Evolutionary Fuzzy Control of Pandemics with Vaccination and Isolation Constraints

[1] Z. Abbasi, M. Shafieirad, A. H. Amiri Mehra, I. Zamani, Optimized ANFIS-based control design using genetic
algorithm to obtain the vaccination and isolation rates for the COVID-19, in 2021 29th Iranian Conference on
Electrical Engineering, ICEE 2021, IEEE, (2021), 731-735. https://doi.org/10.1109/ICEE52715.2021.9544298

[2] A. Adhikary, M. Pal, R. Maiti, Impact of COVID-19 vaccinations in India: A state-wise analysis, BMC Public
Health, 25(1) (2025), 219. https://doi.org/10.1186/s12889-025-21401-7

[3] C. Anastassopoulou, L. Russo, A. Tsakris, C. Siettos, Data-based analysis, modelling and forecasting of the COVID-
19 outbreak, PLoS One, 15(3) (2020), e0230405. https://doi.org/10.1371/journal.pone.0230405

[4] R. Arghal, S. S. Bidokhti, S. Sarkar, Optimal capacity-constrained COVID-19 vaccination for heterogeneous populations,
Proceedings of the IEEE Conference on Decision and Control, (2022), 5620-5626. https://doi.org/10.
1109/CDC51059.2022.9992682

[5] E. Badfar, E. J. Zaferani, A. Nikoofard, Design a robust sliding mode controller based on the state and parameter
estimation for the nonlinear epidemiological model of Covid-19, Nonlinear Dynamics, 109(1) (2022), 5-18. https:
//doi.org/10.1007/s11071-021-07036-4

[6] C. A. Bojan-Dragos, R. E. Precup, E. M. Petriu, R. C. Roman, E. L. Hedrea, A. I. Szedlak-Stinean, GWO-based
optimal tuning of controllers for shape memory alloy wire actuators, IFAC-PapersOnLine, 55(15) (2022), 39-44.
https://doi.org/10.1016/j.ifacol.2022.07.605

[7] M. Borah, B. K. Roy, T. Kapitaniak, K. Rajagopal, C. Volos, A revisit to the past plague epidemic (India) versus the
present COVID-19 pandemic: Fractional-order chaotic models and fuzzy logic control, European Physical Journal:
Special Topics, 231(5) (2022), 905-919. https://doi.org/10.1140/epjs/s11734-021-00335-2

[8] A. Buelta, A. Olivares, E. Staffetti, Chance-constrained stochastic optimal control of epidemic models: A fourth
moment method-based reformulation, Computers in Biology and Medicine, 183 (2024), 109283. https://doi.org/
10.1016/j.compbiomed.2024.109283

[9] B. Cao, T. Kang, Nonlinear adaptive control of COVID-19 with media campaigns and treatment, Biochemical and
Biophysical Research Communications, 555 (2021), 202-209. https://doi.org/10.1016/j.bbrc.2020.12.105

[10] Z. Chen, L. Feng, H. A. Lay, K. Furati, A. Khaliq, SEIR model with unreported infected population and dynamic
parameters for the spread of COVID-19, Mathematics and Computers in Simulation, 198 (2022), 31-46. https:
//doi.org/10.1016/j.matcom.2022.02.025

[11] X. Chen, C. Xiang, Y. Yang, Sliding mode control of the susceptible-exposed-asymptomatic-infected-recovered model
with vaccination, Mathematical Methods in the Applied Sciences, 48(3) (2025), 3936-3955. https://doi.org/10.
1002/mma.10526

[12] C. Cheng, E. Aruchunan, M. H. Noor Aziz, Leveraging dynamics informed neural networks for predictive modeling
of COVID-19 spread: A hybrid SEIRV-DNNs approach, Scientific Reports, 15(1) (2025), 2043. https://doi.org/
10.1038/s41598-025-85440-1

[13] B. Chhetri, V. M. Bhagat, S. Muthusamy, D. K. K. Vamsi, C. B. Sanjeevi, Time optimal control studies on
COVID-19 incorporating adverse events of the antiviral drugs, Computational and Mathematical Biophysics, 9(1)
(2021), 214-241. https://doi.org/10.1515/cmb-2020-0125

[14] G. Chowell, C. Viboud, X.Wang, S. M. Bertozzi, M. A. Miller, Adaptive vaccination strategies to mitigate pandemic
influenza: Mexico as a case study, PLoS One, 4(12) (2009), e8164. https://doi.org/10.1371/journal.pone.
0008164

[15] M. Coccia, The evolution of COVID-19 pandemic wave and optimal levels of vaccination to reduce negative effects
in society, Research Square, (2021). https://doi.org/10.21203/rs.3.rs-941879/v1

[16] M. Dashtbali, M. Mirzaie, A compartmental model that predicts the effect of social distancing and vaccination on
controlling COVID-19, Scientific Reports, 11(1) (2021), 1-11. https://doi.org/10.1038/s41598-021-86873-0

