Ankush A. Patil
Research Scholar, Department of Mathematics, LVH ASC College, Nashik, MS (India)
S. D. Manjarekar
Professor, Department of Mathematics, LVH ASC College, Nashik, Maharashtra (India)
Abstract
Compartmental SIR model and Stochastic processes in machine learning are two different approaches to model the spread of diseases or other phenomena that can be spread among individuals. Compartmental SIR model is a mathematical framework that divides the population into three compartments: Susceptible, Infected, Recovered. These models assume individuals transition from one compartment to another based on a set of well-defined rules and it can be used to estimate the spread of diseases and predict future trends. On other side Stochastic processes in machine learning involve randomness and uncertainty in modeling the spread of diseases or other phenomena. Stochastic processes can be used to model the spread of diseases in more complex and realistic ways, taking into account the variability of individuals and the environment and also be used for prediction, decision making and risk assessment. This research paper mainly focuses on analysing behaviour of Compartmental SIR model and Stochastic processes in machine learning of Epidemiology.
Keywords- Mathematical Modeling, Machine Learning, Epidemiology. AMS (2020) Classification: 00A71,68Q05,92C60