Epidemic Modelling Frameworks for Studying Infectious Disease Dynamics

Advancements in treating infectious diseases have improved in the past two decades, but the COVID-19 pandemic showed how quickly diseases can spread in today’s interconnected world. Computational epidemiology, using artificial intelligence and model simulations, helps experts analyse and control the spread of illnesses. This objective can be achieved using two different modelling paradigms: (i) macro-simulations , and (ii) micro-simulations. The former aims to characterize a system’s behaviour from a macroscopic perspective, utilizing mathematical methods like deterministic or stochastic processes to depict disease evolution and population dynamics. By concentrating on average or aggregated metrics, macro-simulations are especially useful for analysing broad trends and long-term implications. Differently, the latter aim at describing a system’s behaviour from a microscopic level in terms of its components and their interactions. Agent-Based Models are a common approach in this paradigm, representing individual agents with specific characteristics and decision-making rules. These models allow researchers to simulate complex, heterogeneous populations and capture localized or emergent phenomena, such as super-spreading events or the effects of targeted interventions [1] .