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Modeling Spatial Contacts for Epidemic Prediction in a Large-Scale Artificial City

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Spatial contacts among human beings are considered as one of the influential factors during the transmission of contagious diseases, such as influenza and tuberculosis. Therefore, representing and understanding spatial contacts plays an important role in epidemic modeling research. However, most current research only considers regular spatial contacts such as contacts at home/school/office, or they assume static social networks for modeling social contacts and omit travel contacts in their epidemic models. This paper describes a way to model relatively complete spatial contacts in the context of a large-scale artificial city, which combines different data sources to construct an agent-based model of the city Beijing. In this model, agents have regular contacts when executing their daily activity patterns which is similar to other large-scale agent-based epidemic models. Besides, a microscopic public transportation component is included in the artificial city to model public travel contacts. Moreover, social contacts also emerge in this model due to the dynamic generation of social networks. To systematically examine the effect of the relatively complete spatial contacts have for epidemic prediction in the artificial city, a pandemic influenza disease progression model was implemented in this artificial city. The simulation results validated the model. In addition, the way to model spatial contacts in this paper shows potential not only for improving comprehension of disease spread dynamics, but also for use in other social systems, such as public transportation systems and city level evacuation planning.

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  • Mingxin Zhang & Alexander Verbraeck & Rongqing Meng & Bin Chen & Xiaogang Qiu, 2016. "Modeling Spatial Contacts for Epidemic Prediction in a Large-Scale Artificial City," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(4), pages 1-3.
    • Handle: RePEc:jas:jasssj:2015-69-2
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    1. Zhao, Pengjun & Lü, Bin & Roo, Gert de, 2011. "Impact of the jobs-housing balance on urban commuting in Beijing in the transformation era," Journal of Transport Geography, Elsevier, vol. 19(1), pages 59-69.
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    1. Christopher J Lynch & Saikou Y Diallo & Hamdi Kavak & Jose J Padilla, 2020. "A content analysis-based approach to explore simulation verification and identify its current challenges," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-33, May.
    2. Kaxiras, Efthimios & Neofotistos, Georgios & Angelaki, Eleni, 2020. "The first 100 days: Modeling the evolution of the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    3. Angeli, Mattia & Neofotistos, Georgios & Mattheakis, Marios & Kaxiras, Efthimios, 2022. "Modeling the effect of the vaccination campaign on the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
    4. Fatima-Zohra Younsi & Djamila Hamdadou, 2021. "Dynamic Contact Network Simulation Model Based on Multi-Agent Systems," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 16(4), pages 1-21, October.

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