Infection Spread in Populations: An Agent-Based Model

Adarsh Prabhakaran () and Somdatta Sinha ()
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Adarsh Prabhakaran: The University of Edinburgh, Artificial Intelligence and its Applications Institute, The School of Informatics
Somdatta Sinha: Indian Institute of Science Education and Research (IISER)

A chapter in Trends in Biomathematics: Modeling Epidemiological, Neuronal, and Social Dynamics, 2023, pp 17-27 from Springer

Abstract: Abstract Spread of infection in a population is commonly modeled using differential equations representing the disease evolution in different epidemiological compartments. Herein, both population and time are continuous states, and the system behavior represents a mean-field description. However, in reality, the description is more granular, and the microscopic description of the interaction between heterogeneous individuals distributed in space represents a better description to study complex collective behavior through agent-based modeling (ABM) approach. It is much closer to real-world modeling where both space and time are discrete and disease evolution in space and time relies on the dynamical interactions between autonomous, heterogeneous agents/individuals, the environment in which the agents interact, and the behavioral rules governing their interaction with other agents. The ABM description may show new behaviors not seen in the mean-field description. In this study, we simulate the SI epidemiological model using the ABM approach. The simulations show that the time for infection decreases with increasing population size and the initial number of infected. Further, the structure of the environment/space in which the agents move has an important role to play in infection spread. From our simulations, we see that the infection time increases considerably when the movement of agents is restricted by a boundary wall with a slit dividing the environment. This time taken can be increased significantly by placing the slit at one of the edges compared to the wall’s center. We also see that when the population density is low, the infection spread is quite unpredictable, whereas when the environment is densely packed, the spread is localized in fronts. This demonstrates how spatial structures like boundaries with different porosities can indicate ways to contain infections in both heavily populated and sparsely populated areas. The time taken for the spread of infection can be vastly increased with planned spatial structures modeled using ABMs.

Date: 2023
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DOI: 10.1007/978-3-031-33050-6_2

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