Control, intervention, and behavioral economics over human social networks against COVID-19

References

• Bobrow J, Dubowsky S, Gibson J. Time-optimal control of robotic manipulators along specified paths. Int J Rob Res. 1985;4(3):3–17.
   Web of Science ®Google Scholar
• Leung GMH, Francis BA, Apkarian J. Bilateral controller for teleoperators with time delay via μ-synthesis. IEEE Trans Robot Autom. 1995;11(1):105–116.
   Google Scholar
• Schultz G, Mombaur K. Modeling and optimal control of human-like running. IEEE/ASME Trans Mechatron. 2010;15(5):783–792.
   Web of Science ®Google Scholar
• Sowe SK, Simmon E, Zettsu K, et al. Cyber-physical-human systems: putting people in the loop. IT Prof. 2016;18(1):10–13.
   PubMed Web of Science ®Google Scholar
• Annaswamy A. Cyberphysical and human systems: A new frontier in control systems [president's message]. IEEE Control Syst Mag. 2020;40(4):8–9.
   Web of Science ®Google Scholar
• Ojo O, García-Agundez A, Girault B, et al. Coronasurveys: Using surveys with indirect reporting to estimate the incidence and evolution of epidemics. arXiv preprint arXiv:200512783; 2020;.
   Google Scholar
• Dong X, Thanou D, Rabbat M, et al. Learning graphs from data: A signal representation perspective. IEEE Signal Process Mag. 2019;36(3):44–63.
   Web of Science ®Google Scholar
• Mateos G, Segarra S, Marques AG, et al. Connecting the dots: identifying network structure via graph signal processing. IEEE Signal Process Mag. 2019;36(3):16–43.
   Web of Science ®Google Scholar
• Kalofolias V. How to learn a graph from smooth signals. In: International Conference on Artificial Intelligence and Statistics AISTATS, Cadiz, Spain; 2016.
   Google Scholar
• Hallac D, Park Y, Boyd S, et al. Network inference via the time-varying graphical lasso. In: Proc. of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '17), New York (NY); 2017. p. 205–213.
   Google Scholar
• Egilmez HE, Pavez E, Ortega A. Graph learning from data under laplacian and structural constraints. IEEE J Sel Top Signal Process. 2017;11(6):825–841.
   Web of Science ®Google Scholar
• Yamada K, Tanaka Y, Ortega A. Time-varying graph learning based on sparseness of temporal variation. In: Proc. Int. Conf. Acoust., Speech, Signal Process. (ICASSP); 2019. p. 5411–5415.
   Google Scholar
• Yamada K, Tanaka Y, Ortega A. Time-varying graph learning with constraints on graph temporal variation. arXiv preprint arXiv:200103346; 2020.
   Google Scholar
• Pastor-Satorras R, Castellano C, Van Mieghem P, et al. Epidemic processes in complex networks. Rev Mod Phys. 2015;87(3):925–979.
   Web of Science ®Google Scholar
• Nowzari C, Preciado VM, Pappas GJ. Analysis and control of epidemics: a survey of spreading processes on complex networks. IEEE Control Syst. 2016;36(1):26–46.
   Web of Science ®Google Scholar
• Ogura M, Preciado VM. Stability of spreading processes over time-varying large-scale networks. IEEE Trans Netw Sci Engin. 2016 1;3(1):44–57.
   Web of Science ®Google Scholar
• Costa OLV, Fragoso MD, Todorov MG. Continuous-time Markov jump linear systems. Berlin: Springer; 2013.
   Google Scholar
• Farina L, Rinaldi S. Positive linear systems: theory and applications. New York (NY): Wiley-Interscience; 2000.
   Google Scholar
• Boyd S, Kim SJ, Vandenberghe L, et al. A tutorial on geometric programming. Optim Engin. 2007;8(1):67–127.
   Web of Science ®Google Scholar
• Ogura M, Preciado VM. Epidemic processes over adaptive state-dependent networks. Phys Rev E. 2016;93:062316.
   PubMed Web of Science ®Google Scholar
• Imbens GW, Rubin DB. Causal inference for statistics, social, and biomedical science. New York (NY): Cambridge University Press; 2015.
   Google Scholar
• Murakami K, Shimada H, Ushifusa Y, et al. Heterogeneous treatment effects of nudge and rebate: causal machine learning in a field experiment on electricity conservation. Kyoto University; 2020. (Graduate School of Economics Discussion Paper Series). issue.E-20-003:1–46.
   Google Scholar
• Hekmati A, Luhar M, Krishnamachari B, et al. Simulation based analysis of covid-19 spread through classroom transmission on a university campus. in submission; 2021.
   Google Scholar
• Anderson RM, May RM. Infectious diseases of humans: dynamics and control. New York (NY): Oxford University Press; 1991.
   Google Scholar
• Diamond J. Guns, germs and steel: the fates of human societies. New York (NY): Norton; 1997.
   Google Scholar
• McNeill WH. Plagues and peoples. New York (NY): Doubleday; 1977.
   Google Scholar
• Valente TW. Social networks and health: models, methods, and applications. New York (NY): Oxford University Press; 2010.
   Google Scholar
• Christakis NA, Fowler JH. The spread of obesity in a large social network over 32 years. N Engl J Med. 2007;357:370–379.
   PubMed Web of Science ®Google Scholar
• Valente T, Fujimoto K, Soto D, et al. A comparison of peer influence measures as predictors of smoking among predominately hispanic/latino high school adolescents. J Adolescent Health. 2012;52:358–364.
   PubMed Web of Science ®Google Scholar
• Valente TW, Watkins S, Jato MN, et al. Social network associations with contraceptive use among cameroonian women in voluntary associations. Soc Sci Med. 1997;45:677–687.
