Use of Mathematical Models in Epidemiology to Predict Infectious

Abstract

Mathematical models play a key role in epidemiology, providing a powerful tool for predicting and controlling the spread of infectious diseases. This paper examines the use of mathematical models to analyze the dynamics of infectious diseases, assess the impact of health interventions, and predict future outbreaks. Initially, the structure of basic models such as SIR (Susceptible, Infected, Recovered) and their modifications to take into account factors such as population heterogeneity, social networks, and seasonal changes will be discussed. Next, model parameterization and calibration techniques will be explored to ensure accurate predictions in the context of data collected in real-time. The results show that mathematical models can be a valuable tool for public health policies, helping to identify optimal strategies for the prevention and control of infectious diseases. In conclusion, this analysis highlights the importance of the continued development of epidemiological models for improving the response to future epidemics and pandemics.