Cross-sectional analysis and data-driven forecasting of confirmed COVID-19 cases

Abstract

The coronavirus disease 2019 (COVID-19) is rapidly becoming one of the leading causes for mortality worldwide. Various models have been built in previous works to study the spread characteristics and trends of the COVID-19 pandemic. Nevertheless, due to the limited information and data source, the understanding of the spread and impact of the COVID-19 pandemic is still restricted. Therefore, within this paper not only daily historical time-series data of COVID-19 have been taken into account during the modeling, but also regional attributes, e.g., geographic and local factors, which may have played an important role on the confirmed COVID-19 cases in certain regions. In this regard, this study then conducts a comprehensive cross-sectional analysis and data-driven forecasting on this pandemic. The critical features, which has the significant influence on the infection rate of COVID-19, is determined by employing XGB (eXtreme Gradient Boosting) algorithm and SHAP (SHapley Additive exPlanation) and the comparison is carried out by utilizing the RF (Random Forest) and LGB (Light Gradient Boosting) models. To forecast the number of confirmed COVID-19 cases more accurately, a Dual-Stage Attention-Based Recurrent Neural Network (DA-RNN) is applied in this paper. This model has better performance than SVR (Support Vector Regression) and the encoder-decoder network on the experimental dataset. And the model performance is evaluated in the light of three statistic metrics, i.e. MAE, RMSE and R 2 . Furthermore, this study is expected to serve as meaningful references for the control and prevention of the COVID-19 pandemic.

Exploring potential dual-stage attention based recurrent neural network machine learning application for dosage prediction in intelligent municipal management

“…Based on the RNN and attention mechanism, 35 Qin 36 proposed a dual-stage attention-based recurrent neural network (DA-RNN), which demonstrated great success in temporal forecasting, it seems the fitness of this model regarding the features of water quality is matched but it needs deeper research. Jing 37 combined historical time-series data of COVID-19 with geographic and local factors in the DA-RNN model to predict COVID-19 cases, the model has better performance than support-vector-regression and the encoder-decoder network on the experimental datasets. Huang 38 exactly forecasts wind power generation by historical power and wind speed information in DA-RNN.…”

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

“…Another study [83]) analyzed the spread of COVID-19 in the most affected Brazilian cities using hybrid and single ARIMA models, which integrated EEMD and ARIMA techniques. The results showed that the EEMD performed approximately 27% better than the single model [33,[47][48][49][53][54][55]71,74,106,[110][111][112][134][135][136][137][138][139][140][141][142][143].…”

Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

“…The attention approaches are inspired by human attention visual mechanisms, which use limited attention to quickly screen high value information from a large amount of information. This not only contributes to increase the prediction performance but is also efficient in gaining insight into information that is more critical to the model outputs instead of learning non-useful information [37,127,128].…”

“…The rest of the papers, e.g., [98,127,132], mostly utilize the data of one country or region, especially from the US. The analyzed studies focus on different research objectives.…”