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

Abstract

The novel Coronavirus disease, known as COVID-19, is an outbreak that started in Wuhan, one of the Central Chinese cities. In this report, a short analysis focusing on Australia, Italy, and the United Kingdom has been conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia as compared with Italy and the United Kingdom, and the outbreak in different Australian cities. Mathematical approaches based on the susceptible, infected, and recovered case (SIR) and susceptible, exposed, infected, and recovered (SEIR) models were proposed to predict the epidemiology in the countries. Since the performance of the classic form of SIR and SEIR depends on parameter settings, some optimization algorithms, namely, the Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), L-BFGS-B, and Nelder-Mead are proposed to optimize the parameters of SIR and SEIR models and improve its predictive capabilities. The results of optimized SIR and SEIR models are compared with the Prophet algorithm and logistic function as two known ML algorithms. The results show that different algorithms display different behaviours in different countries. However, the improved version of the SIR and SEIR models have a better performance compared with other mentioned algorithms described in this study. Moreover, the Prophet algorithm works better for Italy and the United Kingdom cases than for Australian cases and Logistic function compared with Prophet algorithm has a better performance in these cases. It seems that Prophet algorithm is suitable for data with increasing trend in pandemic situations. Optimization of the SIR and SEIR models parameters has yielded a significant improvement in the prediction accuracy of the models. Although there are several algorithms for prediction of this Pandemic, there is no certain algorithm that would be the best one for all cases.

A review on COVID-19 forecasting models

“…A detailed scientometric analysis was performed as an influential tool for use in bibliometric analyses and reviews. For this aim, keywords and subject areas are discussed, while Present the effects of public health interventions on the outcome of the pandemic [74][75][76][77][78] The proposed models are mostly deterministic and work with large populations [79] Nature-inspired algorithms (genetic programming)…”

Applying Machine Learning Prediction Methods to COVID-19 Data

“…The curve fit is a data analysis method that attempts to construct a linear or nonlinear 𝑓(𝑥) model function. Some optimization algorithms such as L-BFGS-B are proposed to optimize the parameters of SIR and SEIR models and improve their prediction capabilities [32]. The curve fitting methods used in this study are explained as follows.…”

Genetic justification of severe COVID-19 using a rigorous algorithm

“…To find the variants that predict severe disease we developed a collaboration of four international computational centers (Iran, Italy, Malaysia, Greece). This multi-disciplinary approach was necessary to address the multidimensional aspects of COVID-19 infection by established collaborations [ [38] , [39] , [40] ]. Through this collaboration, a rigorous algorithm, based on laws and rules of logic, has been developed and trained for the disease risk prediction/assessment of patients with a confirmed infection caused by COVID-19.…”