IEEE Circuits and Systems Magazine - Q4 2021 - 23

[109] V. L. J. Somers and I. R. Manchester, " Sparse resource allocation
for control of spreading processes via convex optimization, " IEEE Control
Syst. Lett., vol. 5, no. 2, pp. 547-552, 2021. doi: 10.1109/LCSYS.2020.3003401.
[110] N. M. Hung, Q. van Tran, J. Liu, and H. Ahn, " Resource allocation
for epidemic network under complications, " in Proc. 2019 19th
Int. Conf. Control, Automat. Syst. (ICCAS), pp. 1512-1516. doi: 10.23919/
ICCAS47443.2019.8971743.
[111] P. Di Giamberardino and D. Iacoviello, " Optimal resource allocation
to reduce an epidemic spread and its complication, " Information,
vol. 10, no. 6, p. 213, 2019. doi: 10.3390/info10060213.
[112] M. Bloem, T. Alpcan, and T. Bas¸ar, " Optimal and robust epidemic
response for multiple networks, " Control Eng. Pract., vol. 17, no. 5,
pp. 525-533, 2009. doi: 10.1016/j.conengprac.2008.10.007.
[113] S. Eshghi, M. H. R. Khouzani, S. Sarkar, and S. S. Venkatesh, " Optimal
patching in clustered malware epidemics, " IEEE/ACM Trans. Netw.,
vol. 24, no. 1, pp. 283-298, 2016. doi: 10.1109/TNET.2014.2364034.
[114] M. Ye, J. Liu, B. D. O. Anderson, and M. Cao, " Distributed feedback control
on the SIS network model: An impossibility result, " IFAC-PapersOnLine,
2020. doi: 10.1016/j.ifacol.2020.12.2844.
[115] M. Ye, J. Liu, B. D. O. Anderson, and M. Cao, " Applications of the
Poincaré-Hopf Theorem: Epidemic Models and Lotka-Volterra Systems, "
IEEE Trans. Autom. Control, 2021. doi: 10.1109/TAC.2021.3064519.
[116] J. Liu, P. E. Paré, A. Nedic´, C. Y. Tang, C. L. Beck, and T. Bas¸ar,
" Analysis and control of a continuous-time bi-virus model, " IEEE
Trans. Autom. Control, vol. 64, no. 12, pp. 4891-4906, 2019. doi: 10.1109/
TAC.2019.2898515.
[117] E. H. Bussell, C. E. Dangerfield, C. A. Gilligan, and N. J. Cunniffe,
" Applying optimal control theory to complex epidemiological models
to inform real-world disease management, " Philos. Trans. Roy. Soc. B,
Biol. Sci., vol. 374, no. 1776, p. 20,180,284, 2019.
[118] C. Borgs, J. Chayes, A. Ganesh, and A. Saberi, " How to distribute
antidote to control epidemics, " Random Struct. Algorithms, vol. 37,
pp. 204-222, 2010. doi: 10.1002/rsa.20315.
[119] K. Drakopoulos, A. Ozdaglar, and J. N. Tsitsiklis, " An efficient curing
policy for epidemics on graphs, " IEEE Trans. Netw. Sci. Eng., vol. 1,
pp. 67-75, 2014. doi: 10.1109/TNSE.2015.2393291.
[120] K. Drakopoulos, A. Ozdaglar, and J. N. Tsitsiklis, " When is a
network epidemic hard to eliminate? " Math. Oper. Res., vol. 42, no. 1,
pp. 1-14, 2017.
[121] K. Scaman, A. Kalogeratos, and N. Vayatis, " Suppressing epidemics
in networks using priority planning, " IEEE Trans. Netw. Sci. Eng.,
vol. 3, no. 4, pp. 271-285, 2016. doi: 10.1109/TNSE.2016.2600029.
[122] F. Sélley, Á. Besenyei, I. Z. Kiss, and P. L. Simon, " Dynamic control
of modern, network-based epidemic models, " SIAM J. Appl. Dyn. Syst.,
vol. 14, no. 1, pp. 168-187, 2015. doi: 10.1137/130947039.
[123] J. Köhler, C. Enyioha, and F. Allgöwer, " Dynamic resource allocation
to control epidemic outbreaks a model predictive control approach, "
in Proc. 2018 Annu. Amer. Control Conf., pp. 1546-1551.
