Generation of Reusable Synthetic Population and Social Networks for Agent-Based Modeling

Jiang, Na; Kavak, Hamdi; Kennedy, William G.; Crooks, Andrew

doi:10.23919/annsim52504.2021.9552172

Cited by 3 publications

(1 citation statement)

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“…Groups can range from individual families to the entire population. The wide coverage of the size of such populations means there is a growing interest in using such populations in various fields [33,34,35,36,37,38,39,40]. These synthetic populations, often called "digital twins," include agents modeled with different demographic attributes such as age, gender, location and housing, among others.…”

Section: Creation Of a Synthetic Populationmentioning

confidence: 99%

Artificial Populations in Agent-Based Modeling

Glushchenko,

Manolov,

Kozlov

et al. 2023

Preprint

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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.

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