SIS epidemics on open networks: A replacement-based approximation (2403.16727v1)
Abstract: In this paper we analyze continuous-time SIS epidemics subject to arrivals and departures of agents, by using an approximated process based on replacements. In defining the SIS dynamics in an open network, we consider a stochastic setting in which arrivals and departures take place according to Poisson processes with similar rates, and the new value of the infection probability of an arriving agent is drawn from a continuous distribution. Since the system size changes with time, we define an approximated process, in which replacements take place instead of arrivals and departures, and we focus on the evolution of an aggregate measure of the level of infection. So long as the reproduction number is less than one, the long-term behavior of this function measures the impact of the changes of the set of agents in the epidemic. We derive upper bounds for the expectation and variance of this function and we include a numerical example to show that the approximated process is close to the original SIS process.
- H. W. Hethcote, “The mathematics of infectious diseases,” SIAM review, vol. 42, no. 4, pp. 599–653, 2000.
- C. Nowzari, V. M. Preciado, and G. J. Pappas, “Analysis and control of epidemics: A survey of spreading processes on complex networks,” IEEE Control Systems Magazine, vol. 36, no. 1, pp. 26–46, 2016.
- T. Alamo, P. Millán, D. G. Reina, V. M. Preciado, and G. Giordano, “Challenges and future directions in pandemic control,” IEEE Control Systems Letters, vol. 6, pp. 722–727, 2022.
- M. Tizzoni, P. Bajardi, A. Decuyper, G. Kon Kam King, C. M. Schneider, V. Blondel, Z. Smoreda, M. C. González, and V. Colizza, “On the use of human mobility proxies for modeling epidemics,” PLoS computational biology, vol. 10, no. 7, p. e1003716, 2014.
- S. Hazarie, D. Soriano-Paños, A. Arenas, J. Gómez-Gardeñes, and G. Ghoshal, “Interplay between population density and mobility in determining the spread of epidemics in cities,” Communications Physics, vol. 4, no. 1, pp. 1–10, 2021.
- M. Ogura and V. M. Preciado, “Stability of spreading processes over time-varying large-scale networks,” IEEE Transactions on Network Science and Engineering, vol. 3, no. 1, pp. 44–57, 2016.
- P. E. Paré, C. L. Beck, and A. Nedić, “Epidemic processes over time-varying networks,” IEEE Transactions on Control of Network Systems, vol. 5, no. 3, pp. 1322–1334, 2018.
- J. Leitch, K. A. Alexander, and S. Sengupta, “Toward epidemic thresholds on temporal networks: a review and open questions,” Applied Network Science, vol. 4, pp. 1–21, 2019.
- L. Zino and M. Cao, “Analysis, prediction, and control of epidemics: A survey from scalar to dynamic network models,” IEEE Circuits and Systems Magazine, vol. 21, no. 4, pp. 4–23, 2021.
- V. Abhishek and V. Srivastava, “SIS epidemic spreading under multi-layer population dispersal in patchy environments,” IEEE Transactions on Control of Network Systems, 2023.
- J. M. Hendrickx and S. Martin, “Open multi-agent systems: Gossiping with random arrivals and departures,” in 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017, pp. 763–768.
- M. Franceschelli and P. Frasca, “Stability of open multiagent systems and applications to dynamic consensus,” IEEE Transactions on Automatic Control, vol. 66, no. 5, pp. 2326–2331, 2021.
- R. Vizuete, C. Monnoyer de Galland, P. Frasca, E. Panteley, and J. M. Hendrickx, “Trends and questions in open multi-agent systems,” in Hybrid and Networked Dynamical Systems. Springer, 2024.
- T. Carletti, D. Fanelli, A. Guarino, F. Bagnoli, and A. Guazzini, “Birth and death in a continuous opinion dynamics model: The consensus case,” The European Physical Journal B, vol. 64, pp. 285–292, 2008.
- J. Török, G. Iñiguez, T. Yasseri, M. San Miguel, K. Kaski, and J. Kertész, “Opinions, conflicts, and consensus: Modeling social dynamics in a collaborative environment,” Physical Review Letters, vol. 110, no. 8, p. 088701, 2013.
- A. Lajmanovich and J. A. Yorke, “A deterministic model for gonorrhea in a nonhomogeneous population,” Mathematical Biosciences, vol. 28, no. 3-4, pp. 221–236, 1976.
- A. Khanafer, T. Başar, and B. Gharesifard, “Stability properties of infection diffusion dynamics over directed networks,” in 53rd IEEE Conference on Decision and Control, 2014, pp. 6215–6220.
- R. Vizuete, P. Frasca, and F. Garin, “Graphon-based sensitivity analysis of SIS epidemics,” IEEE Control Systems Letters, vol. 4, no. 3, pp. 542–547, 2020.
- S. Gao and P. E. Caines, “Spectral representations of graphons in very large network systems control,” in 2019 IEEE 58th conference on decision and Control (CDC). IEEE, 2019, pp. 5068–5075.
- R. Brockett, “Stochastic control,” Lecture Notes, Harvard University, 2009.
- R. Brockett, W. Gong, and Y. Guo, “Stochastic analysis for fluid queueing systems,” in Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304), vol. 3, 1999, pp. 3077–3082 vol.3.
- F. Chung and M. Radcliffe, “On the spectra of general random graphs,” The Electronic Journal of Combinatorics, vol. 18, no. 1, p. 215, 2011.
- C. Monnoyer de Galland, S. Martin, and J. M. Hendrickx, “Modelling gossip interactions in open multi-agent systems,” arXiv preprint arXiv:2009.02970, 2020.
- R. Vizuete, “Contributions to open multi-agent systems: consensus, optimization and epidemics,” Ph.D. dissertation, Université Paris-Saclay, 2022.