   PubMed Web of Science ®Google Scholar
• Iyengar R, Van den Bulte C, Valente TW. Opinion leadership and contagion in new product diffusion. Market Sci. 2011;30:195–212.
   Web of Science ®Google Scholar
• Valente TW, Dyal SP, Chu KC, et al. Diffusion of innovations theory applied to global tobacco control treaty ratification. Soc Sci Med. 2015;145:89–97.
   PubMed Web of Science ®Google Scholar
• Bell D, Nicoll A, Fukuda K, et al. Nonpharmaceutical interventions for pandemic influenza, national and community measures. Emerging Infect Dis. 2006;12(1):88–94.
   PubMed Web of Science ®Google Scholar
• Funk S, Salathé M, Jansen VAA. Modelling the influence of human behaviour on the spread of infectious diseases: a review. J R Soc Interface. 2010 9;7(50):1247–1256.
   PubMed Web of Science ®Google Scholar
• Ogura M, Preciado VM, Masuda N. Optimal containment of epidemics over temporal activity-driven networks. SIAM J Appl Math. 2019;79(3):986–1006.
   Web of Science ®Google Scholar
• Giordano G, Blanchini F, Bruno R, et al. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy. Nat Med. 2020 6;26(6):855–860.
   PubMed Web of Science ®Google Scholar
• Ogura M, Preciado VM. Efficient containment of exact SIR Markovian processes on networks. In: 55th IEEE Conference on Decision and Control; 2016. p. 967–972.
   Google Scholar
• Ogura M, Kishida M, Lam J. Geometric programming for optimal positive linear systems. IEEE Trans Automat Contr. 2020 11;65(11):4648–4663.
   Web of Science ®Google Scholar
• Onoue Y, Hashimoto K, Ogura M, et al. Event-triggered control for mitigating SIS spreading processes. Submitted for publication; 2020.
   Google Scholar
• Nagahara M, Quevedo DE, Nešić D. Maximum hands-off control: a paradigm of control effort minimization. IEEE Trans Autom Control. 2016;61(3):735–747.
   Web of Science ®Google Scholar
• Nagahara M, Chatterjee D, Challapalli N, et al. CLOT norm minimization for continuous hands-off control. Automatica. 2020;113:108679.
   Web of Science ®Google Scholar
• Nagahara M. Sparsity methods for systems and control. Boston (MA): Now Publishing; 2020.
   Google Scholar
• Flaxman S, Mishra S, Gandy A, et al. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature. 2020;584(7820):257–261.
   PubMed Web of Science ®Google Scholar
• Sebhatu A, Wennberg K, Arora-Jonsson S, et al. Explaining the homogeneous diffusion of COVID-19 nonpharmaceutical interventions across heterogeneous countries. Proc Natl Acad Sci USA. 2020;117(35):21201–21208.
   PubMed Web of Science ®Google Scholar
• Su R. Real-time V2X-based traffic signal control for large traffic networks. IEEE CDC 2019 Workshop: Systems and Control for Smart Society and Cyber-Physical & Human Systems; 2019.
   Google Scholar
• Mishima OA Systems. Social distance AI; 2020. https://www.mishimaoa.co.jp/products/sds-ai/.
   Google Scholar
• Lai TL, Robbins H. Asymptotically efficient adaptive allocation rules. Adv Appl Math. 1985;6(1):4–22.
   Web of Science ®Google Scholar
• Auer P, Cesa-Bianchi N, Fischer P. Finite-time analysis of the multiarmed bandit problem. Mach Learn. 2002;47(2):235–256.
   Web of Science ®Google Scholar
• Kaufmann E, Korda N, Munos R. Thompson sampling: an asymptotically optimal finite-time analysis. In: International Conference on Algorithmic Learning Theory. Springer; 2012. p. 199–213.
   Google Scholar
• Ahmad SHA, Liu M, Javidi T, et al. Optimality of myopic sensing in multichannel opportunistic access. IEEE Trans Infor Theory. 2009;55(9):4040–4050.
   Web of Science ®Google Scholar
• Gai Y, Krishnamachari B, Jain R. Combinatorial network optimization with unknown variables: multi-armed bandits with linear rewards and individual observations. IEEE ACM Trans Netw. 2012;20(5):1466–1478.
   Web of Science ®Google Scholar
• Mansourifard P, Jazizadeh F, Krishnamachari B, et al. Online learning for personalized room-level thermal control: A multi-armed bandit framework. In: Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings; 2013. p. 1–8.
   Google Scholar
• Sakulkar P, Krishnamachari B. Stochastic contextual bandits with known reward functions. arXiv preprint arXiv:160500176; 2016.
   Google Scholar
• Hatanaka T, Chopra N, Fujita M, et al. Passivity-based control and estimation in networked robotics. Cham: Springer; 2015.
   Google Scholar
• Kanda T, Hirano T, Eaton D, et al. Interactive robots as social partners and peer tutors for children: A field trial. Human–Comput Interact. 2004;19(1-2):61–84.
   Web of Science ®Google Scholar
• Zeng Z, Chen PJ, Lew AA. From high-touch to high-tech: COVID-19 drives robotics adoption. Tourism Geograph. 2020;22(3):724–734.
   Web of Science ®Google Scholar
• Taniguchi T, Hafi L, Hagiwara Y, et al. Semiotically Adaptive cognition: toward the realization of remotely-operated service robots for the new normal symbiotic society. Advanced Robotics; 2021.
   Google Scholar
• Khalil HK. Nonlinear systems. Hoboken (NJ): Prentice Hall; 2002.
   Google Scholar