024:i:2:p:138-158
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