Quantifying the Public Health Effects of Vaccine Hesitancy and Delays in Screening Clinically Infected Patients: Insights From a COVID-19 Transmission Model
Keywords:
Coronavirus, Mathematical Model, Time Delay,, Vaccine Hesitancy, Screening Delay
Abstract
Motivated by the recent COVID-19 outbreak, we develop a time delay infectious disease model that incorporates vaccination and screening of clinically infected patients and calibrate it using Chinese data to understand the quantitative implications of vaccine hesitancy and delay in the screening of clinically infected patients. Vaccine hesitancy refers to the denial or delay in acceptance of vaccines despite their availability. Understanding the implications of vaccine hesitancy is therefore essential for designing public health interventions. Analysis of the model revealed that whenever R0≤1R0≤1, there exists a globally asymptotically disease-free equilibrium. However, whenever R0>1R0>1, there exists a unique endemic equilibrium which is globally asymptotically stable. In addition, results also show that vaccine hesitancy and delay in hospitalizing clinically infected patients have a stronger impact on the deaths toll and new infections generated. Vaccine hesitancy and delayed screening of clinically infected patients lead to harmonic oscillations in deaths and new cases, which, however, die out over time. Our findings underscore the importance of including vaccine hesitancy and delay in hospitalizing clinically infected patients in the design of control strategies for infectious diseases.
References
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Yang, W. (2021). Modeling COVID-19 pandemic with hierarchical quarantine and time delay, Dyn. Games Appl., 11(4), 892-914. DOI: 10.1007/s13235-021-00382-3
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Rui Xu. Global stability of a delayed epidemic model with latent period and vaccination strategy. (2012). Applied Mathematical Modelling 36 (2012) 52935300. DOI: 10.1016/j.apm.2011.12.037
Hale, J. K., & Verduyn Lunel, S. (1993). Introduction to Functional Differential Equations. Springer-Verlag, New York. DOI: 10.1007/978-1-4612-4342-7
Van den, D.P., & Watmough, J. (2002). Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Math. Biosci. 180 (2002), 29-48. DOI: 10.1016/s0025-5564(02)00108-6
LaSalle, J. (1960). Some extensions of Liapunov's second method, IRE Trans. Circuit Theory, 7(4), 520-527. DOI: 10.1109/tct.1960.1086720
Faicai, N., Ivan, A., Juan, J. N., & Delfim, F. M. (2020). Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan, Chaos Solit. Fractals 135 (2020) 109846. DOI: 10.1016/j.chaos.2020.109846
Mushayabasa, S., Ngarakana-Gwasira, E. T., & Mushanyu, J. (2020). On the role of governmental action and individual reaction on COVID-19 dynamics in South Africa: A mathematical modelling study. Inform. Med. Unlocked 20 (2020) 100387. DOI: 10.1016/j.imu.2020.100387.
Aatif, A., Saif, U., & Khan, M. A.(2022). The impact of vaccination on the modeling of COVID-19 dynamics: A fractional order model. Nonlinear Dyn., 110 (2022), 3921-3940. DOI: 10.1007/s11071-022-07798-5
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Marino, S., Hogue, I. B., Ray, C. J. R., & Kirschner, D. E. (2008). A methodology for performing global uncertainty and sensitivity analysis in systems biology, J. Theor. Biol. 254(1), 178-96. DOI: 10.1016/j.jtbi.2008.04.011
