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

Iman Rahimi,Fang Chen,Panagiotis G Asteris,Amir H Gandomi

doi:10.3390/info12030109

Abstract

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.

Highlights

In December 2019, the Chinese government informed the rest of the world that a virus was rapidly spreading throughout China
We report on a short analysis focusing on Australia, which continued as a short-term simulation
The confirmed growth rates in Australia, Italy, and UK were calculated for every day from 24 April to 23 May 2020

Summary

Introduction

In December 2019, the Chinese government informed the rest of the world that a virus was rapidly spreading throughout China. A few months later, it had detrimentally spread to several other countries. The United States Centers for Disease Control and Prevention (CDC) identified a seafood market in Wuhan as the center of the outbreak of the novel coronavirus disease (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2. The World Health Organization (WHO) reported a case in Thailand on. 13 January 2020, the first case to be identified outside China. On 16 January 2020, Japan confirmed its first case of the novel coronavirus, followed by South Korea on 20 January. Most countries around the world have been affected

Methods

Results

Conclusion