[17] D. Faranda, T. Alberti, Modeling the second wave of COVID-19 infections in France and Italy via a stochastic
SEIR model, Chaos, 30(11) (2020), 1-13. https://doi.org/10.1063/5.0015943

[18] M. A. Hadi, H. I. Ali, Control of COVID-19 system using a novel nonlinear robust control algorithm, Biomedical
Signal Processing and Control, 64 (2021), 102317. https://doi.org/10.1016/j.bspc.2020.102317

[19] S. Han, J. Cai, J. Yang, J. Zhang, Q.Wu, W. Zheng, H. Shi, M. Ajelli, X. H. Zhou, H. Yu, Time-varying optimization
of COVID-19 vaccine prioritization in the context of limited vaccination capacity, Nature Communications, 12(1)
(2021), 4673. https://doi.org/10.1038/s41467-021-24872-5

[20] R. Ikram, A. Khan, M. Zahri, A. Saeed, M. Yavuz, P. Kumam, Extinction and stationary distribution of a stochastic
COVID-19 epidemic model with time-delay, Computers in Biology and Medicine, 141 (2022), 105115. https:
//doi.org/10.1016/j.compbiomed.2021.105115

[21] J. S. Jia, X. Lu, Y. Yuan, G. Xu, J. Jia, N. A. Christakis, Population flow drives spatio-temporal distribution of
COVID-19 in China, Nature, 582(7812) (2020), 389-394. https://doi.org/10.1038/s41586-020-2284-y

[22] M. Khalighi, L. Lahti, F. Nda¨ırou, P. Rashkov, D. F. Torres, Fractional modelling of COVID-19 transmission
incorporating asymptomatic and super-spreader individuals, Mathematical Biosciences, 380 (2025), 109373. https:
//doi.org/10.1016/j.mbs.2024.109373

[23] K. K. F. Li, S. A. Jarvis, F. Minhas, Elementary effects analysis of factors controlling COVID-19 infections in
computational simulation reveals the importance of social distancing and mask usage, Computers in Biology and
Medicine, 134 (2021), 104369. https://doi.org/10.1016/j.compbiomed.2021.104369

[24] G. B. Libotte, F. S. Lobato, G. M. Platt, A. J. Silva Neto, Determination of an optimal control strategy for vaccine
administration in COVID-19 pandemic treatment, Computer Methods and Programs in Biomedicine, 196 (2020),
105664. https://doi.org/10.1016/j.cmpb.2020.105664

[25] X. X. Liu, S. Hu, S. J. Fong, R. G. Crespo, E. Herrera-Viedma, Modelling dynamics of coronavirus disease 2019
spread for pandemic forecasting based on Simulink, Physical Biology, 18(4) (2021), 1-20. https://doi.org/10.
1088/1478-3975/abf990

[26] F. S. Lobato, G. B. Libotte, G. M. Platt, Mathematical modelling of the second wave of COVID-19 infections using
deterministic and stochastic SIDR models, Nonlinear Dynamics, 106(2) (2021), 1359-1373. https://doi.org/10.
1007/s11071-021-06680-0

[27] A. H. A. Mehra, I. Zamani, Z. Abbasi, A. Ibeas, Observer-based adaptive PI sliding mode control of developed
uncertain SEIAR influenza epidemic model considering dynamic population, Journal of Theoretical Biology, 482
(2019), 109984. https://doi.org/10.1016/j.jtbi.2019.08.015

[28] J. M. Mendel, Type 2 fuzzy sets and systems: An overview, IEEE Computational Intelligence Magazine, 2(1)
(2007), 20-29. https://doi.org/10.1109/MCI.2007.380672

[29] M. R. Mohammadi, M. Pourgholi, M. Nouri Manzar, A fuzzy driver assistance system design with active disturbance
rejection in autonomous vehicles with longitudinal movement, Iranian Journal of Fuzzy Systems, 22(1) (2025), 93-
110. https://doi.org/10.22111/ijfs.2025.48966.8628

[30] B. Naffeti, Z. Ounissi, A. K. Srivastav, N. Stollenwerk, J. B. Van-Dierdonck, M. Aguiar, Modeling COVID-19
dynamics in the Basque Country: Characterizing population immunity profile from 2020 to 2022, BMC Infectious
Diseases, 25(1) (2025), 9. https://doi.org/10.1186/s12879-024-10342-y

[31] D. Ni˜no-Torres, A. R´ıos-Guti´errez, V. Arunachalam, C. Ohajunwa, P. Seshaiyer, Stochastic modeling, analysis, and
simulation of the COVID-19 pandemic with explicit behavioral changes in Bogot´a: A case study, Infectious Disease
Modelling, 7(1) (2022), 199-211. https://doi.org/10.1016/j.idm.2021.12.008

[32] A. Olivares, E. Staffetti, Optimal control-based vaccination and testing strategies for COVID-19, Computer Methods
and Programs in Biomedicine, 211 (2021), 106411. https://doi.org/10.1016/j.cmpb.2021.106411

[33] Y. Ouyang, S. Zhang, J. Xu, A deterministic SAIR model with vaccination and treatment: Dynamical behaviors
and control strategies, Journal of Applied Mathematics and Computing, 71(1) (2025), 573-604. https://doi.org/
10.1007/s12190-024-02238-6