Suneja, M., Beekmann, S.E., Dhaliwal, G., Miller, A. C., & Polgreen, P. M. (2022). Diagnostic delays in infectious diseases. Diagnosis (Berl), 9(3), 332-339. DOI: 10.1515/dx-2021-0092
Aw, J., Seng, J.J.B., Seah, S.S.Y., & Low, L.L. (2021). COVID-19 Vaccine HesitancyA Scoping Review of Literature in High-Income Countries. Vaccines, 9(8), 900. DOI: 10.3390/vaccines9080900
Dhama, K., Sharun, K., Tiwari, R., Dhawan, M., Emran, T. B., Rabaan, A. A., & Alhumaid, S. (2021). COVID-19 vaccine hesitancy- reasons and solutions to achieve a successful global vaccination campaign to tackle the ongoing pandemic. Human Vaccines & Immunotherapeutics, 17(10), 3495-3499. DOI: 10.1080/21645515.2021.1926183
Pourrazavi, S., Fathifar, Z., Sharma, M., & Allahverdiipour, H. (2023). COVID-19 vaccine hesitancy: A Systematic review of cognitive determinants. Health Promotion Perspectives, 13(1), 21-35. DOI: 10.34172/hpp.2023.03
Yörük, S., & Güler, D. (2021). Factors associated with pediatric vaccine hesitancy of parents: A cross-sectional study in Turkey. Human Vaccines & Immunotherapeutics, 17(11), 4505-4511. DOI: 10.1080/21645515.2021.1953348
Romer, D., Jamieson, K. H., & Kligler-Vilenchik, L. (2022). Misinformation about vaccine safety and uptake of COVID-19 vaccines among adults and 5-11-year-olds in the United States. Vaccine, 40(45), 6463-6470. DOI: 10.1016/j.vaccine.2022.09.046
Kelemu, B., Tariku, A., Diriba, G., & Gelibo, T. (2024). Global COVID-19 Vaccine acceptance level and its determinants: An umbrella review. BMC Public Health, 24(1), 5. DOI: 10.1186/s12889-023-17497-4
Elharakeh, A., Thakur, N., Quader, S., Hashmi, F. K., Saleem, A., Zeeshan, M., & Riaz, M. (2021). COVID-19 Vaccine Hesitancy Worldwide: A Concise Systematic Review of Vaccine Acceptance Rates. Vaccines, 9(2), 160. DOI: 10.3390/vaccines.902016
Sauro, K., Vatanpour S., Thomas, A., et al. (2024). Consequences of delaying non-urgent surgeries during COVID-19: A population-based retrospective cohort study in Alberta, Canada. BMJ Open 2024 (14), e085247. DOI: 10.1136/bmjopen-2024-085247
Olanipekun, T. (2021). The impact of COVID-19 testing on length of hospital stay and patient flow in hospitals. Journal of Community Hospital Internal Medicine Perspectives, 11(2), 180-183. DOI: 10.1080/20009666.2020.1866249
Motta, M., Callaghan, T., Padmanabhan, M. (2025). Quantifying the prevalence and determinants of respiratory syncytial virus (RSV) vaccine hesitancy in US adults aged 60 or older. Public Health, 238(40), 3-6. DOI: 10.1016/j.puhe.2024.08.004
Carethers, J. M., Sengupta, R., Blakey, R., Ribas, A., & D'Souza, G. (2020). Disparities in Cancer Prevention in the COVID-19 Era. Cancer Prev Res (Phila), 13(11), 893-896. DOI: 10.1158/1940-6207.CAPR-20-0447
Blumenthal, S. J., Goldenson, J., Gonzalez, A., Landefeld, C. S., & Krieger, N. (2021). Racial and ethnic inequities in the early distribution of U.S. COVID-19 testing sites and mortality. European Journal of Clinical Investigation, 51(11), e13669. DOI: 10.1111/ec.13669,
Gashirai, T. B., Musekwa-Hove, S. D., Lolika, P. O., & Mushayabasa, S. (2020). Global stability and optimal control analysis of a foot-and-mouth disease model with vaccine failure and environmental transmission. Chaos Solitons and Fractals 132(2020), 109568. DOI: 10.1016/j.chaos.2019.109568.
