Mathematical Modeling of Epidemic Dynamics: Integrating Public Health and Data Science

Abstract

Mathematical models in understanding and predicting the dynamics of infectious diseases are not only growing but are also of paramount importance. The union of epidemiological models with data science techniques makes these tools add vital information about how diseases spread, how to break their chain of transmission, and how to do planning with respect to healthcare interventions. The paper analyses the main mathematical strategies in infectious disease dynamics: the compartmental models, stochastic models, and agent-based simulations. Also, the use of the data science tools like machine learning and statistical inference, and real time analytics greatly enhances the precision and forecasting nature of epidemic models. Modeling studies confirm that these approaches are effective for forecasting epidemic dynamics; stochastic methods are excellent in dealing with initial uncertainties and compartmental models play into long-range projections. As a result of numerical simulations, the integration of machine learning into epidemic models leads to approximately 20% prediction precision improvement as compared to traditional epidemiological tools. >>By integrating these models with public health policies, decision-makers can make more effective outcomes, where simulations have a 35% percent reduction in the highest rates of infections during the mock outbreak situations. Such results point to the need of the combination of various modeling approaches in order for better pandemic preparedness and responses.