[124] N. J. Watkins, C. Nowzari, and G. J. Pappas, " Robust economic
model predictive control of continuous-time epidemic processes, " IEEE
Trans. Autom. Control, vol. 65, no. 3, pp. 1116-1131, 2020. doi: 10.1109/
TAC.2019.2919136.
[125] P. Grandits, R. M. Kovacevic, and V. M. Veliov, " Optimal control
and the value of information for a stochastic epidemiological SIS-model, "
J. Math. Anal. Appl., vol. 476, no. 2, pp. 665-695, 2019. doi: 10.1016/j.
jmaa.2019.04.005.
[126] C. Nowzari, M. Ogura, V. M. Preciado, and G. J. Pappas, " Optimal resource
allocation for containing epidemics on time-varying networks, " in
Proc. 49th Asilomar Conf. Signals, Syst. Comput., 2015, pp. 1333-1337.
[127] M. Youssef and C. Scoglio, " Mitigation of epidemics in contact networks
through optimal contact adaptation, " Math. Biosci. Eng., vol. 10,
no. 4, pp. 1227-1251, 2013.
[128] S. Gracy, P. E. Pare, H. Sandberg, and K. H. Johansson, " Analysis
and distributed control of periodic epidemic processes, " IEEE Trans.
Control Netw. Syst., 2020, doi: 10.1109/TCNS.2020.3017717.
[129] S. Liu, N. Perra, M. Karsai, and A. Vespignani, " Controlling contagion
processes in activity driven networks, " Phys. Rev. Lett., vol. 112,
p. 118,702, 2014.
[130] A. Rizzo, M. Frasca, and M. Porfiri, " Effect of individual behavior
on epidemic spreading in activity driven networks, " Phys. Rev. E,
vol. 90, p. 042801, 2014.
[131] L. Zino, A. Rizzo, and M. Porfiri, " Analysis and control of epidemics
in temporal networks with self-excitement and behavioral changes, "
Eur. J. Control, vol. 54, pp. 1-11, 2020. doi: 10.1016/j.ejcon.2019.12.007.
FOURTH QUARTER 2021
[132] S. Funk, E. Gilad, C. Watkins, and V. A. A. Jansen, " The spread of
awareness and its impact on epidemic outbreaks. " Proc. Nat. Acad. Sci.,
vol. 106, no. 16, pp. 6872-6877, 2009. doi: 10.1073/pnas.0810762106.
[133] M. Ogura, V. M. Preciado, and N. Masuda, " Optimal containment of
epidemics over temporal activity-driven networks, " SIAM J. Appl. Math.,
vol. 79, no. 3, pp. 986-1006, 2019. doi: 10.1137/18M1172740.
[134] H. Yang, C. Gu, M. Tang, S.-M. Cai, and Y.-C. Lai, " Suppression of
epidemic spreading in time-varying multiplex networks, " Appl. Math.
Model., vol. 75, pp. 806-818, 2019. doi: 10.1016/j.apm.2019.07.011.
[135] L. Zino, A. Rizzo, and M. Porfiri, " On assessing control actions for
epidemic models on temporal networks, " IEEE Control Syst. Lett., vol. 4,
no. 4, pp. 797-802, 2020. doi: 10.1109/LCSYS.2020.2993104.
[136] A. L. Bertozzi, E. Franco, G. Mohler, M. B. Short, and D. Sledge,
" The challenges of modeling and forecasting the spread of COVID-19, "
Proc. Nat. Acad. Sci., vol. 117, no. 29, pp. 16,732-16,738, 2020.
[137] A. Vespignani et al., " Modelling COVID-19, " Nature Rev. Phys.,
vol. 2, no. 6, pp. 279-281, 2020. doi: 10.1038/s42254-020-0178-4.
[138] G. Stewart, K. Heusden, and G. A. Dumont, " How control theory
can help us control Covid-19, " IEEE Spectr., vol. 57, no. 6, pp. 22-29, 2020.
doi: 10.1109/MSPEC.2020.9099929.
[139] M. Bin et al., " Post-lockdown abatement of COVID-19 by fast periodic
switching, " PLOS Comput. Biol., vol. 17, p. e1008604, 2021. doi:
10.1371/journal.pcbi.1008604.
