Research article Special Issues

Mathematical modeling of infectious diseases and the impact of vaccination strategies

  • Received: 26 June 2024 Revised: 20 August 2024 Accepted: 06 September 2024 Published: 19 September 2024
  • Mathematical modeling plays a crucial role in understanding and combating infectious diseases, offering predictive insights into disease spread and the impact of vaccination strategies. This paper explored the significance of mathematical modeling in epidemic control efforts, focusing on the interplay between vaccination strategies, disease transmission rates, and population immunity. To facilitate meaningful comparisons of vaccination strategies, we maintained a consistent framework by fixing the vaccination capacity to vary from 10 to 100% of the total population. As an example, at a 50% vaccination capacity, the pulse strategy averted approximately 45.61% of deaths, while continuous and hybrid strategies averted around 45.18 and 45.69%, respectively. Sensitivity analysis further indicated that continuous vaccination has a more direct impact on reducing the basic reproduction number $ R_0 $ compared to pulse vaccination. By analyzing key parameters such as $ R_0 $, pulse vaccination coefficients, and continuous vaccination parameters, the study underscores the value of mathematical modeling in shaping public health policies and guiding decision-making during disease outbreaks.

    Citation: Diana Bolatova, Shirali Kadyrov, Ardak Kashkynbayev. Mathematical modeling of infectious diseases and the impact of vaccination strategies[J]. Mathematical Biosciences and Engineering, 2024, 21(9): 7103-7123. doi: 10.3934/mbe.2024314

    Related Papers:

  • Mathematical modeling plays a crucial role in understanding and combating infectious diseases, offering predictive insights into disease spread and the impact of vaccination strategies. This paper explored the significance of mathematical modeling in epidemic control efforts, focusing on the interplay between vaccination strategies, disease transmission rates, and population immunity. To facilitate meaningful comparisons of vaccination strategies, we maintained a consistent framework by fixing the vaccination capacity to vary from 10 to 100% of the total population. As an example, at a 50% vaccination capacity, the pulse strategy averted approximately 45.61% of deaths, while continuous and hybrid strategies averted around 45.18 and 45.69%, respectively. Sensitivity analysis further indicated that continuous vaccination has a more direct impact on reducing the basic reproduction number $ R_0 $ compared to pulse vaccination. By analyzing key parameters such as $ R_0 $, pulse vaccination coefficients, and continuous vaccination parameters, the study underscores the value of mathematical modeling in shaping public health policies and guiding decision-making during disease outbreaks.



