Discrete-Time Sequential Persuasive Game for Containing SIS Epidemic Propagation

References

• Abernathy E, Barskey A, Beall B, Bell BP, Bellini W, Bialek S, Blanton L, et al. (2012) Manual for the Surveillance of Vaccine-Preventable Diseases: 2012 (Centers for Disease Control and Prevention, Atlanta).Google Scholar
• Acemoglu D, Makhdoumi A, Malekian A, Ozdaglar A (2024) Testing, voluntary social distancing, and the spread of an infection. Oper. Res. 72(2):533–548.Link, Google Scholar
• Agarwal P, Hunt K, Jose E, Zhuang J (2024) Shutdown and compliance decisions in the face of a viral pandemic: A game between governments and citizens. Decision Support Systems 178:114128.Crossref, Google Scholar
• Al Jazeera (2020) Australia’s Victoria imposes fines to enforce COVID-19 isolation. Accessed April 10, 2026, https://www.aljazeera.com/news/2020/8/4/australias-victoria-imposes-fines-to-enforce-covid-19-isolation.Google Scholar
• Bergemann D, Morris S (2019) Information design: A unified perspective. J. Econom. Literature 57(1):44–95.Crossref, Google Scholar
• Brauer F, Castillo-Chavez C, Feng Z, et al. (2019) Mathematical Models in Epidemiology, vol. 32 (Springer, New York).Crossref, Google Scholar
• Brunham RC, Nagelkerke NJ, Plummer FA, Moses S (1994) Estimating the basic reproductive rates of Neisseria gonorrhoeae and Chlamydia trachomatis: The implications of acquired immunity. Sexually Transmitted Diseases 21(6):353–356.Crossref, Google Scholar
• Candogan O, Gurkan H (2025) The value of information design in supply chain management. Management Sci. 71(8):6545–6558.Link, Google Scholar
• Chang M, Makhdoumi A, Sun P (2023) Controlling epidemics: The value of information design. Preprint, submitted May 31, https://doi.org/10.2139/ssrn.4460475.Google Scholar
• Chen Y, Zhang J (2020) Signalling by Bayesian persuasion and pricing strategy. Econom. J. 130(628):976–1007.Google Scholar
• Christensen H, Hickman M, Edmunds WJ, Trotter CL (2013) Introducing vaccination against serogroup b meningococcal disease: An economic and mathematical modelling study of potential impact. Vaccine 31(23):2638–2646.Crossref, Google Scholar
• De Véricourt F, Gurkan H, Wang S (2021) Informing the public about a pandemic. Management Sci. 67(10):6350–6357.Link, Google Scholar
• Drakopoulos K, Jain S, Randhawa R (2021) Persuading customers to buy early: The value of personalized information provisioning. Management Sci. 67(2):828–853.Link, Google Scholar
• Fitch JT (2001) Are condoms effective in reducing the risk of sexually transmitted disease? Ann. Pharmacotherapy 35(9):1136–1138.Crossref, Google Scholar
• Gao Z, Xu Y, Sun C, Wang X, Guo Y, Qiu S, Ma K (2021) A systematic review of asymptomatic infections with COVID-19. J. Microbiology Immunology Infection 54(1):12–16.Crossref, Google Scholar
• Hethcote HW (2000) The mathematics of infectious diseases. SIAM Rev. 42(4):599–653.Crossref, Google Scholar
• Hota AR, Satapathi A, Maitra U (2023a) Bayesian persuasion for containing SIS epidemic with asymptomatic infection. Preprint, submitted December 7, https://arxiv.org/abs/2312.04182.Google Scholar
• Hota AR, Maitra U, Elokda E, Bolognani S (2023b) Learning to mitigate epidemic risks: A dynamic population game approach. Dynamic Games Appl. 13(4):1106–1129.Crossref, Google Scholar
• Huang Y, Zhu Q (2022) Game-theoretic frameworks for epidemic spreading and human decision-making: A review. Dynamic Games Appl. 12(1):7–48.Crossref, Google Scholar
