Abstract
Many contagion processes evolving on populations do so simultaneously, interacting over time. Examples are co-evolution of human social processes and diseases, such as the uptake of mask wearing and disease spreading. Commensurately, multi-contagion agent-based simulations (ABSs) that represent populations as networks in order to capture interactions between pairs of nodes are becoming more popular. In this work, we present a new ABS system that simulates any number of contagions co-evolving on any number of networked populations. Individual (interacting) contagion models and individual networks are specified, and the system computes multi-contagion dynamics over time. This is a significant improvement over simulation frameworks that require union graphs to handle multiple networks, and/or additional code to orchestrate the computations of multiple contagions. We provide a formal model for the simulation system, an overview of the software, and case studies that illustrate applications of interacting contagions.
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Acknowledgments
We thank the anonymous reviewers for their helpful feedback. We thank our colleagues at NSSAC and Research Computing at the University of Virginia for providing computational resources and technical support. This work has been partially supported by University of Virginia Strategic Investment Fund award number SIF160, NSF Grant OAC-1916805 (CINES), NSF Grant CMMI-1916670 (CRISP 2.0) and CCF-1918656 (Expeditions).
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Kishore, A., Machi, L., Kuhlman, C.J., Machi, D., Ravi, S.S. (2022). A Framework for Simulating Multiple Contagions Over Multiple Networks. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-93413-2_21
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