Glushchenko, O., Manolov, A., Kozlov, I., Tsurkis, V., Maslova, I., Popov, N., Samoilov, A., Lukashev, A., & Ilina, E. (2023). Artificial Populations in Agent-Based Modeling. Preprints. https://doi.org/10.20944/preprints202312.0346.v1
Chicago/Turabian Style
Glushchenko, O., Alexandr Lukashev and Elena Ilina. 2023 "Artificial Populations in Agent-Based Modeling" Preprints. https://doi.org/10.20944/preprints202312.0346.v1
Abstract
In the field of modeling social systems, the use of agent-based models (AM) has become a powerful tool for understanding processes occurring in society, in particular, for studying the spread of infections among the population. The agent model is based on a synthetic population consisting of agents, their interactions and sometimes the territory in which the interaction occurs. The choice of an appropriate method for creating such artificial populations and the selection of parameters becomes a key issue affecting the realism of the model and its ability to reflect real-world scenarios. This article delves into the interplay between agent-based modeling, synthetic population construction, and parameter selection, offering our insight into the complex process of modeling infectious disease dynamics. The review concludes with an exploration of extending the Covasim model [1] to experiments in computational epidemiology. Covasim, a dynamic transmission modeling tool, is known for its ability to adapt to different scenarios and offers predictive information critical for managing epidemiological situations. The Covasim model could include improvements to better simulate real-world epidemic conditions, including a revised transmission strategy to account for accumulation through daily contacts. In addition, we propose to add a module for intercity travel and a visualization package that will significantly expand the functionality of Covasim. These improvements make Covasim a more versatile and efficient tool for modeling and analyzing epidemic processes.
Keywords
agen based modeling; COVID-19; digital epidemiology
Subject
Biology and Life Sciences, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