• Kamenica E, Gentzkow M (2011) Bayesian persuasion. Amer. Econom. Rev. 101(6):2590–2615.Crossref, Google Scholar
• Khazaei H, Paarporn K, Garcia A, Eksin C (2021) Disease spread coupled with evolutionary social distancing dynamics can lead to growing oscillations. Proc. 60th IEEE Conf. Decision Control (IEEE, Piscataway, NJ), 4280–4286.Google Scholar
• Lai A, Poon C, Cheung A (2012) Effectiveness of facemasks to reduce exposure hazards for airborne infections among general populations. J. Roy. Soc. Interface 9(70):938–948.Crossref, Google Scholar
• Laurière M, Perrin S, Pérolat J, Girgin S, Muller P, Élie R, Geist M, Pietquin O (2022) Learning in mean field games: A survey. Preprint, submitted May 25, https://arxiv.org/abs/2205.12944.Google Scholar
• Levitt EE, Gohari MR, Syan SK, Belisario K, Gillard J, DeJesus J, Levitt A, MacKillop J (2022) Public health guideline compliance and perceived government effectiveness during the COVID-19 pandemic in Canada: Findings from a longitudinal cohort study. Lancet Regional Health Amer. 9:100185.Google Scholar
• Levy AG, Thorpe A, Scherer LD, Scherer AM, Drews FA, Butler JM, Burpo N, Shoemaker H, Stevens V, Fagerlin A (2022) Misrepresentation and nonadherence regarding COVID-19 public health measures. JAMA Network Open 5(10):e2235837.Crossref, Google Scholar
• Lewis D (2021) Contact-tracing apps help to reduce COVID infections. Nature 591(7848):18–19.Google Scholar
• Lin Y, Li W, Zha H, Wang B (2023) Information design in multi-agent reinforcement learning. Adv. Neural Inform. Processing Systems, vol. 36 (Curran Associates, Inc., Red Hook, NY), 25584–25597.Google Scholar
• Lingenbrink D, Iyer K (2019) Optimal signaling mechanisms in unobservable queues. Oper. Res. 67(5):1397–1416.Link, Google Scholar
• López-Pintado D (2008) Diffusion in complex social networks. Games Econom. Behav. 62(2):573–590.Crossref, Google Scholar
• Maitra U, Hota AR, Paré PE (2024) Optimal Bayesian persuasion for containing SIS epidemics. IEEE Control Systems Lett. 8:2499–2504.Crossref, Google Scholar
• Maitra U, Hota AR, Srivastava V (2023) SIS epidemic propagation under strategic non-myopic protection: A dynamic population game approach. IEEE Control Systems Lett. 7:1578–1583.Crossref, Google Scholar
• Nagurney A (2021) Game theory and the COVID-19 pandemic. Tutorials in Operations Research: Emerging Optimization Methods and Modeling Techniques with Applications (INFORMS, Cantonsville, MD), 83–130.Link, Google Scholar
• NBC (2021) Sydney lockdown fines hiked to $3,700 as Australia faces ‘worst’ COVID situation. NBC News. Accessed April 10, 2026, https://www.nbcnews.com/news/world/sydney-lockdown-fines-hiked-3-700-australia-faces-worst-covid-n1276824.Google Scholar
• Olmez SY, Aggarwal S, Kim JW, Miehling E, Başar T, West M, Mehta PG (2022) Modeling presymptomatic spread in epidemics via mean-field games. Proc. Amer. Control Conference (IEEE, Piscataway, NJ), 3648–3655.Google Scholar
• Pandit JA, Radin JM, Quer G, Topol EJ (2022) Smartphone apps in the COVID-19 pandemic. Nature Biotechnology 40(7):1013–1022.Crossref, Google Scholar
• Pariser H (1972) Asymptomatic gonorrhea. Medical Clinics North Amer. 56(5):1127–1132.Crossref, Google Scholar
• Pathak S, Kulkarni AA (2025) A scalable Bayesian persuasion framework for epidemic containment on heterogeneous networks. J. Math. Econom. 119:103134.Crossref, Google Scholar
