Covid-19 data modelling using hybrid conjugate gradient method

Abstract

Optimization problem is a type of mathematical problem with several uses in real life. As one of the commonly applied methods for solving optimization problem, the conjugate gradient (CG) method has undergone numerous modifications in order to improve its efficiency. The hybrid CC is one of the best CC methods which can also be applied for data fitting [1]. Nowadays, Corona Virus Disease (Covid-19) has been extensively studied by many researchers worldwide due to the rising number of infected cases and deaths resulting from contracting the virus. In 2020, the Malaysian government enforced the first 'Movement Control Order (MCO)' in order to significantly lower the infection rate. However, the number of cases rose again at the end of year 2020, leading to the necessity to implement the second MCO in January 2021. In this study, a new hybrid conjugate gradient method is introduced to analyse the Covid-19 cases in Malaysia before MCO 2.0. This new hybrid method yields a better numerical result compared to classical and other hybrid CG methods.