SEIRS research map COVID-19 vaccines: analysis of trends, findings, and challenges of mathematical models

Authors

  • Muhamad Fahri Fauzan Universitas Islam Negeri Siber Syekh Nurjati Cirebon Author
  • Alya Ramadhani Universitas Islam Negeri Siber Syekh Nurjati Cirebon Author
  • Dara Maywanti Universitas Islam Negeri Siber Syekh Nurjati Cirebon Author
  • Aban Subanul Fahmi Universitas Islam Negeri Siber Syekh Nurjati Cirebon Author
  • Bayu Sukmaangara Universitas Islam Negeri Siber Syekh Nurjati Cirebon Author

Keywords:

SEIRS Model, COVID-19 Vaccination, Optimal Control, Fractional Order Model, Data Assimilation

Abstract

The control of the COVID-19 pandemic requires mathematical models that are able to accommodate complex immune dynamics, especially waning immunity phenomena and vaccination interventions. This study conducted a Systematic Literature Review using the PRISMA protocol to map trends, findings, and methodological challenges in SEIRS-Vaccination modeling. Of the 80 articles identified in the initial stage, 56 articles met the inclusion criteria and were analyzed. Furthermore, 11 articles were selected through purposive sampling techniques as representative samples for in-depth comparative analysis of five main methodology categories: Deterministic (ODE), Optimal Control, Fractional Order, Data Assimilation/Stochastic, and Spatial. The results of the literature synthesis reveal a significant paradigm shift from classical deterministic models that focused on stability analysis  static towards a more adaptive model. Specifically, this study identifies the use of the Ensemble Kalman Filter for estimation of dynamic parameters and Optimal Control Theory for resource allocation strategies as the dominant methodological trends. The model's findings consistently validate that vaccination rate is the most sensitive intervention parameter, but its long-term effectiveness is highly dependent on the duration of immunity. The study concludes the need to develop a hybrid model that integrates stochastic approaches and optimal control to generate more precise policy recommendations in the future.

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Author Biography

  • Bayu Sukmaangara, Universitas Islam Negeri Siber Syekh Nurjati Cirebon

    Mathematics Departments

References

[1] M. A. Misri, K. H. Qadr, and M. A. Rahmatullah, “SEIR Mathematical Model with the Use of Hand Sanitizers to Prevent the Spread of COVID-19 Disease,” CAUCHY J. Mat. Murni Dan Apl., vol. 9, no. 1, pp. 138–154, May 2024, doi: 10.18860/ca.v9i1.25754.

[2] Y. A. Terefe and T. A. Tegegn, “Contribution of the 2021 COVID-19 Vaccination Regime To COVID-19 Transmission and Control in South Africa: A Mathematical Modeling Perspective,” 2024, SSRN. doi: 10.2139/ssrn.5025247.

[3] Y. Hou and H. Bidkhori, “Multi-feature SEIR model for epidemic analysis and vaccine prioritization,” PLOS ONE, vol. 19, no. 3, p. e0298932, Mar. 2024, doi: 10.1371/journal.pone.0298932.

[4] Y. Hou and H. Bidkhori, “Multi-feature SEIR model for epidemic analysis and vaccine prioritization,” PLOS ONE, vol. 19, no. 3, p. e0298932, Mar. 2024, doi: 10.1371/journal.pone.0298932.

[5] N. Nadia et al., “Data-Driven Generating Operator in SEIRV Model for COVID-19 Transmission,” Commun. Biomath. Sci., vol. 6, no. 1, pp. 74–89, July 2023, doi: 10.5614/cbms.2023.6.1.6.

[6] N. Saidi and A. Radid, “Dynamics of a fractional order SEIRS epidemic model with vaccination and nonlinear incidence rates,” Sci. Afr., vol. 29, p. e02825, Sept. 2025, doi: 10.1016/j.sciaf.2025.e02825.

[7] M. S. P. Pramudito and B. P. Prawoto, “Model SEIR Penyakit COVID-19 dengan adanya Migrasi dan Pemberian Vaksin,” MATHunesa J. Ilm. Mat., vol. 9, no. 2, pp. 260–267, Aug. 2021, doi: 10.26740/mathunesa.v9n2.p260-267.

