TABLE I. Description of Models Interms of Various Factors.

Factor	Compartment Model	Statistical Models	Data Driven Model	Machine Learning and Deep Learning Based Models	Mixed Models
Meaning	The model is named, becuase the entire population is divided into different compartents. The dynamic behavior on spreading of disease is modelled as movement of people from one compartment to another.	The model is named, because, the models are based on probabiliy theory.	Data driven models are named, because, they predict only using real time data.	The models work by learning from data.	The combination of statistical, differential equation, machine learning and deep learning models are called mixed models.
Objective	Capture the spreading of the disease using simple differential equation.	To derive the closed form mathematical expression for the disease spread.	To develop disease spread prediction model by learning from the data alone.	To provide generalized model which can cover wide scenario	To provide better performance by exploiting the advantages of mixed models.
Data Considered	Data collected from various organization like WHO can be used.	Data collected from various organization like WHO can be used.	The real time data collected from various sources, which are listed in Table I.	Data are collected from various sources as like mentioned in Table I.	As the model is mixed one, the data required for the model will be from mixed sources.
Mathematical Assumption	1. Initially, fixed number of population is assued to be in susceptible compartment. 2. Movement of people is not considered and population size is assumed to be fixed one.	The model assume that the disease dynamic follows particular proability distribution for the given condition and scenario.	The model works well with the assumption that the data are correlated with some degree. The drastic change in the data will affect the performance of the model and it will take some time to correct itself.	1. It is assumed that the data are normally distributed with 0 mean and variance 1. 2. Intraclass data are highly correlated and interclass data are less correlated.	1. It is assumed that the data are not exactly normally distributed and has high variance. 2. Interclass and intraclass data are moderately correlated.
Outcome	The number of people in each compartment at the given time can be obtained from the model.	The solution can be obtained by simple substitution of parameters in the closed form expression.	The prediction of event related to disaese spreading is the major outcome, since it uses the available real time data for prediction.	Outcome includes the disease spread prediction, purely based on available data	Outcome includes high precision results in comparision with traditional models.
Limitations	Model is purely based on available old statistical data which fails to capture sudden changes in the spreading. Not able to handle more dynamic scenario.	The assumed probability distribution may not be valid for all scenarios.	The performance depends purely on the correctness of the data. However, the data available from the sources have some level of ucertainity.	The model need large amount of training data which consume exhaustive training time.	It is highly complex, as the model consists a mixture of many models.