MATHEMATICAL MODEL FOR INFECTION AND REMOVAL IN POPULATION

Abstract

The present paper deals with epidemic model base on some assumptions for the Population will be affected by diseases and final number of susceptible. Estimated Parameters involved in the model. The proposed model tested some characteristic of a general deterministic epidemic. This includes infectious disease dynamics, where scientific understanding can help capture biological processes in so called mechanistic models and their likelihood functions. However, when the likelihood of such mechanistic models lacks a closed form expression, computational burdens are substantial. In this context, algorithmic advances have facilitated likelihood
maximization, promoting the study of novel data motivated mechanistic models over the last decade In particular; we highlight statistical aspects of these models like over dispersion, which has key in the interface between nonlinear infectious disease modeling and data analysis. We also point out potential directions for further model exploration