    加载中


    [1] W. O. Kermack, A. G. Mckendrick, A contribution to the mathematical theory of epidemics, Proc. R. Soc. Math. Phys. Eng. Sci., 115 (1927), 700–721. https://doi.org/10.1098/rspa.1927.0118 doi: 10.1098/rspa.1927.0118
    [2] A. L. Lloyd, S. Valeika, Network models in epidemiology: an overview, in Complex Population Dynamics, World Scientific, (2007), 189–214. https://doi.org/10.1142/9789812771582_0008
    [3] W. M. Getz, R. M. Salter, L. L. Vissat, Simulation platforms to support teaching and research in epidemiological dynamics, preprint, medRxiv: 2022.02.09.22270752.
    [4] G. Huang, Y. Takeuchi, W. Ma, D. Wei, Global stability for delay SIR and SEIR epidemic models with nonlinear incidence rate, Bull. Math. Biol., 72 (2010), 1192–1207. https://doi.org/10.1007/s11538-009-9487-6 doi: 10.1007/s11538-009-9487-6
    [5] K. Hattaf, A. A. Lashari, Y. Louartassi, N. Yousfi, A delayed SIR epidemic model with a general incidence rate, Electron. J. Qual. Theory Differ. Equations, 2013 (2013), 1–9. http://dx.doi.org/10.14232/ejqtde.2013.1.3 doi: 10.14232/ejqtde.2013.1.3
    [6] M. Naim, Y. Sabbar, M. Zahri, B. Ghanbari, A. Zeb, N. Gul, et al., The impact of dual time delay and Caputo fractional derivative on the long-run behavior of a viral system with the non-cytolytic immune hypothesis, Phys. Scr., 97 (2022). https://doi.org/10.1088/1402-4896/ac9e7a
    [7] I. Al-Darabsah, A time-delayed SVEIR model for imperfect vaccine with a generalized nonmonotone incidence and application to measles, Appl. Math. Modell., 91 (2020), 74–92. https://doi.org/10.1016/j.apm.2020.08.084 doi: 10.1016/j.apm.2020.08.084
    [8] U. Ghosh, S. Chowdhury, D. K. Khan, W. Bengal, Mathematical modelling of epidemiology in presence of vaccination and delay, Comput. Sci. Inf. Technol., 2013 (2013), 91–98. http://dx.doi.org/10.5121/csit.2013.3209 doi: 10.5121/csit.2013.3209
    [9] K. E. Church, X. Liu, Analysis of a SIR model with pulse vaccination and temporary immunity: Stability, bifurcation and a cylindrical attractor, Nonlinear Anal. Real World Appl., 50 (2019), 240–266. https://doi.org/10.1016/j.nonrwa.2019.04.015 doi: 10.1016/j.nonrwa.2019.04.015
    [10] A. Kashkynbayev, D. Koptleuova, Global dynamics of tick-borne diseases, Math. Biosci. Eng., 17 (2020), 4064–4079. https://doi.org/10.3934/mbe.2020225 doi: 10.3934/mbe.2020225
    [11] A. Kashkynbayev, F. A. Rihan, Dynamics of fractional-order epidemic models with general nonlinear incidence rate and time-delay, Mathematics, 9 (2021), 1829. https://doi.org/10.3390/math9151829 doi: 10.3390/math9151829
    [12] A. Sow, C. Diallo, H. Cherifi, Interplay between vaccines and treatment for dengue control: An epidemic model, Plos One, 19 (2024), e0295025. https://doi.org/10.1371/journal.pone.0295025 doi: 10.1371/journal.pone.0295025
    [13] S. Gao, L. Chen, Z. Teng, Pulse vaccination of an SEIR epidemic model with time delay, Nonlinear Anal. Real World Appl., 9 (2008), 599–607. https://doi.org/10.1016/j.nonrwa.2006.12.004 doi: 10.1016/j.nonrwa.2006.12.004
    [14] W. Wei, M. Li, Pulse vaccination strategy in the SEIR epidemic dynamics model with latent period, in 2010 2nd International Conference on Information Engineering and Computer Science, (2010), 1–4. https://doi.org/10.1109/ICIECS.2010.5677692
    [15] G. Bolarin, O. M. Bamigbola, Pulse vaccination strategy in a SVEIRS epidemic model with two-time delay and saturated incidence, Univ. J. Appl. Math., 2014 (2014). http://dx.doi.org/10.13189/ujam.2014.020505
    [16] D. J. Nokes, J. Swinton, Vaccination in pulses: a strategy for global eradication of measles and polio?, Trends Microbiol., 5 (1997), 14–19. https://doi.org/10.1016/s0966-842x(97)81769-6 doi: 10.1016/s0966-842x(97)81769-6
    [17] O. Diekmann, J. A. P. Heesterbeek, J. A. J. Metz, On the definition and the computation of the basic reproduction ratio $R_0$ in models for infectious diseases in heterogeneous populations, J. Math. Biol., 28 (1990), 365–382. http://dx.doi.org/10.1007/BF00178324 doi: 10.1007/BF00178324
    [18] H. R. Thieme Global asymptotic stability in epidemic models, in Equadiff 82: Proceedings of the international conference held in Würzburg, (2006), 608–615. http://dx.doi.org/10.1007/BFb0103284
    [19] S. Gao, L. Chen, Z. Teng, Impulsive vaccination of an SEIRS model with time delay and varying total population size, Bull. Math. Biol., 69 (2007), 731–745. https://doi.org/10.1007/s11538-006-9149-x doi: 10.1007/s11538-006-9149-x
    [20] Z. Bai, Threshold dynamics of a time-delayed SEIRS model with pulse vaccination, Math. Biosci., 269 (2015), 178–185. https://doi.org/10.1016/j.mbs.2015.09.005 doi: 10.1016/j.mbs.2015.09.005
    [21] H. L. Smith, An introduction to delay differential equations with applications to the life sciences, Springer-Verlag, New York, 2011. https://doi.org/10.1007/978-1-4612-0873-0
    [22] A. Kashkynbayev, M. Yeleussinova, S. Kadyrov, An SIRS pulse vaccination model with nonlinear incidence rate and time delay, Lett. Biomath., 10 (2023), 133–148.
    [23] N. Bacaër, S. Guernaoui, The epidemic threshold of vector-borne diseases with seasonality: the case of cutaneous leishmaniasis in Chichaoua, Morocco J. Math. Biol., 53 (2006), 521–436. http://dx.doi.org/10.1007/s00285-006-0015-0
    [24] N. S. Al-Shimari, A. S. Al-Jilawi, Using second-order optimization algorithms approach for solving the numerical optimization problem with new software technique, in American Institute of Physics Conference Series, 2591 (2023), 050004. https://doi.org/10.1063/5.0119641
    [25] Z. Bai, Z. Ma, L. Jing, Y. Li, S. Wang, B. G. Wang, et al., Estimation and sensitivity analysis of a COVID-19 model considering the use of face mask and vaccination, Sci. Rep., 13 (2023), 6434. https://doi.org/10.1038/s41598-023-33499-z doi: 10.1038/s41598-023-33499-z
    [26] G. Albi, L. Pareschi, M. Zanella, Modelling lockdown measures in epidemic outbreaks using selective socio-economic containment with uncertainty, Math. Biosci. Eng., 18 (2021), 7161–7191. https://doi.org/10.3934/mbe.2021355 doi: 10.3934/mbe.2021355
    [27] G. Ledder, Incorporating mass vaccination into compartment models for infectious diseases, Math. Biosci. Eng., 19 (2022), 9457–9480. https://doi.org/10.3934/mbe.2022440 doi: 10.3934/mbe.2022440
    [28] H. B. Saydaliyev, S. Kadyrov, L. Chin Attitudes toward vaccination and its impact on economy, Int. J. Econ. Manage., 16 (2022). http://dx.doi.org/10.47836/ijeamsi.16.1.009
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(567) PDF downloads(72) Cited by(0)

Article outline

Figures and Tables

Figures(16)  /  Tables(4)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog