Intelligent Health Care and Diseases Management System: Multi-Day-Ahead Predictions of COVID-19
Source of Publication
The rapidly growing number of COVID-19 infected and death cases has had a catastrophic worldwide impact. As a case study, the total number of death cases in Algeria is over two thousand people (increased with time), which drives us to search its possible trend for early warning and control. In this paper, the proposed model for making a time-series forecast for daily and total infected cases, death cases, and recovered cases for the countrywide Algeria COVID-19 dataset is a two-layer dropout gated recurrent unit (TDGRU). Four performance parameters were used to assess the model’s performance: mean absolute error (MAE), root mean squared error (RMSE), R (Formula presented.), and mean absolute percentage error (MAPE). The results generated with TDGRU are compared with actual numbers as well as predictions with conventional time-series techniques, such as autoregressive integrated moving average (ARIMA), machine learning model of linear regression (LR), and the time series-based deep learning method of long short-term memory (LSTM). The experiment results on different time horizons show that the TDGRU model outperforms the other forecasting methods that deliver correct predictions with lower prediction errors. Furthermore, since this TDGRU is based on a relatively simpler architecture than the LSTM, in comparison to LSTM-based models, it features a significantly reduced number of parameters, a shorter training period, a lower memory storage need, and a more straightforward hardware implementation.
Computer Sciences | Medicine and Health Sciences
COVID-19, LSTM, TDGRU, time series analysis, traditional regression models
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abugabah, Ahed and Shahid, Farah, "Intelligent Health Care and Diseases Management System: Multi-Day-Ahead Predictions of COVID-19" (2023). All Works. 5732.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series