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An open-data-driven agent-based model to simulate infectious disease outbreaks

Author

Listed:
  • Elizabeth Hunter
  • Brian Mac Namee
  • John Kelleher

Abstract

Agent-based models are a tool that can be used to better understand the dynamics of an infectious disease outbreak. An infectious disease outbreak is influenced by many factors including vaccination or immunity levels, population density, and the age structure of the population. We hypothesize that these factors along with interactions of factors and the actions of individuals would lead to outbreaks of different size and severity even in two towns that appear similar on paper. Thus, it is necessary to implement a model that is able to capture these interactions and the actions of individuals. Using openly available data we create a data-driven agent-based model to simulate the spread of an airborne infectious disease in an Irish town. Agent-based models have been known to produce results that include the emergence of patterns and behaviours that are not directly programmed into the model. Our model is tested by simulating an outbreak of measles that occurred in Schull, Ireland in 2012. We simulate the same outbreak in 33 different towns and look at the correlations between the model results and the town characteristics (population, area, vaccination rates, age structure) to determine if the results of the model are affected by interactions of those town characteristics and the decisions on the agents in the model. As expected our results show that the outbreaks are not strongly correlated with any of the main characteristics of the towns and thus the model is most likely capturing such interactions and the agent-based model is successful in capturing the differences in the outbreaks.

Suggested Citation

  • Elizabeth Hunter & Brian Mac Namee & John Kelleher, 2018. "An open-data-driven agent-based model to simulate infectious disease outbreaks," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-35, December.
  • Handle: RePEc:plo:pone00:0208775
    DOI: 10.1371/journal.pone.0208775
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    References listed on IDEAS

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    1. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    2. Elizabeth Hunter & Brian Mac Namee & John D. Kelleher, 2017. "A Taxonomy for Agent-Based Models in Human Infectious Disease Epidemiology," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(3), pages 1-2.
    3. Carol Y. Lin, 2008. "Modeling Infectious Diseases in Humans and Animals by KEELING, M. J. and ROHANI, P," Biometrics, The International Biometric Society, vol. 64(3), pages 993-993, September.
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    Cited by:

    1. Ali Asgary & Hudson Blue & Adriano O. Solis & Zachary McCarthy & Mahdi Najafabadi & Mohammad Ali Tofighi & Jianhong Wu, 2022. "Modeling COVID-19 Outbreaks in Long-Term Care Facilities Using an Agent-Based Modeling and Simulation Approach," IJERPH, MDPI, vol. 19(5), pages 1-16, February.
    2. Talal Daghriri & Michael Proctor & Sarah Matthews, 2022. "Evolution of Select Epidemiological Modeling and the Rise of Population Sentiment Analysis: A Literature Review and COVID-19 Sentiment Illustration," IJERPH, MDPI, vol. 19(6), pages 1-20, March.
    3. Elizabeth Hunter & Brian Mac Namee & John D. Kelleher, 2020. "A Model for the Spread of Infectious Diseases in a Region," IJERPH, MDPI, vol. 17(9), pages 1-19, April.
    4. Constanza Fosco & Felipe Zurita, 2021. "Assessing the short-run effects of lockdown policies on economic activity, with an application to the Santiago Metropolitan Region, Chile," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-23, June.

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