[34] R. E. Precup, R. C. Roman, E. L. Hedrea, A. I. Szedlak-Stinean, I. A. Zamfirache, Classical and modern
optimization techniques applied to control and modeling, CRC Press, Taylor and Francis Group, (2025).
https://doi.org/10.1201/9781003488279

[35] H. A. Rad, A. Badi, A study on control of novel corona-virus (2019- nCoV) disease process by using PID controller,
MedRxiv, (2020), 1-9. https://doi.org/10.1101/2020.04.19.20071654

[36] H. Rafiei, M. R. Akbarzadeh-T, Reliable fuzzy neural networks for systems identification and control, IEEE Transactions
on Fuzzy Systems, 31(7) (2022), 2251-2263. https://doi.org/10.1109/TFUZZ.2022.3222036

[37] H. Rafiei, M. R. Akbarzadeh-T, Hedge-embedded linguistic fuzzy neural networks for systems identification and
control, IEEE Transactions on Artificial Intelligence, 5(10) (2024), 4928-4937. https://doi.org/10.1109/TAI.
2024.3395416

[38] H. Rafiei, A. Salehi, F. Baghbani, P. Parsa, M. R. Akbarzadeh-T, Interval type-2 fuzzy control and stochastic
modeling of COVID-19 spread based on vaccination and social distancing rates, Computer Methods and Programs
in Biomedicine, 232 (2023), 107443. https://doi.org/10.1016/j.cmpb.2023.107443

[39] T. C. Reluga, Game theory of social distancing in response to an epidemic, PLoS Computational Biology, 6(5)
(2010), e1000793. https://doi.org/10.1371/journal.pcbi.1000793

[40] F. Rezaee-Ahmadi, H. Rafiei, M. R. Akbarzadeh-T, Z-adaptive fuzzy inference systems, in IEEE International
Conference on Fuzzy Systems, Institute of Electrical and Electronics Engineers Inc., (2021). https://doi.org/10.
1109/FUZZ45933.2021.9494413

[41] R. C. Roman, R. E. Precup, E. M. Petriu, A. I. Borlea, Hybrid data-driven active disturbance rejection sliding
mode control with tower crane systems validation, Romanian Journal of Information Science and Technology, 27(1)
(2024), 50-64. https://doi.org/10.59277/ROMJIST.2024.1.04

[42] S. Saha, G. Samanta, J. J. Nieto, Impact of optimal vaccination and social distancing on COVID-19 pandemic,
Mathematics and Computers in Simulation, 200 (2022), 285-314. https://doi.org/10.1016/j.matcom.2022.04.
025

[43] P. Scarabaggio, R. Carli, G. Cavone, N. Epicoco, M. Dotoli, Non-pharmaceutical stochastic optimal control strategies
to mitigate the COVID-19 spread, IEEE Transactions on Automation Science and Engineering, 19(2) (2021),
560-575. https://doi.org/10.1109/TASE.2021.3111338

[44] S. Singh Negi, Ravina, N. Sharma, A. Priyadarshi, Optimal control analysis on the spread of COVID-19: Impact
of contact transmission and environmental contamination, Gene, 941 (2025), 149033. https://doi.org/10.1016/
j.gene.2024.149033

[45] A. Tarafdar, J. Mahato, R. K. Upadhyay, P. Bhattacharya, Exploring the synergy of media awareness and quarantine
classes in SiSAQEIHR model for pandemic control: A deep LSTM-RNN predictions, Physica D, 474 (2025),
134563. https://doi.org/10.1016/j.physd.2025.134563

[46] J. Torres-Jim´enez, Juan Pav´on, Applications of metaheuristics in real-life problems, Progress in Artificial Intelligence,
2(4) (2014), 175-176. https://doi.org/10.1007/s13748-014-0051-8

[47] C. Treesatayapun, Epidemic model dynamics and fuzzy neural-network optimal control with impulsive traveling
and migrating: Case study of COVID-19 vaccination, Biomedical Signal Processing and Control, 71 (2022), 103227.
https://doi.org/10.1016/j.bspc.2021.103227

[48] P. Yarsky, Using a genetic algorithm to fit parameters of a COVID-19 SEIR model for US states, Mathematics
and Computers in Simulation, 185 (2021), 687-695. https://doi.org/10.1016/j.matcom.2021.01.022

[49] C. Zachreson, R. Tobin, C. Walker, E. Conway, F. M. Shearer, J. McVernon, N. Geard, A model-based assessment
of social isolation practices for COVID-19 outbreak response in residential care facilities, BMC Infectious Diseases,
24(1) (2024), 880. https://doi.org/10.1186/s12879-024-09788-x

[50] G. Zhang, Z. Li, A. Din, T. Chen, Dynamic analysis and optimal control of a stochastic COVID-19 model, Mathematics
and Computers in Simulation, 215 (2024), 498-517. https://doi.org/10.1016/j.matcom.2023.08.005