Comparative analysis of dynamical and statistical models for COVID-19: A comprehensive review

This paper is a review on dynamical and statistical modelling of infectious disease corona virus (COVID-19). Owing to the high population density in India, the rate social contact from human to human is high. Therefore, controlling the pandemic in early stage is quite challenging in India. For this the mathematical models are formulated to study the behaviour of the disease and identifying the parameters to reduce disease outbreak. In dynamical it is observed that most of the mathematical modelling is done based on the classical models such as SIR model, fractal SIR model, SEIR model etc., which evolves as set of differential equations and is used to estimate the rate of transmission of the COVID-19 disease. Also, statistical analysis of infectious disease and modelling as a time series models are applied to estimate the short term and long-term transmission of COVID-19 disease. From the driven data, the number of basic reproductions is calculated and studied effectiveness of the disease reported. Some precautionary measures and their effect are discussed, and predicted the future trends of rate of virus transmission with some control measures and summarized. The aim of this work is to conduct a comparative study of dynamical and statistical analysis of COVID-19.