Faniran, T. S., & Ayoola, E. O. (2019). Mathematical Analysis of Basic Reproduction Number for the Spread and Control of Malaria Model with Non-Drug Compliant Humans. International Journal of Mathematical Sciences and Optimization: Theory and Applications, 2019(2), 558 - 570. https://ijms.unalg.edu.ng/article/view/489
Troiano, G., & Nardi, A. (2021). Vaccine hesitancy in the era of COVID-19, Public health, 194 (2021), 245-251. DOI: 10.1016/j.puhe.2021.02.025
Müller, J., & Koopmann, B. (2016). The effect of delay on contact tracing, Math. Biosci. 282 (2016), 204-214. DOI: 10.1016/j.mbs.2016.10.010
Lu, H., Ding, Y., Gong, S., & Wang, S. (2021). Mathematical modeling and dynamic analysis of SIQR model with delay for pandemic COVID-19. Math. Biosci. Eng., 18(4), 3197-3214. DOI: 10.3934/mbe.2021159
Albani, V. V., Loria, J., Massad, E., & Zubelli, J. P. (2021). The impact of COVID-19 vaccination delay: A data-driven modeling analysis for Chicago and New York City. Vaccine 39(41), 6088-6094. DOI: 10.1016/j.vaccine.2021.08.098
Yang, W. (2021). Modeling COVID-19 pandemic with hierarchical quarantine and time delay, Dyn. Games Appl., 11(4), 892-914. DOI: 10.1007/s13235-021-00382-3
Frank, B., Ethin- Osa (2021). A Laplace Decomposition Analysis of Corona Virus Disease 2019 (Covid 19) Pandemic Model. International Journal of Mathematical Sciences and Optimization: Theory and Applications, 6(2), 847-861. DOI: 10.6084/m9.figshare.13643414
Al-Tuwairqi, S. M., & Al-Harbi, S. K. (2022). A time-delayed model for the spread of COVID-19 with vaccination. Scientific Reports, (2022) 12, 19435. DOI: 10.1038/s41598-022-23822-5
Chen, T., Wu, D., Chen, H., Yan, W., Yang, D., Chen, G., Ma, K., Xu, D., Yu, H., & Wang, H. (2020). Clinical Characteristics of 113 deceased Patients with coronavirus disease 2019: Retrospective study. BMJ (2020), 368, m1091. DOI: 10.1136/bmj.m1091
Wang, C., Horby, P. W., Hayden, F. G., & Gao, G. F. (2020). A novel coronavirus outbreak of global health concern. The Lancet, 395(10223), 470-473. DOI: 10.1016/S0140-6736(20)30185-9
Rui Xu. Global stability of a delayed epidemic model with latent period and vaccination strategy. (2012). Applied Mathematical Modelling 36 (2012) 52935300. DOI: 10.1016/j.apm.2011.12.037
Hale, J. K., & Verduyn Lunel, S. (1993). Introduction to Functional Differential Equations. Springer-Verlag, New York. DOI: 10.1007/978-1-4612-4342-7
Van den, D.P., & Watmough, J. (2002). Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Math. Biosci. 180 (2002), 29-48. DOI: 10.1016/s0025-5564(02)00108-6
LaSalle, J. (1960). Some extensions of Liapunov's second method, IRE Trans. Circuit Theory, 7(4), 520-527. DOI: 10.1109/tct.1960.1086720
Faicai, N., Ivan, A., Juan, J. N., & Delfim, F. M. (2020). Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan, Chaos Solit. Fractals 135 (2020) 109846. DOI: 10.1016/j.chaos.2020.109846
Mushayabasa, S., Ngarakana-Gwasira, E. T., & Mushanyu, J. (2020). On the role of governmental action and individual reaction on COVID-19 dynamics in South Africa: A mathematical modelling study. Inform. Med. Unlocked 20 (2020) 100387. DOI: 10.1016/j.imu.2020.100387.
Aatif, A., Saif, U., & Khan, M. A.(2022). The impact of vaccination on the modeling of COVID-19 dynamics: A fractional order model. Nonlinear Dyn., 110 (2022), 3921-3940. DOI: 10.1007/s11071-022-07798-5
Abdul-Rahman, J. M., & Alfred, K. H. (2020). Mathematical modelling on COVID-19 transmission impacts with preventive measures: a case study of Tanzania, J. Biol. Dyn. 14 (2020), 748-766. DOI: 10.1080/17513758.2020.1823494.
Marino, S., Hogue, I. B., Ray, C. J. R., & Kirschner, D. E. (2008). A methodology for performing global uncertainty and sensitivity analysis in systems biology, J. Theor. Biol. 254(1), 178-96. DOI: 10.1016/j.jtbi.2008.04.011
Published
2025-10-10
How to Cite
Lolika, P. O., Helikumi, M., Sube, K., & Mushayabasa, S. (2025). Quantifying the Public Health Effects of Vaccine Hesitancy and Delays in Screening Clinically Infected Patients: Insights From a COVID-19 Transmission Model. International Journal of Mathematical Sciences and Optimization: Theory and Applications, 11(3), 88 - 107. Retrieved from https://ijmso.unilag.edu.ng/article/view/2905
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