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The spread of an epidemic: a game-theoretic approach

Published online by Cambridge University Press:  31 January 2025

Sayar Karmakar*
Affiliation:
University of Florida
Moumanti Podder*
Affiliation:
Indian Institute of Science Education and Research Pune
Souvik Roy*
Affiliation:
Indian Statistical Institute, Kolkata
Soumyarup Sadhukhan*
Affiliation:
Indian Institute of Technology Kanpur
*
*Postal address: University of Florida, 230 Newell Drive, Gainesville, FL 32603, USA. Email address: [email protected]
**Postal address: Indian Institute of Science Education and Research Pune, Dr. Homi Bhabha Road, Pashan, Pune 411008, Maharashtra, India. Email address: [email protected]
***Postal address: Indian Statistical Institute, 203 Barrackpore Trunk Road, Kolkata 700108, West Bengal, India. Email address: [email protected]
****Postal address: Indian Institute of Technology, Kalyanpur, Kanpur, Uttar Pradesh 208016, India. Email address: [email protected]

Abstract

We introduce and study a game-theoretic model to understand the spread of an epidemic in a homogeneous population. A discrete-time stochastic process is considered where, in each epoch, first, a randomly chosen agent updates their action trying to maximize a proposed utility function, and then agents who have viral exposures beyond their immunity get infected. Our main results discuss asymptotic limiting distributions of both the cardinality of the subset of infected agents and the action profile, considered under various values of two parameters (initial action and immunity profile). We also show that the theoretical distributions are almost always achieved in the first few epochs.

Type
Original Article
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Applied Probability Trust