[8] Q. Sun, T. Miyoshi, and S. Richard, “Analysis of COVID-19 in Japan with extended SEIR model and ensemble Kalman filter,” J. Comput. Appl. Math., vol. 419, p. 114772, Feb. 2023, doi: 10.1016/j.cam.2022.114772.

[9] I. F. Y. Tello, A. V. Wouwer, and D. Coutinho, “State estimation of the time–space propagation of COVID-19 using a distributed parameter observer based on a SEIR-type model,” J. Process Control, vol. 118, pp. 231–241, Oct. 2022, doi: 10.1016/j.jprocont.2022.08.016.

[10] C. Bounkaicha and K. Allali, “Stability analysis of reaction–diffusion fractional-order SEIR model with vaccination and saturated incidence rate,” Partial Differ. Equ. Appl. Math., vol. 13, p. 101069, Mar. 2025, doi: 10.1016/j.padiff.2024.101069.

[11] A. El Alami Laaroussi, A. El Bhih, and M. Rachik, “Optimal vaccination and treatment policies with constrained inequalities to study limited vaccination resources for a multistrain reaction–diffusion S E I R model of COVID-19,” Partial Differ. Equ. Appl. Math., vol. 10, p. 100684, June 2024, doi: 10.1016/j.padiff.2024.100684.

[12] J. Ilnytskyi and T. Patsahan, “Compartmental and cellular automaton $SEIRS$ epidemiology models for the COVID-19 pandemic with the effects of temporal immunity and vaccination,” Dec. 07, 2021, arXiv: arXiv:2112.02661. doi: 10.48550/arXiv.2112.02661.

[13] R. C. Poonia, A. K. J. Saudagar, A. Altameem, M. Alkhathami, M. B. Khan, and M. H. A. Hasanat, “An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect,” Life, vol. 12, no. 5, p. 647, Apr. 2022, doi: 10.3390/life12050647.

[14] C. Xu, Y. Yu, G. Ren, Y. Sun, and X. Si, “Stability analysis and optimal control of a fractional-order generalized SEIR model for the COVID-19 pandemic,” Appl. Math. Comput., vol. 457, p. 128210, Nov. 2023, doi: 10.1016/j.amc.2023.128210.

[15] R. Pino, V. M. Mendoza, E. A. Enriquez, A. C. Velasco, and R. Mendoza, “An optimization model with simulation for optimal regional allocation of COVID-19 vaccines,” Healthc. Anal., vol. 4, p. 100244, Dec. 2023, doi: 10.1016/j.health.2023.100244.

[16] S. Arshad, S. Khalid, S. Javed, N. Amin, and F. Nawaz, “Modeling the impact of the vaccine on the COVID-19 epidemic transmission via fractional derivative,” Eur. Phys. J. Plus, vol. 137, no. 7, p. 802, July 2022, doi: 10.1140/epjp/s13360-022-02988-x.

[17] N. Nuraini, F. N. Soekotjo, A. Alifia, K. K. Sukandar, and B. W. Lestari, “Assessing potential surge of COVID-19 cases and the need for booster vaccine amid emerging SARS-CoV-2 variants in Indonesia: A modelling study from West Java,” Heliyon, vol. 9, no. 9, p. e20009, Sept. 2023, doi: 10.1016/j.heliyon.2023.e20009.

[18] N. B. Khedher, L. Kolsi, and H. Alsaif, “A multi-stage SEIR model to predict the potential of a new COVID-19 wave in KSA after lifting all travel restrictions,” Alex. Eng. J., vol. 60, no. 4, pp. 3965–3974, Aug. 2021, doi: 10.1016/j.aej.2021.02.058.

[19] G. González-Parra, M. S. Mahmud, and C. Kadelka, “Learning from the COVID-19 pandemic: A systematic review of mathematical vaccine prioritization models,” Infect. Dis. Model., vol. 9, no. 4, pp. 1057–1080, Dec. 2024, doi: 10.1016/j.idm.2024.05.005.

[20] O. Agossou, M. N. Atchadé, and A. M. Djibril, “Modeling the effects of preventive measures and vaccination on the COVID-19 spread in Benin Republic with optimal control,” Results Phys., vol. 31, p. 104969, Dec. 2021, doi: 10.1016/j.rinp.2021.104969.

