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

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 …

@inproceedings{grossmannImportanceOfInteractionStructure2020,
 author = {Gerrit Großmann and Michael Backenköhler and Verena Wolf},
 booktitle = {Quantitative Evaluation of Systems. International Conference on Quantitative Evaluation of Systems (QEST-2020), 17th International Conference, QEST 2020, October 31 - September 1, Vienna, Austria},
 doi = {10.1007/978-3-030-59854-9_16},
 edition = {1st},
 editor = {Marco Gribaudo and David N. Jansen and Anne Remke},
 isbn = {9783030598549},
 month = {9},
 pages = {211-229},
 publisher = {Springer International Publishing, Switzerland},
 title = {Importance of Interaction Structure and Stochasticity for Epidemic Spreading: A COVID-19 Case Study},
 volume = {12289},
 year = {2020}
}