Abstract
This paper presents a flexible and extensible agent-based simulation model designed for decision-making in the management of epidemics, focusing on infectious disease spread and mitigation measures. The model incorporates the stochastic modelling of individuals movement in various environments, social interactions, and individual attributes, enabling the assessment of prevention and containment strategies. A prototype, based on the COVID-19 pandemic, was implemented in Python to validate the proposed model. The simulation scenarios considered factors such as mask usage for specific locations, isolation of infected symptomatic individuals, and global lockdowns to evaluate their impact on disease spread, in terms of infections and casualties. The results demonstrated the model's capacity to aid in the management of epidemiological alerts and its potential to be refined for real-life applications. The model's flexibility allows for future enhancements, including more complex particularizations of locations and further refinement of mitigation measures, without necessitating a reformulation of the basic structure.
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Acknowledgements
Authors acknowledge sponsorship from MadridDataSpace4Pandemics-CM project, funded as part of the Union response to the COVID-19 pandemic (REACT-UE Comunidad de Madrid). César Alberte Tapia acknowledges funding for his scholarship from UPM project RP180022025.
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Alberte, C., Carramiñana, D., Bernardos, A.M., Besada, J.A. (2023). Managing Pandemics Through Agent-Based Simulation: A Case Study Based on COVID-19. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-031-42529-5_18
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DOI: https://doi.org/10.1007/978-3-031-42529-5_18
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