Analysis the use of machine learning algorithm-based methods in predicting COVID-19 infection

Fei, Yiming

doi:10.1145/3500931.3500948

Cited by 2 publications

(1 citation statement)

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“…In Russia, Peru, and Iran, an enhanced LSTM model predicts COVID-19 epidemic patterns [22]. Moreover, SVR, LSTM, BiLSTM, and GRU are used to forecast COVID-19 time-series data in 10 nations, and the result shows that BiLSTM data accessible through June 27, 2020, show higher performance [23].…”

Section: Introductionmentioning

confidence: 99%

Multi-step CNN forecasting for COVID-19 multivariate time-series

Haviluddin

Alfred

2023

Int. J. Adv. Intell. Informatics

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The new coronavirus (COVID-19) has spread to over 200 countries, with over 36 million confirmed cases as of October 10, 2020. As a result, numerous machine learning models capable of forecasting the epidemic worldwide have been produced. This paper reviews and summarizes the most relevant machine learning forecasting models for COVID-19. The dataset is derived from the world health organization (WHO) COVID-19 dashboard, and it contains official daily counts of COVID-19 cases, fatalities, and vaccination use reported by countries, territories, and regions. We propose various convolutional neural network (CNN) based models such as CNN, single exponential smoothing CNN (S-CNN), moving average CNN (MA-CNN), smoothed moving average CNN (SMA-CNN), and moving average smoothed CNN (MAS-CNN). Here, MAPE and MSE are used to assess the suggested models. MAPE is frequently used to compare accuracy across time series with different scales. MSE, the model must strive for a total forecast equal to the entire demand. That is, optimizing MSE seeks to create a forecast that is right on average and so unbiased. The final result shows that SMA-CNN outperformed its baselines in both MAPE and MSE. The main contribution of this novel forecasting approach is a more accurate result as a base of the strategy of preventing COVID-19 spreads.

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Section: Introductionmentioning

confidence: 99%