TY - JOUR
T1 - Adaptive Susceptibility and Heterogeneity in Contagion Models on Networks
AU - Pagliara, Renato
AU - Leonard, Naomi Ehrich
N1 - Funding Information:
Manuscript received July 17, 2019; revised July 19, 2019 and January 2, 2020; accepted March 29, 2020. Date of publication April 6, 2020; date of current version January 28, 2021. The work was supported in part by the Army Research Office under Grant W911NF-18-1-0325 and in part by the Office of Naval Research under Grant N00014-19-1-2556. Recommended by Associate Editor I. Queinnec. (Corresponding author: Naomi Ehrich Leonard.) The authors are with the Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544 USA (e-mail: renapagli@gmail.com; naomi@princeton.edu).
Publisher Copyright:
© 1963-2012 IEEE.
PY - 2021/2
Y1 - 2021/2
N2 - Contagious processes, such as spread of infectious diseases, social behaviors, or computer viruses, affect biological, social, and technological systems. Epidemic models for large populations and finite populations on networks have been used to understand and control both transient and steady-state behaviors. Typically it is assumed that after recovery from an infection, every agent will either return to its original susceptible state or acquire full immunity to reinfection. We study the network SIRI (Susceptible-Infected-Recovered-Infected) model, an epidemic model for the spread of contagious processes on a network of heterogeneous agents that can adapt their susceptibility to reinfection. The model generalizes existing models to accommodate realistic conditions in which agents acquire partial or compromised immunity after first exposure to an infection. We prove necessary and sufficient conditions on model parameters and network structure that distinguish four dynamic regimes: infection-free, epidemic, endemic, and bistable. For the bistable regime, which is not accounted for in traditional models, we show how there can be a rapid resurgent epidemic after what looks like convergence to an infection-free population. We use the model and its predictive capability to show how control strategies can be designed to mitigate problematic contagious behaviors.
AB - Contagious processes, such as spread of infectious diseases, social behaviors, or computer viruses, affect biological, social, and technological systems. Epidemic models for large populations and finite populations on networks have been used to understand and control both transient and steady-state behaviors. Typically it is assumed that after recovery from an infection, every agent will either return to its original susceptible state or acquire full immunity to reinfection. We study the network SIRI (Susceptible-Infected-Recovered-Infected) model, an epidemic model for the spread of contagious processes on a network of heterogeneous agents that can adapt their susceptibility to reinfection. The model generalizes existing models to accommodate realistic conditions in which agents acquire partial or compromised immunity after first exposure to an infection. We prove necessary and sufficient conditions on model parameters and network structure that distinguish four dynamic regimes: infection-free, epidemic, endemic, and bistable. For the bistable regime, which is not accounted for in traditional models, we show how there can be a rapid resurgent epidemic after what looks like convergence to an infection-free population. We use the model and its predictive capability to show how control strategies can be designed to mitigate problematic contagious behaviors.
KW - Adaptive systems, complex networks
KW - multiagent systems
KW - propagation of infection
KW - spreading dynamics
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U2 - 10.1109/TAC.2020.2985300
DO - 10.1109/TAC.2020.2985300
M3 - Article
AN - SCOPUS:85100378637
SN - 0018-9286
VL - 66
SP - 581
EP - 594
JO - IEEE Transactions on Automatic Control
JF - IEEE Transactions on Automatic Control
IS - 2
M1 - 9057446
ER -