International Journal of Nonlinear Analysis and Applications

A model for predicting the trend deaths of COVID-19

Articles in Press

Document Type : Research Paper

Authors

1 Department of Entrepreneurship, Qazvin Branch, Islamic Azad University, Qazvin, Iran

2 Faculty of Business Administration, Memorial University of Newfoundland, St. John’s, NL, Canada

3 Department of Mathematics, Semnan University, Semnan, Iran

4 Department of Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran

10.22075/ijnaa.2024.21537.2268

Abstract

The novel coronavirus pneumonia (COVID-19) originated in Wuhan and rapidly disseminated across China and subsequently the globe. This study aims to predict the trend of COVID-19-related deaths by optimizing the parameters of deep learning algorithms, particularly focusing on integrating big data. The performance of long-short-term memory (LSTM) learning models was rigorously compared with the auto-regressive integrated moving average (ARIMA) model to forecast future trends in COVID-19 fatalities. Through extensive data analysis and model optimization, the results indicate that the experimental results highlight the performance differences between the ARIMA and LSTM models in predicting COVID-19 outcomes. Specifically, the ARIMA model demonstrates superior performance with an accuracy of 87 percent, compared to the LSTM model's 79 percent accuracy. However, this does not mean ARIMA is unequivocally better than LSTM across all metrics. The findings suggest that the implementation of these predictive models can significantly improve the timeliness of reporting in existing surveillance systems, thereby enhancing public health responses and reducing societal costs associated with the pandemic. The study highlights the potential of using advanced predictive modelling to support healthcare planning and intervention strategies during global health crises.

Keywords

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International Journal of Nonlinear Analysis and Applications

Articles in Press, Corrected Proof
Available Online from 16 February 2025
  • Receive Date: 08 October 2020
  • Revise Date: 31 July 2024
  • Accept Date: 19 August 2024