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References

Aron, J. L. and Schwartz, I. B. (1984). Seasonality and period-doubling bifurcations in an epidemic model. J. Theoret. Biol. 110, 665679.CrossRefGoogle Scholar
Aurell, A., Carmona, R., Dayankl, G. and Laurière, M. (2022). Finite state graphon games with applications to epidemics. Dynamic Games Appl. 12, 4981.CrossRefGoogle ScholarPubMed
Aurell, A., Carmona, R., Dayankl, G. and Laurière, M. (2022). Optimal incentives to mitigate epidemics: a Stackelberg mean field game approach. SIAM J. Control Optimization 60, S294S322.CrossRefGoogle Scholar
Azizi, A. et al. (2020). Epidemics on networks: reducing disease transmission using health emergency declarations and peer communication. Infectious Disease Modelling 5, 1222.CrossRefGoogle ScholarPubMed
Brauer, F. (2008). Compartmental models in epidemiology. In Mathematical Epidemiology, Springer, Berlin, Heidelberg, pp. 1979.CrossRefGoogle Scholar
Browne, C. A., Amchin, D. B., Schneider, J. and Datta, S. S. (2021). Infection percolation: a dynamic network model of disease spreading. Frontiers Phys. 9, article no. 645954.CrossRefGoogle Scholar
Cho, S. (2020). Mean-field game analysis of SIR model with social distancing. Preprint. Available at https://arxiv.org/abs/2005.06758.Google Scholar
Cooney, D. B. et al. (2022). Social dilemmas of sociality due to beneficial and costly contagion. PLoS Comput. Biol. 18, article no. e1010670.CrossRefGoogle Scholar
Craig, B. R., Phelan, T., Siedlarek, J.-P. and Steinberg, J. (2020). Improving epidemic modeling with networks. Econom. Commentary no. 2020-23.CrossRefGoogle Scholar
Cui, Y., Ni, S. and Shen, S. (2021). A network-based model to explore the role of testing in the epidemiological control of the COVID-19 pandemic. BMC Infectious Diseases 21, article no. 58.CrossRefGoogle ScholarPubMed
Doncel, J., Gast, N. and Gaujal, B. (2022). A mean field game analysis of SIR dynamics with vaccination. Prob. Eng. Inf. Sci. 36, 482499.CrossRefGoogle Scholar
Gaujal, B., Doncel, J. and Gast, N. (2021). Vaccination in a large population: mean field equilibrium versus social optimum. In Network Games, Control and Optimization: 10th International Conference, NetGCooP 2020, Springer, Cham, pp. 51–59.Google Scholar
Hethcote, H. W. (1973). Asymptotic behavior in a deterministic epidemic model. Bull. Math. Biol. 35, 607614.CrossRefGoogle Scholar
Hubert, E., Mastrolia, T., Possama, D. and Warin, X. (2022). Incentives, lockdown, and testing: from Thucydides’ analysis to the COVID-19 pandemic. J. Math. Biol. 84, article no. 37.CrossRefGoogle ScholarPubMed
Hubert, E. and Turinici, G. (2018). Nash-MFG equilibrium in a SIR model with time dependent newborn vaccination. Ric. Mat. 67, 227246.CrossRefGoogle Scholar
Keeling, M. J. and Eames, K. T. D. (2005). Networks and epidemic models. J. R. Soc. Interface 2, 295307.CrossRefGoogle ScholarPubMed
Kermack, W. O. and McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proc. R. Soc. London A 115, 700721.CrossRefGoogle Scholar
Laguzet, L. and Turinici, G. (2015). Individual vaccination as Nash equilibrium in a SIR model with application to the 2009–2010 Influenza A (H1N1) epidemic in France. Bull. Math. Biol. 77, 19551984.CrossRefGoogle Scholar
Laguzet, L., Turinici, G. and Yahiaoui, G. (2016). Equilibrium in an individual–societal SIR vaccination model in presence of discounting and finite vaccination capacity. In New Trends in Differential Equations, Control Theory and Optimization, World Scientific, Singapore, pp. 201214.CrossRefGoogle Scholar
Lahkar, R., Mukherjee, S. and Roy, S. (2022). Generalized perturbed best response dynamics with a continuum of strategies. J. Econom. Theory 200, article no. 105398.CrossRefGoogle Scholar
Lahkar, R., Mukherjee, S. and Roy, S. (2023). The logit dynamic in supermodular games with a continuum of strategies: a deterministic approximation approach. Games Econom. Behavior 139, 133160.CrossRefGoogle Scholar
Lahkar, R. and Riedel, F. (2015). The logit dynamic for games with continuous strategy sets. Games Econom. Behavior 91, 268282.CrossRefGoogle Scholar
Lang, J. C., De Sterck, H., Kaiser, J. L. and Miller, J. C. (2018). Analytic models for SIR disease spread on random spatial networks. J. Complex Networks 6, 948970.CrossRefGoogle Scholar
Lee, W. et al. (2021). Controlling propagation of epidemics via mean-field control. SIAM J. Appl. Math. 81, 190207.CrossRefGoogle Scholar
Lee, W., Liu, S., Li, W. and Osher, S. (2022). Mean field control problems for vaccine distribution. Res. Math. Sci. 9, article no. 51.CrossRefGoogle ScholarPubMed
Maheshwari, P. and Albert, R. (2020). Network model and analysis of the spread of Covid-19 with social distancing. Appl. Network Sci. 5, 113.CrossRefGoogle ScholarPubMed
Maschler, M., Zamir, S. and Solan, E. (2020). Game Theory. Cambridge University Press.CrossRefGoogle Scholar
Mukherjee, S. and Roy, S. (2021). Regularized Bayesian best response learning in finite games. Preprint. Available at https://arxiv.org/abs/2111.13687.Google Scholar
Mukherjee, S. and Roy, S. (2024). Perturbed Bayesian best response dynamic in continuum games. SIAM J. Control Optimization 62, 30913120.CrossRefGoogle Scholar
Newman, M. E. J. (2002). Spread of epidemic disease on networks. Phys. Rev. E 66, article no. 016128.CrossRefGoogle ScholarPubMed
Olmez, S. Y. et al. (2022). How does a rational agent act in an epidemic? In 2022 IEEE 61st Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers, Piscataway, NJ, pp. 5536–5543.Google Scholar
Olmez, S. Y. et al. (2022). Modeling presymptomatic spread in epidemics via mean-field games. In 2022 American Control Conference (ACC), Institute of Electrical and Electronics Engineers, Piscataway, NJ, pp. 36483655.CrossRefGoogle Scholar
Pastor-Satorras, R. et al. (2015). Epidemic processes in complex networks. Rev. Modern Phys. 87, article no. 925.CrossRefGoogle Scholar
Ross, R. and Hudson, H. P. (1917). An application of the theory of probabilities to the study of a priori pathometry. Proc. R. Soc. London A 93, 225240.CrossRefGoogle Scholar
Roy, A., Singh, C. and Narahari, Y. (2022). Recent advances in modeling and control of epidemics using a mean field approach. Preprint. Available at https://arxiv.org/abs/2208.14765.Google Scholar
Sah, P. et al. (2021). Revealing mechanisms of infectious disease spread through empirical contact networks. PLoS Comput. Biol. 17, article no. e1009604.CrossRefGoogle Scholar
Salvarani, F. and Turinici, G. (2018). Optimal individual strategies for influenza vaccines with imperfect efficacy and durability of protection. Math. Biosci. Eng. 15, 629652.CrossRefGoogle ScholarPubMed
Small, M. and Tse, C. K. (2010). Complex network models of disease propagation: modelling, predicting and assessing the transmission of SARS. Hong Kong Med. J. 16, 4344.Google ScholarPubMed
Vizuete, R., Frasca, P. and Garin, F. (2020). Graphon-based sensitivity analysis of SIS epidemics. IEEE Control Systems Lett. 4, 542547.CrossRefGoogle Scholar