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Simulating SARS: Small-World Epidemiological Modeling and Public Health Policy Assessments

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Abstract

The authors propose a novel small-world model that makes use of cellular automata with the mirror identities of daily-contact social networks to simulate epidemiological scenarios. We established the mirror identity concept (a miniature representation of frequently visited places) to acknowledge human long-distance movement and geographic mobility. Specifically, the model was used to a) simulate the dynamics of SARS transmission in Singapore, Taipei, and Toronto and b) discuss the effectiveness of the respective public health policies of those cities. We believe the model can be applied to influenza, enteroviruses, AIDS, and other contagious diseases according to the various needs of health authorities.

Suggested Citation

  • Chung-Yuan Huang & Chuen-Tsai Sun & Ji-Lung Hsieh & Holin Lin, 2004. "Simulating SARS: Small-World Epidemiological Modeling and Public Health Policy Assessments," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 7(4), pages 1-2.
    • Handle: RePEc:jas:jasssj:2004-29-2
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    Cited by:

    1. Xiao Xue & Shufang Wang & Baoyun Lu, 2015. "Computational Experiment Approach to Controlled Evolution of Procurement Pattern in Cluster Supply Chain," Sustainability, MDPI, vol. 7(2), pages 1-26, January.
    2. Teruhiko Yoneyama & Sanmay Das & Mukkai Krishnamoorthy, 2012. "A Hybrid Model for Disease Spread and an Application to the SARS Pandemic," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(1), pages 1-5.
    3. Xiaoguang Gong & Renbin Xiao, 2007. "Research on Multi-Agent Simulation of Epidemic News Spread Characteristics," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(3), pages 1-1.
    4. Chung-Yuan Huang & Chuen-Tsai Sun & Hsun-Cheng Lin, 2005. "Influence of Local Information on Social Simulations in Small-World Network Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-8.
    5. Dario Esposito & Giovanni Dipierro & Alberico Sonnessa & Stefania Santoro & Simona Pascazio & Irene Pluchinotta, 2021. "Data-Driven Epidemic Intelligence Strategies Based on Digital Proximity Tracing Technologies in the Fight against COVID-19 in Cities," Sustainability, MDPI, vol. 13(2), pages 1-25, January.
    6. Yong Yang & Peter M Atkinson, 2008. "Individual Space – Time Activity-Based Model: A Model for the Simulation of Airborne Infectious-Disease Transmission by Activity-Bundle Simulation," Environment and Planning B, , vol. 35(1), pages 80-99, February.
    7. Phillip Stroud & Sara Del Valle & Stephen Sydoriak & Jane Riese & Susan Mniszewski, 2007. "Spatial Dynamics of Pandemic Influenza in a Massive Artificial Society," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(4), pages 1-9.

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