Document Type
Journal Article
Publication Title
Intelligent Systems with Applications
Volume
14
Publisher
Elsevier
School
School of Medical and Health Sciences
RAS ID
43781
Funders
Edith Cowan University Murdoch University, Australia
Abstract
SARS-CoV2, which causes coronavirus disease (COVID-19) is continuing to spread globally, producing new variants and has become a pandemic. People have lost their lives not only due to the virus but also because of the lack of counter measures in place. Given the increasing caseload and uncertainty of spread, there is an urgent need to develop robust artificial intelligence techniques to predict the spread of COVID-19. In this paper, we propose a deep learning technique, called Deep Sequential Prediction Model (DSPM) and machine learning based Non-parametric Regression Model (NRM) to predict the spread of COVID-19. Our proposed models are trained and tested on publicly available novel coronavirus dataset. The proposed models are evaluated by using Mean Absolute Error and compared with the existing methods for the prediction of the spread of COVID-19. Our experimental results demonstrate the superior prediction performance of the proposed models. The proposed DSPM and NRM achieve MAEs of 388.43 (error rate 1.6%) and 142.23 (0.6%), respectively compared to 6508.22 (27%) achieved by baseline SVM, 891.13 (9.2%) by Time-Series Model (TSM), 615.25 (7.4%) by LSTM-based Data-Driven Estimation Method (DDEM) and 929.72 (8.1%) by Maximum-Hasting Estimation Method (MHEM).
DOI
10.1016/j.iswa.2022.200068
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Comments
Ayris, D., Imtiaz, M., Horbury, K., Williams, B., Blackney, M., See, C. S. H., & Shah, S. A. A. (2022). Novel deep learning approach to model and predict the spread of COVID-19. Intelligent Systems with Applications, 14, 200068. https://doi.org/10.1016/j.iswa.2022.200068