COMPARATIVE ANALYSIS OF PARAMETER CONSISTENCY BETWEEN AGENT-BASED AND SIR EPIDEMIC MODELS

Abstract

In the context of the rapid spread of new viral infections, particularly during the COVID-19 pandemic, there is an increasing need to develop models that are capable not only of accurately representing the dynamics of the disease, but also of providing a well-grounded interpretation of the parameters used in analytical models. This paper examines the classical compartmental SIR (Susceptible–Infectious–Recovered) model, which allows for the assessment of disease dynamics through the solution of a system of differential equations. It is noted that, despite its wide application, this model has a number of limitations, as it does not take into account individual differences in population behavior, spatial structure, or variability of contacts. To address these limitations, a multi-agent model is proposed, in which individual agents simulate real people moving in a two-dimensional space and interacting with each other. The transition of agents between states (susceptible, infected, recovered) depends on the duration of the disease and the occurrence of spatial contact with an infected agent. The proposed model allows for consideration of the physical meaning of parameters, such as the infection radius and disease duration. Based on the results of agent-based modeling, the parameters of the SIR model – the infection transmission rate and the recovery rate – were identified using the least squares method. Numerical experiments examined how these parameters change depending on the duration of the disease and the spatial interaction distance between agents. The obtained results demonstrated qualitative agreement between the agent-based model and the SIR model when parameters were properly chosen. Thus, multi-agent modeling can not only significantly improve the accuracy of epidemic forecasting but also serve as a tool for the well-grounded identification of parameters in classical mathematical models. The proposed approach can be used to support decision-making in healthcare during real epidemic threats, providing a more substantiated assessment of the potential development of an epidemic, planning of preventive and control measures, and evaluation of the effectiveness of different intervention scenarios, taking into account the spatial and temporal dynamics of infection spread.