Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019

Huang, Chiou Jye; Shen, Yamin; Kuo, Ping‐Huan; Chen, Yung‐Hsiang

doi:10.1016/j.seps.2020.100976

Cited by 28 publications

(23 citation statements)

References 35 publications

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“…The recurrent neural network (RNN) model is a commonly used model for predicting time series. A number of researchers used long short term memory (LSTM) to build COVID-19 prediction models [53]- [55]. A modified LSTM model, trained on the 2003 SARS data, is utilized to predict the epidemic in China from Jan 23 to Apr 24, 2020 [54].…”

Section: Literature Reviewmentioning

confidence: 99%

Random-Forest-Bagging Broad Learning System With Applications for COVID-19 Pandemic

Zhan

Zheng

Zhang

et al. 2021

IEEE Internet Things J.

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The rapid geographic spread of COVID-19, to which various factors may have contributed, has caused a global health crisis. Recently, the analysis and forecast of the COVID-19 pandemic have attracted worldwide attention. In this work, a large COVID-19 dataset consisting of COVID-19 pandemic, COVID-19 testing capacity, economic level, demographic information, and geographic location data in 184 countries and 1,241 areas from Dec 18, 2019, to Sep 30, 2020, were developed from public reports released by national health authorities and bureau of statistics. We proposed a machine learning model for COVID-19 prediction based on the Broad Learning System (BLS). Here, we leveraged Random Forest to screen out the key features. Then, we combine the bagging strategy and Broad Learning System to develop a Random-forest-Bagging Broad Learning System (RF-Bagging-BLS) approach to forecast the trend of the COVID-19 pandemic. In addition, we compared the forecasting results with Linear Regression (LR) model, K-Nearest Neighbors (KNN), Decision Tree (DT), Adaptive Boosting (Ada), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Support Vector Regression (SVR), Extra Trees (ET) regressor, CatBoost (CAT), LightGBM (LGB), XGBoost (XGB), and Broad Learning System (BLS).The RF-Bagging BLS model showed better forecasting performance in terms of relative mean square error (RMSE), coefficient of determination (R 2 ), adjusted coefficient of determination (R 2 adj ), median absolute error (MAD), and mean absolute percentage error (MAPE) than other models. Hence, the proposed model demonstrates superior predictive power over other benchmark models.

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Section: Literature Reviewmentioning

confidence: 99%

Random-Forest-Bagging Broad Learning System With Applications for COVID-19 Pandemic

Zhan

Zheng

Zhang

et al. 2021

IEEE Internet Things J.

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show abstract

“…For the forecasting of COVID-19 cases, a combination of CNN and GRU was used by him. [60]. Shen et al [61] proposed to use CNN and compared it with GRU-based models for the forecasting of electricity loads.…”

Section: Introductionmentioning

confidence: 99%

Attention-Based Bi-Directional Long-Short Term Memory Network for Earthquake Prediction

et al. 2021

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An earthquake is a tremor felt on the surface of the earth created by the movement of the major pieces of its outer shell. Till now, many attempts have been made to forecast earthquakes, which saw some success, but these attempted models are specific to a region. In this paper, an earthquake occurrence and location prediction model is proposed. After reviewing the literature, long short-term memory (LSTM) is found to be a good option for building the model because of its memory-keeping ability. Using the Keras tuner, the best model was selected from candidate models, which are composed of combinations of various LSTM architectures and dense layers. This selected model used seismic indicators from the earthquake catalog of Bangladesh as features to predict earthquakes of the following month. Attention mechanism was added to the LSTM architecture to improve the model's earthquake occurrence prediction accuracy, which was 74.67%. Additionally, a regression model was built using LSTM and dense layers to predict the earthquake epicenter as a distance from a predefined location, which provided a root mean square error of 1.25.

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“…Many studies have shown that COVID-19 is highly infectious, widespread, and prone to mutation ). These characteristics have also made COVID-19 prevention and control in many countries more complicated, and the pressure is also rising (Huang et al 2020a;Rahimi and Abadi 2020). Although the vaccine has been developed and used in some groups, there are still no specific drugs to treat COVID-19 (Huang et al 2020b;Zhu et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal characteristics and control strategies in the early period of COVID-19 spread: a case study of the mainland China

Ning

Chu

Liu

et al. 2021

Environ Sci Pollut Res

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Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019

Cited by 28 publications

References 35 publications

Random-Forest-Bagging Broad Learning System With Applications for COVID-19 Pandemic

Random-Forest-Bagging Broad Learning System With Applications for COVID-19 Pandemic

Attention-Based Bi-Directional Long-Short Term Memory Network for Earthquake Prediction

Spatio-temporal characteristics and control strategies in the early period of COVID-19 spread: a case study of the mainland China

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