Mathematical epidemic models play a crucial role in monitoring and predicting the spread dynamics of disease outbreaks. By utilizing mathematical tools and techniques, we can develop models that provide a quantitative representation of the underlying mechanisms of disease transmission and progression. These models can capture significant phenomena and help us understand the complex interactions between the pathogen, the host, and the environment. Furthermore, mathematical models can be used to simulate different intervention strategies and evaluate their effectiveness in controlling the spread of the disease. The compartmental model is one such mathematical epidemic model that has been widely used in analyzing infectious disease outbreaks. This model divides the population into different compartments based on their disease status. The compartmental model has been widely utilized for predicting patterns of disease outbreaks, including recurrent outbreaks, and for assessing the effectiveness of interventions such as vaccination and quarantine. However, it has been observed that this model does not yield highly accurate prediction results. Since dynamical population prediction models have low accuracy on predicting the population in the time-series, so machine learning methods are applied for forecasting. Therefore, machine learning techniques can be integrated with mathematical models to strengthen pandemic analysis and prediction scenarios. Machine learning models can be used to analyze large datasets and identify patterns and trends in disease transmission. These models can also be used to predict future disease outbreaks and estimate the impact of different interventions. Overall, mathematical epidemic models have the potential to significantly improve our understanding and control of diseases. By accurately quantifying the dynamics of disease transmission, we can develop effective strategies to mitigate the spread of diseases and protect public health.

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