A Survey on Two Viruses Extensions of Epidemic Model with Continuous and Impulse Control

Authors

DOI:

https://doi.org/10.21638/11701/spbu31.2021.12

Abstract

The current study represents a survey on several modifications of compartment epidemic models with continuous and impulse control policies. The main contribution of the survey is the modification of the classical Susceptible Infected Recovered (SIR) model with the assumption that two types of viruses are circulating in the population at the same time. Moreover, we also take into consideration the network structure of the initial population in two-virus SIIR models and estimate the effectiveness of protection measures over complex networks. In each model, the optimal control problem has been formalized to minimize the costs of the virus spreading and find optimal continuous and impulse antivirus controllers. All theoretical results are corroborated by a large number of numerical simulations.

Keywords:

epidemic process, compartment epidemic models, SIR model, optimal control, evolutionary games, virus mutation, structured population

Downloads

Download data is not yet available.
 

References

Agur, Z. et al. (1993). Pulse mass measles vaccination across age cohorts. Proceedings of the National Academy of Sciences of the United States of America, 90, 11698–11702

Altman, E. et al. (2010). Dispatch then stop: Optimal dissemination of security patches in mobile wireless networks. In Proceedings of the 48th IEEE Conference on Decisions and Control (CDC). pp. 2354–2359

Balter, M. (1998). New HIV strain could pose health threat. Science, 281(5382), 1425–1426

Barabási, A. L. and R. Albert (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512

Barabási, A.-L. and R. Albert (2002). Statistical mechanics of complex networks. Rev. Mod. Phys., 74, 47

Behncke, H. (2000). Optimal control of deterministic epidemics. Optimimal Control Appl. Methods, 21, 269–285

Blaquière, A. (1985). Impulsive Optimal Control with Finite or Infinite Time Horizon. Journal Of Optimization Theory And Applications. Vol. 46

Bomze, I. and S. Rota Bulò (2011). Infection and immunization: A new class of evolutionary game dynamics. Games Econ. Behav, 71, 193–211

Bonhoeffer, S. et al. (1997). Virus dynamics and drug therapy. Proc. Natl. Acad. Sci. USA, 94, 6971–6976

Butler, D. (2012). Flu surveillance lacking. Nature, 483, 520–522

Capasso, V. (1993). Mathematical Structures of Epidemic Systems; Lecture Notes in Biomathematics; Springer: Berlin/Heidelberg, Germany. Vol. 97

Castillo-Chavez, C. et al. (1996). Competitive exclusion in gonorrhea models and other sexually transmitted diseases. SIAM J. on App. Math., 56, 2, 494–508

Chahim, M. et al. (2012). A tutorial on the deterministic Impulse Control Maximum Principle: Necessary and sufficient optimality conditions. European Journal of Operational Research, 219, 18–26

Chowell, G. et al. (2017). Perspectives on model forecasts of the 2014–2015 Ebola epidemic in West Africa: Lessons and the way forward. BMC Med. Vol. 15, 42. doi:10.1186/s12916-017-0811-y

Conn, M. (2006). Handbook of Models for Human Aging; Academic Press: London, UK

Dykhta, V. A. and O. N. Samsonyuk (2009). A maximum principle for smooth optimal impulsive control problems with multipoint state constraints. Computational Mathematics and Mathematical Physics, 49, 942–957

Francis, P. J. (2004). Optimal tax/subsidy combinations for the flu season. J. Econ. Dyn. Control, 28, 2037–2054

Fu X. et al. (2008). Epidemic dynamics on scale-free networks with piecewise linear infectivity and immunization. Phys. Rev. E., 77, 3, 036113

Fu, F. et al. (2011). Imitation dynamics of vaccination behaviour on social networks. Proc. R. Soc. Lond. Ser. B., 278, 42-49

Gjorgjieva, J. et al. (2005). The role of vaccination in the control of SARS. Math. Biosci. Eng., 2, 753–769

Gubar, E. and Q. Zhu (2013). Optimal Control of Influenza Epidemic Model with Virus Mutations. Proceedings of the 12th Biannual European Control Conference, Zurich, Switzerland. IEEE Control Systems Society: New York, NY, USA, 2013; pp. 3125–3130

Gubar E. et al. (2015). Impact of Propagation Information in the Model of Tax Audit. Recent Advances in Game Theory and Applications. "Static and Dynamic Game Theory: Foundations and Applications". Switzerland. pp. 91–110

Kermack, W. O. and A. G. McKendrick (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A. Vol. 115, 772, pp. 700–721. The Royal Society, New York

Kharraz, A. et al. (2015). Cutting the gordian knot: A look under the hood of ransomware attacks. International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment. pp. 3–24. Springer

Khatri, S. et al. (2003). The Role of Network Topology on the Initial Growth Rate of Influenza Epidemic. Technical Report BU-1643-M; published by KeAi, China

Khouzani, M. H. R. et al. (2010). Maximum damage malware attack mobile wireless networks. Proceedings of the 29th International Conference on Computer Communications (INFOCOM), San Diego, CA, USA. pp. 749–757

Khouzani, M. H. R. et al. (2011). Optimal control of epidemic evolution. Proceedings of the 30th International Conference on Computer Communications (INFOCOM), Shanghai, China. pp. 1683–1691

Leander, R. et al. (2015). Optimal control of continuous systems with impulse controls. Optimal Control Applications and Methods Optim. Control Appl. Meth., 36: 535–549

