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Importance of Interaction Structure and Stochasticity for Epidemic Spreading: A COVID-19 Case Study

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Quantitative Evaluation of Systems (QEST 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12289))

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Abstract

In the recent COVID-19 pandemic, computer simulations are used to predict the evolution of the virus propagation and to evaluate the prospective effectiveness of non-pharmaceutical interventions. As such, the corresponding mathematical models and their simulations are central tools to guide political decision-making. Typically, ODE-based models are considered, in which fractions of infected and healthy individuals change deterministically and continuously over time.

In this work, we translate an ODE-based COVID-19 spreading model from literature to a stochastic multi-agent system and use a contact network to mimic complex interaction structures. We observe a large dependency of the epidemic’s dynamics on the structure of the underlying contact graph, which is not adequately captured by existing ODE-models. For instance, existence of super-spreaders leads to a higher infection peak but a lower death toll compared to interaction structures without super-spreaders. Overall, we observe that the interaction structure has a crucial impact on the spreading dynamics, which exceeds the effects of other parameters such as the basic reproduction number \(R_0\).

We conclude that deterministic models fitted to COVID-19 outbreak data have limited predictive power or may even lead to wrong conclusions while stochastic models taking interaction structure into account offer different and probably more realistic epidemiological insights.

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Notes

  1. 1.

    Available at .

  2. 2.

    Available at .

  3. 3.

    At the time of finalizing this manuscript, the model of Khailaie et al. seems to be updated in a similar way. However, it also became more complex (see

  4. 4.
  5. 5.

    The number of fatalities in the figures is difficult to see, but it is (in the time limit) proportional to the number of recovered nodes.

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Acknowledgements

We thank Luca Bortolussi and Thilo Krüger for helpful comments regarding the manuscript. This work was partially funded by the DFG project MULTIMODE.

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Authors and Affiliations

  1. Saarland Informatics Campus, Saarland University, 66123, Saarbrücken, Germany

    Gerrit Großmann, Michael Backenköhler & Verena Wolf

Authors
  1. Gerrit Großmann
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  2. Michael Backenköhler
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Correspondence to Gerrit Großmann .

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Editors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Marco Gribaudo

  2. Chinese Academy of Sciences, Beijing, China

    David N. Jansen

  3. University of Münster, Münster, Germany

    Anne Remke

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Großmann, G., Backenköhler, M., Wolf, V. (2020). Importance of Interaction Structure and Stochasticity for Epidemic Spreading: A COVID-19 Case Study. In: Gribaudo, M., Jansen, D.N., Remke, A. (eds) Quantitative Evaluation of Systems. QEST 2020. Lecture Notes in Computer Science(), vol 12289. Springer, Cham. https://doi.org/10.1007/978-3-030-59854-9_16

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  • DOI: https://doi.org/10.1007/978-3-030-59854-9_16

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