[140] M. Gatto et al., " Spread and dynamics of the COVID-19 epidemic
in Italy: Effects of emergency containment measures, " Proc. Nat. Acad.
Sci., vol. 117, no. 19, pp. 10,484-10,491, 2020.
[141] F. Parino, L. Zino, M. Porfiri, A. Rizzo, " Modelling and predicting
the effect of social distancing and travel restrictions on COVID-19
spreading, " J. Roy. Soc. Interface, vol. 18, p. 20,200,875, 2021.
[142] V. A. Karatayev, M. Anand, and C. T. Bauch, " Local lockdowns
outperform global lockdown on the far side of the COVID-19 epidemic
curve, " Proc. Nat. Acad. Sci., vol. 117, no. 39, pp. 24575-24580, 2020.
[143] M. Small and D. Cavanagh, " Modelling strong control measures
for epidemic propagation with networks-A COVID-19 case study, " IEEE
Access, vol. 8, pp. 109,719-109,731, 2020.
[144] A. Kuzdeuov et al., " A network-based stochastic epidemic simulator:
controlling COVID-19 with region-specific policies, " IEEE J. Biomed.
Health Informat., vol. 24, no. 10, pp. 2743-2754, 2020. doi: 10.1109/
JBHI.2020.3005160.
[145] R. Carli, G. Cavone, N. Epicoco, P. Scarabaggio, and M. Dotoli,
" Model predictive control to mitigate the COVID-19 outbreak in a multiregion
scenario, " Annu. Rev. Control, vol. 50, pp. 373-393, 2020. doi:
10.1016/j.arcontrol.2020.09.005.
[146] J. Köhler, L. Schwenkel, A. Koch, J. Berberich, P. Pauli, and F.
Allgöwer, " Robust and optimal predictive control of the COVID-19 outbreak, "
Annu. Rev. Control, 2020. doi: 10.1016/j.arcontrol.2020.11.002.
[147] F. Della Rossa et al., " A network model of Italy shows that intermittent
regional strategies can alleviate the COVID-19 epidemic, " Nature
Commun., vol. 11, no. 1, 2020. doi: 10.1038/s41467-020-18827-5.
[148] J. J. Van Bavel et al., " Using social and behavioural science to support
COVID-19 pandemic response, " Nature Human Behav., vol. 4, no. 5,
pp. 460-471, 2020.
[149] S. N. Wood, E. C. Wit, M. Fasiolo, and P. J. Green, " COVID-19 and the
difficulty of inferring epidemiological parameters from clinical data, " Lancet
Infect. Dis., vol. 21, pp. 27-28, 2020. doi: 10.1016/S1473-3099(20)30437-0.
[150] G. C. Calafiore, C. Novara, and C. Possieri, " A time-varying SIRD
model for the COVID-19 contagion in Italy, " Annu. Rev. Control, vol. 50,
pp. 361-372, 2020. doi: 10.1016/j.arcontrol.2020.10.005.
[151] P. Poletti, B. Caprile, M. Ajelli, A. Pugliese, and S. Merler, " Spontaneous
behavioural changes in response to epidemics, " J. Theor. Biol.,
vol. 260, no. 1, pp. 31-40, 2009. doi: 10.1016/j.jtbi.2009.04.029.
[152] T. C. Reluga, " Game theory of social distancing in response to an
epidemic, " PLOS Comput. Biol., vol. 6, no. 5, May 2010. doi: 10.1371/journal.pcbi.1000793.
[153]
C. T. Bauch and D. J. D. Earn, " Vaccination and the theory of
games, " Proc. Nat. Acad. Sci., vol. 101, no. 36, pp. 13,391-13,394, 2004.
[154] S. Trajanovski, F. A. Kuipers, Y. Hayel, E. Altman, and P. Van
Mieghem, " Designing virus-resistant, high-performance networks: a
game-formation approach, " IEEE Trans. Control Netw. Syst., vol. 5, no. 4,
pp. 1682-1692, 2018. doi: 10.1109/TCNS.2017.2747840.
[155] A. R. Hota and S. Sundaram, " Game-theoretic vaccination against
networked sis epidemics and impacts of human decision-making, " IEEE
Trans. Control Netw. Syst., vol. 6, no. 4, pp. 1461-1472, 2019. doi: 10.1109/
TCNS.2019.2897904.
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IEEE Circuits and Systems Magazine - Q4 2021

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