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Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases

1
Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
2
Data Science Institute, University of Technology Sydney, Ultimo 2007, Australia
3
Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 15122 Athens, Greece
*
Author to whom correspondence should be addressed.
Academic Editor: Gennady Agre
Information 2021, 12(3), 109; https://doi.org/10.3390/info12030109
Received: 14 January 2021 / Revised: 13 February 2021 / Accepted: 15 February 2021 / Published: 3 March 2021
The novel coronavirus disease, also known as COVID-19, is a disease outbreak that was first identified in Wuhan, a Central Chinese city. In this report, a short analysis focusing on Australia, Italy, and UK is conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia compared with that in Italy and UK, and the trend of the disease in different Australian regions. Mathematical approaches based on susceptible, infected, and recovered (SIR) cases and susceptible, exposed, infected, quarantined, and recovered (SEIQR) cases models are proposed to predict epidemiology in the above-mentioned countries. Since the performance of the classic forms of SIR and SEIQR depends on parameter settings, some optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), limited memory bound constrained BFGS (L-BFGS-B), and Nelder–Mead, are proposed to optimize the parameters and the predictive capabilities of the SIR and SEIQR models. The results of the optimized SIR and SEIQR models were compared with those of two well-known machine learning algorithms, i.e., the Prophet algorithm and logistic function. The results demonstrate the different behaviors of these algorithms in different countries as well as the better performance of the improved SIR and SEIQR models. Moreover, the Prophet algorithm was found to provide better prediction performance than the logistic function, as well as better prediction performance for Italy and UK cases than for Australian cases. Therefore, it seems that the Prophet algorithm is suitable for data with an increasing trend in the context of a pandemic. Optimization of SIR and SEIQR model parameters yielded a significant improvement in the prediction accuracy of the models. Despite the availability of several algorithms for trend predictions in this pandemic, there is no single algorithm that would be optimal for all cases. View Full-Text
Keywords: COVID-19; analysis; machine learning; SIR and SEIQR models; optimization COVID-19; analysis; machine learning; SIR and SEIQR models; optimization
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MDPI and ACS Style

Rahimi, I.; Gandomi, A.H.; Asteris, P.G.; Chen, F. Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases. Information 2021, 12, 109. https://doi.org/10.3390/info12030109

AMA Style

Rahimi I, Gandomi AH, Asteris PG, Chen F. Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases. Information. 2021; 12(3):109. https://doi.org/10.3390/info12030109

Chicago/Turabian Style

Rahimi, Iman; Gandomi, Amir H.; Asteris, Panagiotis G.; Chen, Fang. 2021. "Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases" Information 12, no. 3: 109. https://doi.org/10.3390/info12030109

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