Impact of Vaccination Opinion Dynamics on Influenza Epidemics

The aim of this study is to test the hypothesis that the dynamics of social opinion about vaccination influence the development of the epidemic process, using an influenza epidemic as an example. It is assumed that the opinion dynamics is completed before the start of the seasonal influenza outbreak, by which time everyone has made a decision about vaccination. Such decisions influence the formation of personal immune status for each economic agent as well as the formation of collective immunity in the population as a whole. The epidemic process is studied using two modifications of compartmental models (SIR and SEIRD). The cost-effectiveness of vaccination is also studied. The modeling is accompanied by a simulation of opinion dynamics and the epidemic process, implemented on a network model based on a random graph. A scenario analysis is performed based on statistical data on influenza incidence and annual vaccination campaigns in Russia.

Keywords

vaccination; influenza epidemic; network model; opinion dynamics; compartmental models.

References

1. Khasanova R.R. Morbidity and Mortality of the Russian Population from Influenza in 2008-2019. Economic Development of Russia, 2020, vol. 27, no. 4, pp. 88-92.
2. The Web-Site of A.A. Smorodintsev Influenza Research Institute. Available at: https://www.influenza.spb.ru (accessed: 15.02.2025)
3. Briko N., Feldblyum I. The Modern Concept of Development of Vaccine Prevention in Russia. Epidemiology and Vaccinal Prevention, 2019, vol. 18, no. 5, pp. 4-13. DOI: 10.31631/2073-3046-2019-18-5-4-13
4. Wolff G.G. Influenza Vaccination and Respiratory Virus Interference among Department of Defense Personnel During the 2017-2018 Influenza Season. Vaccine, 2020, vol. 38, no. 2, pp. 350-354. DOI: 10.1016/j.vaccine.2019.10.005
5. Sandmann F.G., van Leeuwen E., Bernard-Stoecklin S. et al. Health and Economic Impact of Seasonal Influenza Mass Vaccination Strategies in European Settings: A Mathematical Modeling and Cost-Effectiveness Analysis. Vaccine, 2022, vol. 40, pp. 1306-1315. DOI: 10.1016/j.vaccine.2022.01.015
6. Bubeev Yu.A., Vladimirskiy B.M., Ushakov I.B., Usov V.M., Bogomolov A.V. Mathematical Modelling of Spread COVID-19 Epidemic for Preventive Measures to Protect Life and Health of Elderly. Bulletin of the South Ural State University. Series: Mathematical Modelling, Programming and Computer Software, 2021, vol. 14, no. 3, pp. 92-98. DOI: 10.14529/mmp210307
7. Saperkin N., Kukunova V. Vaccination Issues and Internet Space. Medical Almanah, 2013, vol. 2, no. 26, pp. 75-78.
8. Zakharov V.V., Ndiaye S.M. Two Epidemic Models of Malaria and Their Practical Applications. Doklady Mathematics, 2023, vol. 15, no. 2, pp. 33-52. DOI: 10.1134/S1064562423600811
9. Menda K., Laird L., Kochenderfer M.J., et al. Explaining COVID-19 Outbreaks with Reactive Seird Models. Scientific Reports, 2021, vol. 11, article ID: 17905, 12 p. DOI: 10.1038/s41598-021-97260-0
10. Kumacheva S., Zhitkova E., Tomilina G. Attitudes to Vaccination: How the Opinion Dynamics Affects the Influenza Epidemic Process. Second International Conference (MSBC 2022) Proceedings, Communications in Computer and Information Science (CCIS), 2023, vol. 1717, pp. 63-77. DOI: 10.1007/978-3-031-33728-4
11. Kumacheva S., Zhitkova E., Tomilina G. Impact of Public Attitudes Towards Vaccination on Influenza Epidemic Dynamics. Third International Conference (MSBC 2024) Proceedings. Communications in Computer and Information Science, 2024, vol. 2211, pp. 17-31. DOI: 10.1007/978-3-031-72260-8-2
12. Budyansky A.V. Impact of Directed Migration on the Incidence of the Population in the SIS Model. Bulletin of the South Ural State University. Series: Mathematical Modelling, Programming and Computer Software, 2024, vol. 17, no. 3, pp. 18-28. DOI: 10.14529/mmp240302
13. Kermack W.O., McKendrick A.G. Contributions to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 1927, vol. 115, no. 772, pp. 700-721. DOI: 10.1098/rspa.1927.0118
14. De Groot M.H. Reaching a Consensus. Journal of the American Statistical Association, 1974, vol. 69, no. 345, pp. 118-121. DOI: 10.1080/01621459.1974.10480137
15. Nekovee A., Moreno Y., Bianconi G., Marsili M. Theory of Rumor Spreading in Complex Social Networks. Physica A, 2007, vol. 374, pp. 457-470. DOI: 10.1016/j.physa.2006.07.017
16. Shujing Gao, Zhidong Teng, Juan J Nieto, Torres A. Analysis of an SIR Epidemic Model with Pulse Vaccination and Distributed Time Delay. Journal of Biomedicine and Biotechnology, 2007, article ID: 64870, 10 p. DOI: 10.1155/2007/64870
17. Kolesin I., Gubar E., Zhitkova E. Management Strategies in Medical and Social Systems. St. Petersburg, Publisher SPbGU, 2014.
18. The Web-Site of the Russian Federation State Statistics Service. Available at: http://www.gks.ru (accessed: 15.02.2025)