Luo, X. and Q. Liao (2009). Ransomware: A new cyber hijacking threat to enterprises. Handbook of research on information security and assurance. 2009:1-6

Masuda, N. and N. Konno (2006). Multi-state epidemic processes on complex networks. J. Theor. Biol., 243, 1, 64–75

Newman, M. E. J. (2005). Threshold effects for two pathogens spreading on a network. Phys. Rev. Lett., 95, p. 108701

Newman, L. H. (2016). What we know about Friday's massive east coast Internet outage. Wired Magazine

Nuno, M. et al. (2005). Dynamics of two-strain influenza with isolation and partial cross-immunity. SIAM J. on App. Math., 65, 3, 964–982

Omic, J. et al. (2009). Protecting against network infections: A game theoretic perspective. Proceedings of the 28th IEEE Conference on Computer Communications (INFOCOM), Rio de Janeiro, Brazil

Pappas, G. J. et al. (2016). Analysis and Control of Epidemics: A Survey of Spreading Processes on Complex Networks, in IEEE Control Systems Magazine, 36(1), 26–46. doi: 10.1109/MCS.2015.2495000

Pastor-Satorras, R. and A. Vespignani (2001). Epidemic spreading in scale-free networks. Phys. Rev. Lett., 86, 14, 3200

Perkins, T. A. and G. Espana (2020). Optimal Control of the COVID-19 Pandemic with Non-pharmaceutical Interventions. Bulletin of mathematical biology 82(9), 118. https://doi.org/10.1007/s11538-020-00795-y

Pontryagin, L. S. (1987). Mathematical theory of optimal processes. CRC Press

Pragyan, D. et al. (2020). The Economic Effects of Covid-19 Containment Measures. IMF Working Paper WP/20/158

Rohloff, K. R. and T. Başar (2008). Deterministic and stochastic models for the detection of random constant scanning worms. ACM Trans. Model. Comput. Simul. (ACM TOMACS), 18, 1–24

Rowson, T. et al. (2020). How and When to End the COVID-19 Lockdown: an Optimization approach. Frontiers of Public Health, 10. https://doi.org/10.3389/fpubh.2020.00262

Rowthorn, R. and F. Toxvaerd (2020). The Optimal Control of Infectious Diseases via Prevention and Treatment. Technical Report 2013, Cambridge-INET Working Paper

Sethi, S. P. and G. L. Thompson (2006). Optimal Control Theory: Applications to Management Science and Economics. Springer, Berlin

Smith, G. et al. (2009). Origins and evolutionary genomics of the 2009 swine-origin H1N1 influenza A epidemic. Nature, 459, 7250, 1122–1125

Strogatz, S. H. (2001). Exploring complex networks. Nature, 410, 6825, 268–276

Taynitskiy, V., Gubar, E. and E. Zhitkova (2015). Structure of optimal control in the model of propagation of two malicious softwares. Proceedings of the International Conference “Stability and Control Processes” in Memory of V.I. Zubov (SCP), Saint-Petersburg, Russia. pp. 261–264

Taynitskiy, V., Gubar, E. and Q. Zhu (2016). Optimal Security Policy for Protection Against Heterogeneous Malware. Proceedings of the International Conference on “Network Games, Control and Optimization” (NETGCOOP 2016), Avignon, France. pp. 199–210

Taynitskiy, V., Gubar, E. and E. Zhitkova (2016). Optimization of protection of computer networks against malicious software. Proc. of International Conference "Stability and Oscillations of Nonlinear Control Systems" (Pyatnitskiy's Conference)

Taynitskiy, V., Gubar, E. and Q. Zhu (2017). Optimal Impulse Control of Bi-Virus SIR Epidemics with Application to Heterogeneous Internet of Things. Constructive Nonsmooth Analysis and Related Topics. Abstracts of the International Conference. Dedicated to the Memory of Professor V.F. Demyanov. pp. 113–116

Taynitskiy, V., Gubar, E. and Q. Zhu (2017). Optimal Control of Multi-strain Epidemic Processes in Complex Networks. Game Theory for Networks. GameNets 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Vol 212. Springer, Cham. pp. 108–117

Thomasey, D. H. and M. Martcheva (2008). Serotype replacement of vertically transmitted diseases through perfect vaccination. J. Biol. Sys., 16, 02, 255–277

Vespignani, A. et al. (2015). Epidemic processes in complex networks. Rev. Mod. Phys., 87, 925–979

Wickwire, K. H. (1975). A note on the optimal control of carrier-borne epidemics. J. Appl. Probab., 12, 565–346

Zaccour, G., Reddy, P. and S. Wrzaczek (2016). Quality effects in different advertising models - An impulse control approach. European Journal of Operational Research, 255, 984–995

Zhu, Q., Bushnell, L. and T. Başar (2012). Game-theoretic analysis of node capture and cloning attack with multiple attackers in wireless sensor networks. In Proceedings of the 51st IEEE Conference on Decision and Control (CDC'12), Maui, HI, USA

Downloads

Published

2021-10-30

How to Cite

Gubar, E., & Taynitskiy, V. (2021). A Survey on Two Viruses Extensions of Epidemic Model with Continuous and Impulse Control. Contributions to Game Theory and Management, 14, 127–154. https://doi.org/10.21638/11701/spbu31.2021.12

Issue

Section

Articles