TY - JOUR
T1 - On Assessing Control Actions for Epidemic Models on Temporal Networks
AU - Zino, Lorenzo
AU - Rizzo, Alessandro
AU - Porfiri, Maurizio
N1 - Funding Information:
Manuscript received March 16, 2020; revised April 17, 2020; accepted May 4, 2020. Date of publication May 7, 2020; date of current version May 27, 2020. This work was supported in part by the National Science Foundation under Grant CMMI-1561134 and Grant CMMI-2027990, in part by the Compagnia di San Paolo, in part by MAECI (“Mac2Mic”), in part by the European Research Council under Grant ERC-CoG-771687, and in part by the Netherlands Organization for Scientific Research under Grant NWO-vidi-14134. Recommended by Senior Editor M. Arcak. (Corresponding authors: Alessandro Rizzo; Maurizio Porfiri.) Lorenzo Zino is with the Faculty of Science and Engineering, University of Groningen, 9747 AG Groningen, The Netherlands (e-mail: lorenzo.zino@rug.nl).
Publisher Copyright:
© 2017 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - In this letter, we propose an epidemic model over temporal networks that explicitly encapsulates two different control actions. We develop our model within the theoretical framework of activity driven networks (ADNs), which have emerged as a valuable tool to capture the complexity of dynamical processes on networks, coevolving at a comparable time scale to the temporal network formation. Specifically, we complement a susceptible-infected-susceptible epidemic model with features that are typical of nonpharmaceutical interventions in public health policies: i) actions to promote awareness, which induce people to adopt self-protective behaviors, and ii) confinement policies to reduce the social activity of infected individuals. In the thermodynamic limit of large-scale populations, we use a mean-field approach to analytically derive the epidemic threshold, which offers viable insight to devise containment actions at the early stages of the outbreak. Through the proposed model, it is possible to devise an optimal epidemic control policy as the combination of the two strategies, arising from the solution of an optimization problem. Finally, the analytical computation of the epidemic prevalence in endemic diseases on homogeneous ADNs is used to optimally calibrate control actions toward mitigating an endemic disease. Simulations are provided to support our theoretical results.
AB - In this letter, we propose an epidemic model over temporal networks that explicitly encapsulates two different control actions. We develop our model within the theoretical framework of activity driven networks (ADNs), which have emerged as a valuable tool to capture the complexity of dynamical processes on networks, coevolving at a comparable time scale to the temporal network formation. Specifically, we complement a susceptible-infected-susceptible epidemic model with features that are typical of nonpharmaceutical interventions in public health policies: i) actions to promote awareness, which induce people to adopt self-protective behaviors, and ii) confinement policies to reduce the social activity of infected individuals. In the thermodynamic limit of large-scale populations, we use a mean-field approach to analytically derive the epidemic threshold, which offers viable insight to devise containment actions at the early stages of the outbreak. Through the proposed model, it is possible to devise an optimal epidemic control policy as the combination of the two strategies, arising from the solution of an optimization problem. Finally, the analytical computation of the epidemic prevalence in endemic diseases on homogeneous ADNs is used to optimally calibrate control actions toward mitigating an endemic disease. Simulations are provided to support our theoretical results.
KW - Control of networks
KW - epidemics
KW - epidemiology
KW - network analysis and control
KW - predictive model
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U2 - 10.1109/LCSYS.2020.2993104
DO - 10.1109/LCSYS.2020.2993104
M3 - Article
AN - SCOPUS:85084754849
SN - 2475-1456
VL - 4
SP - 797
EP - 802
JO - IEEE Control Systems Letters
JF - IEEE Control Systems Letters
IS - 4
M1 - 9089218
ER -