[21] Y. H. Garcia, S. Diaz-Infante, and J. A. Minjarez-Sosa, “An integrated mathematical epidemiology and inventory model for high demand and limited supplies under uncertainty,” Decis. Anal. J., vol. 14, p. 100543, Mar. 2025, doi: 10.1016/j.dajour.2024.100543.

[22] S. Paul, A. Mahata, U. Ghosh, and B. Roy, “Study of SEIR epidemic model and scenario analysis of COVID-19 pandemic,” Ecol. Genet. Genomics, vol. 19, p. 100087, May 2021, doi: 10.1016/j.egg.2021.100087.

[23] P. Wintachai and K. Prathom, “Stability analysis of SEIR model related to efficiency of vaccines for COVID-19 situation,” Heliyon, vol. 7, no. 4, p. e06812, Apr. 2021, doi: 10.1016/j.heliyon.2021.e06812.

[24] C. Zuo, F. Zhu, and Y. Ling, “Analyzing COVID-19 Vaccination Behavior Using an SEIRM/V Epidemic Model With Awareness Decay,” Front. Public Health, vol. 10, p. 817749, Jan. 2022, doi: 10.3389/fpubh.2022.817749.

[25] M. Manaqib, M. Mahmudi, and R. A. Salsadilla, “Model Matematika COVID-19 dengan Vaksinasi Dua Tahap, Karantina, dan Pengobatan Mandiri,” Limits J. Math. Its Appl., vol. 20, no. 3, p. 255, Nov. 2023, doi: 10.12962/limits.v20i3.14310.

[26] R. C. Poonia, A. K. J. Saudagar, A. Altameem, M. Alkhathami, M. B. Khan, and M. H. A. Hasanat, “An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect,” Life, vol. 12, no. 5, p. 647, Apr. 2022, doi: 10.3390/life12050647.

[27] V. E. Papageorgiou and G. Tsaklidis, “A stochastic particle extended SEIRS model with repeated vaccination: Application to real data of COVID‐19 in Italy,” Math. Methods Appl. Sci., vol. 47, no. 7, pp. 6504–6538, May 2024, doi: 10.1002/mma.9934.

[28] M. E. Baroudi, H. Laarabi, S. Zouhri, M. Rachik, and A. Abta, “Optimal control problem for COVID-19 with multiple time-delays in state and control,” Results Control Optim., vol. 19, p. 100579, June 2025, doi: 10.1016/j.rico.2025.100579.

[29] A. Abbes, A. Ouannas, N. Shawagfeh, and G. Grassi, “The effect of the Caputo fractional difference operator on a new discrete COVID-19 model,” Results Phys., vol. 39, p. 105797, Aug. 2022, doi: 10.1016/j.rinp.2022.105797.

[30] K. Joshi, E. Rumpler, L. Kennedy-Shaffer, R. Bosan, and M. Lipsitch, “Comparative performance of between-population vaccine allocation strategies with applications for emerging pandemics,” Vaccine, vol. 41, no. 11, pp. 1864–1874, Mar. 2023, doi: 10.1016/j.vaccine.2022.12.053.

[31] A. I. Borovkov, M. V. Bolsunovskaya, A. M. Gintciak, V. V. Rakova, M. O. Efremova, and R. B. Akbarov, “COVID-19 Spread Modeling Considering Vaccination and Re-Morbidity,” Int. J. Technol., vol. 13, no. 7, p. 1463, Dec. 2022, doi: 10.14716/ijtech.v13i7.6186.

[32] S. Margenov, N. Popivanov, I. Ugrinova, and T. Hristov, “Differential and Time-Discrete SEIRS Models with Vaccination: Local Stability, Validation and Sensitivity Analysis Using Bulgarian COVID-19 Data,” Mathematics, vol. 11, no. 10, p. 2238, May 2023, doi: 10.3390/math11102238.

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Published

2025-12-19

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How to Cite

[1]
“SEIRS research map COVID-19 vaccines: analysis of trends, findings, and challenges of mathematical models”, Perspect. Math. Appl., vol. 1, no. 02, pp. 34–45, Dec. 2025, Accessed: Dec. 27, 2025. [Online]. Available: https://jurnal.kreasipustaka.com/index.php/permata/article/view/25

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