DESIGN AND DEVELOPMENT OF MATHEMATICAL MODELS FOR EPIDEMIC PREDICTION AND CONTROL

The onset and quick spread of infectious illnesses present serious problems for public health and need for efficient prevention and control measures. In order to comprehend the dynamics of outbreaks and inform decision-making, mathematical models have become important tools. An overview of the creation and creation of mathematical frameworks for epidemic forecasting and management is provided in this study. We start out by going over the core ideas behind epidemic modelling. The susceptible-infected-recovered (SIR) paradigm and its modifications are compartmental models that constitute the foundation for these models. We examine the benefits and drawbacks of various modelling approaches and emphasise the significance of integrating real-world data to raise prediction accuracy. We also look at how mathematical models are used to control epidemics. These models make it easier to assess the results of various interventions, including social isolation programmes, vaccination campaigns, and focused containment techniques. We emphasise the significance of taking both societal dynamics and epidemiological considerations into account when optimising control tactics. Finally, we talk about the potential developments and current trends in epidemic modelling. Instantaneous information streams, spatial modelling, and the inclusion of social networks and human behaviour into models are all examples of this. We stress the importance of interdisciplinary partnerships between mathematicians, researchers in epidemiology, data scientists, and politicians in order to create reliable and flexible models that can successfully address changing epidemics