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The global repercussions of the COVID-19 pandemic on economies and public health worldwide have been profound. This study aims to examine the developmental trends of the COVID-19 pandemic, establish predictive models, and provide insights for effective control measures against potential future disease outbreaks. Considering the coexistence of both linear and nonlinear factors in COVID-19 data, conventional single-machine learning and traditional forecasting models encounter challenges in accurately predicting pandemic trends. To enhance the precision of COVID-19 pandemic predictions by integrating linear and nonlinear factors, this study proposes three combined forecasting models: CNN-LSTM-ARIMA, TCN-LSTM-ARIMA, and SSA-LSTM-ARIMA. These models leverage the strengths of deep learning in capturing nonlinear factors and the capabilities of the traditional ARIMA model in handling linear factors. Initially, LSTM and ARIMA models are used to model and predict the COVID-19 pandemic in Quebec, Canada. Subsequently, CNN models, TCN models, and the Sparrow Search Algorithm are employed to integrate predictions from the LSTM and ARIMA models. Comparative analyses of the three combined models, it was found that the CNN-LSTM-ARIMA model exhibits the highest predictive accuracy, with an MSE of 7048.26, RMSE of 83.95, MAE of 61.18, MAPE of 0.16, and $R^{2}$ of 0.95. To validate the applicability and stability of the CNN-LSTM-ARIMA model in predicting COVID-19 pandemics, Italian COVID-19 pandemic data was employed. The three combined forecasting models are established and evaluated using model evaluation metrics. The results affirm that the CNN-LSTM-ARIMA model remains the optimal choice, underscoring its high stability and suitability for COVID-19 pandemic forecasting endeavors.

COVID-19 has developed into a global public health emergency and has led to restrictions in numerous nations. Thousands of deaths have resulted from the infection of millions of individuals globally. Additionally, COVID-19 has had a significant impact on social and economic activity around the world. The elderly and those with existing medical issues, however, are particularly vulnerable to the effects of COVID-19. Pneumonia, acute respiratory distress syndrome, organ failure, death, etc. are all possible outcomes in severe cases…Traditional prediction approaches like the ARIMA model and multiple linear regression model to handle the linear prediction problem because the new crown virus is in the process of continual mutation. Deep learning models that can take into account nonlinear elements include BP neural network prediction and LSTM neural network prediction. To combine the benefits of traditional and deep learning predictive models and create superior predictive models, we can blend traditional and deep learning predictive models. When the MSE, RMSE, and MAE of these three combined models, PSO-LSTM-ARIMA, MLR-LSTM-ARIMA, and BPNN-LSTM-ARIMA, are compared. We discovered that the third model, which included MSE, RMSE, and MAE, had the best prediction accuracy. The LSTM model and the ARIMA model were selected for this investigation. To begin, it employed a single model to forecast pandemic data in Germany. The BP neural network, particle swarm method, and multiple linear regression were then utilized to merge it. To corroborate this finding, we re-predicted the epidemic data from Japan and retrieved the MSE, RMSE, and MAE values of the BPNN-LSTM-ARIMA model, which were 6141895.956, 2478.285 and 1249.832. The most accurate model is still this integrated model. The BP neural network coupled LSTM model and ARIMA model offers the highest accurate prediction effect, according to our research. Combinatorial models anticipate outbreak data through our study, which can aid governments and public health authorities in improving their responses and educating the public about pandemic trends and potential future directions. As a result, industries and enterprises may make better risk-management decisions to protect the health and safety of their operations and personnel. It also helps healthcare facilities better prepare and deploy medical resources to better meet the demands from the pandemic.