Murdoch University Research Repository

Welcome to the Murdoch University Research Repository

The Murdoch University Research Repository is an open access digital collection of research
created by Murdoch University staff, researchers and postgraduate students.

Learn more

Novel deep learning approach to model and predict the spread of COVID-19

Ayris, D., Imtiaz, M., Horbury, K., Williams, B., Blackney, M., Hui See, C.S. and Shah, S.A.A. (2022) Novel deep learning approach to model and predict the spread of COVID-19. Intelligent Systems with Applications, 14 . Art. 200068.

PDF - Published Version
Download (3MB) | Preview
Free to read:
*No subscription required


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).

Item Type: Journal Article
Murdoch Affiliation(s): IT, Media and Communications
Publisher: Elsevier Ltd.
Copyright: © 2022 The Authors.
Item Control Page Item Control Page


Downloads per month over past year