• Peng C, Tomizuka M (2019) Bayesian persuasive driving. Proc. Amer. Control Conf. (IEEE, Piscataway, NJ), 723–729.Google Scholar
• Portnoy J, Mendelson J, Clecner B, Heisler L (1974) Asymptomatic gonorrhea in the male. Canadian Medical Assoc. J. 110(2):169.Google Scholar
• Sandholm WH (2010) Population Games and Evolutionary Dynamics (MIT Press, Cambridge, MA).Google Scholar
• Satapathi A, Dhar NK, Hota AR, Srivastava V (2024) Coupled evolutionary behavioral and disease dynamics under reinfection risk. IEEE Trans. Control Network Systems 11(2):795–807.Crossref, Google Scholar
• Sauer T, Berry T, Ebeigbe D, Norton MM, Whalen AJ, Schiff SJ (2021) Identifiability of infection model parameters early in an epidemic. SIAM J. Control Optim. 60(2):S27–S48.Crossref, Google Scholar
• Sayin MO, Başar T (2021) Bayesian persuasion with state-dependent quadratic cost measures. IEEE Trans. Automatic Control 67(3):1241–1252.Crossref, Google Scholar
• Sellke SH, Shroff NB, Bagchi S (2008) Modeling and automated containment of worms. IEEE Trans. Dependable Secure Comput. 5(2):71–86.Crossref, Google Scholar
• Shah S, Amin S, Jaillet P (2023) Information design for hybrid work under infectious disease transmission risk. Preprint, submitted December 7, https://arxiv.org/abs/2312.04073.Google Scholar
• Shin J, Wang CY (2024) The role of messenger in advertising content: Bayesian persuasion perspective. Marketing Sci. 43(4):840–862.Link, Google Scholar
• Urbán R, Paksi B, Miklósi Á, Saunders JB, Demetrovics Z (2021) Non-adherence to preventive behaviours during the COVID-19 epidemic: Findings from a community study. BMC Public Health 21(1):1462.Crossref, Google Scholar
• Wan Q, Xu X, Hunt K, Zhuang J (2022) Stay home or not? Modeling individuals’ decisions during the COVID-19 pandemic. Decision Anal. 19(4):319–336.Link, Google Scholar
• Williams SN, Armitage CJ, Tampe T, Dienes KA (2021) Public perceptions of non-adherence to pandemic protection measures by self and others: A study of COVID-19 in the United Kingdom. PLoS One 16(10):e0258781.Crossref, Google Scholar
• Wu J, Zhang Z, Feng Z, Wang Z, Yang Z, Jordan MI, Xu H (2022) Sequential information design: Markov persuasion process and its efficient reinforcement learning. Preprint, submitted February 22, https://arxiv.org/abs/2202.10678.Google Scholar
• Xue L, Jing S, Miller JC, Sun W, Li H, Estrada-Franco JG, Hyman JM, Zhu H (2020) A data-driven network model for the emerging COVID-19 epidemics in Wuhan, Toronto and Italy. Math. Biosciences 326:108391.Crossref, Google Scholar
• Yang Z, Krishnan R, Li B (2024) The interplay between individual mobility, health risk, and economic choice: A holistic model for COVID-19 policy intervention. INFORMS J. Data Sci. 3(1):6–27.Link, Google Scholar
• Ye M, Zino L, Rizzo A, Cao M (2021) Game-theoretic modeling of collective decision making during epidemics. Phys. Rev. E 104(2):024314.Crossref, Google Scholar
• Zhang K, Yang Z, Başar T (2021) Multi-agent reinforcement learning: A selective overview of theories and algorithms. Vamvoudakis KG, Wan Y, Lewis FL, Cansever D, eds. Handbook of Reinforcement Learning and Control (Springer, Cham, Switzerland), 321–384.Crossref, Google Scholar
• Zhu Y, Savla K (2018) On the stability of optimal Bayesian persuasion strategy under a mistrust dynamics in routing games. Proc. 56th Annual Allerton Conf. Comm. Control Comput. (IEEE, Piscataway, NJ), 92–99.Google Scholar