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

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

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

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Research Themes

Securing Digital Futures

Priority Areas

Artificial intelligence and autonomous systems

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