Research Article
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Year 2025, Volume: 5 Issue: 2, 280 - 306, 30.06.2025
https://doi.org/10.53391/mmnsa.1567457

Abstract

References

  • [1] Goel, K., Kumar, A. and Nilam. A deterministic time-delayed SVIRS epidemic model with incidences and saturated treatment. Journal of Engineering Mathematics, 121, 19-38, (2020).
  • [2] Guo, S., Xue, Y., Yuan, R. and Liu, M. An improved method of global dynamics: Analyzing the COVID-19 model with time delays and exposed infection. Chaos: An Interdisciplinary Journal of Nonlinear Science, 33(5), 053116, (2023).
  • [3] Mvogo, A., Tiomela, S.A., Macías-Díaz, J.E. and Bertrand, B. Dynamics of a cross-superdiffusive SIRS model with delay effects in transmission and treatment. Nonlinear Dynamics, 111, 13619-13639, (2023).
  • [4] Singh, A. and Arquam, M. Epidemiological modeling for COVID-19 spread in India with the effect of testing. Physica A: Statistical Mechanics and its Applications, 592, 126774, (2022).
  • [5] Tamilalagan, P., Krithika, B., Manivannan, P. and Karthiga, S. Is reinfection negligible effect in COVID-19? A mathematical study on the effects of reinfection in COVID-19. Mathematical Methods in the Applied Sciences, 46(18), 19115-19134, (2023).
  • [6] Garba, S.M., Lubuma, J.M.S. and Tsanou, B. Modeling the transmission dynamics of the COVID-19 Pandemic in South Africa. Mathematical Biosciences, 328, 108441, (2020).
  • [7] Taboe, H.B., Salako, K.V., Tison, J.M., Ngonghala, C.N. and Kakaï, R.G. Predicting COVID-19 spread in the face of control measures in West Africa. Mathematical Biosciences, 328, 108431, (2020).
  • [8] Ngonghala, C.N., Taboe, H.B., Safdar, S. and Gumel, A.B. Unraveling the dynamics of the Omicron and Delta variants of the 2019 coronavirus in the presence of vaccination, mask usage, and antiviral treatment. Applied Mathematical Modelling, 114, 447-465, (2023).
  • [9] Wang, K., Fan, H. and Zhu, Y. Dynamics and application of a generalized SIQR epidemic model with vaccination and treatment. Applied Mathematical Modelling, 120, 382-399, (2023).
  • [10] Ojo, M.M., Peter, O.J., Goufo, E.F.D. and Nisar, K.S. A mathematical model for the co-dynamics of COVID-19 and tuberculosis. Mathematics and Computers in Simulation, 207, 499-520, (2023).
  • [11] Chen, Z., Feng, L., Lay Jr, H.A., Furati, K. and Khaliq, A. SEIR model with unreported infected population and dynamic parameters for the spread of COVID-19. Mathematics and Computers in Simulation, 198, 31-46, (2022).
  • [12] Saha, S., Samanta, G. and Nieto, J.J. Impact of optimal vaccination and social distancing on COVID-19 pandemic. Mathematics and Computers in Simulation, 200, 285-314, (2022).
  • [13] Peter, O.J., Abidemi, A., Fatmawati, F., Ojo, M.M. and Oguntolu, F.A. Optimizing tuberculosis control: a comprehensive simulation of integrated interventions using a mathematical model. Mathematical Modelling and Numerical Simulation with Applications, 4(3), 238-255, (2024).
  • [14] Ouaziz, S.I. and El Khomssi, M. Mathematical approaches to controlling COVID-19: optimal control and financial benefits. Mathematical Modelling and Numerical Simulation with Applications, 4(1), 1-36, (2024).
  • [15] Mustapha, U.T., Ado, A., Yusuf, A., Qureshi, S. and Musa, S.S. Mathematical dynamics for HIV infections with public awareness and viral load detectability. Mathematical Modelling and Numerical Simulation with Applications, 3(3), 256-280, (2023).
  • [16] Pérez, A.G.C. and Oluyori, D.A. A model for COVID-19 and bacterial pneumonia coinfection with community hospital-acquired infections. Mathematical Modelling and Numerical Simulation with Applications, 2(4), 197-210, (2022).
  • [17] Turan, M., Sevinik Adıgüzel, R. and Koç, F. Stability analysis of an epidemic model with vaccination and time delay. Mathematical Methods in the Applied Sciences, 46(14), 14828-14840, (2023).
  • [18] Zhang, Z., Zhang, W., Nisar, K.S., Gul, N., Zeb, A. and Vijayakumar, V. Dynamical aspects of a tuberculosis transmission model incorporating vaccination and time delay. Alexandria Engineering Journal, 66, 287-300, (2023).
  • [19] Sepulveda, G., Arenas, A.J. and González-Parra, G. Mathematical modeling of COVID-19 dynamics under two vaccination doses and delay effects. Mathematics, 11(2), 369, (2023).
  • [20] Marot, S., Malet, I., Leducq, V., Zafilaza, K., Sterlin, D., Planas, D. et al. Rapid decline of neutralizing antibodies against SARS-CoV-2 among infected healthcare workers. Nature Communications, 12, 844, (2021).
  • [21] Prather, A.A., Dutcher, E.G., Robinson, J., Lin, J., Blackburn, E., Hecht, F.M. et al. Predictors of long-term neutralizing antibody titers following COVID-19 vaccination by three vaccine types: the BOOST study. Scientific Reports, 13, 6505, (2023).
  • [22] Puthiyedath, R., Kataria, S., Payyappallimana, U., Mangalath, P., Nampoothiri, V., Sharma, P. et al. Ayurvedic clinical profile of COVID-19–a preliminary report. Journal of Ayurveda and Integrative Medicine, 13(1), 100326, (2022).
  • [23] Tamilalagan, P., Karthiga, S. and Manivannan, P. Dynamics of fractional order HIV infection model with antibody and cytotoxic T-lymphocyte immune responses. Journal of Computational and Applied Mathematics, 382, 113064, (2021).
  • [24] Cerón Gómez, M. and Yang, H.M. A simple mathematical model to describe antibody-dependent enhancement in heterologous secondary infection in dengue. Mathematical Medicine and Biology: A Journal of the IMA, 36(4), 411-438, (2019).
  • [25] Katriel, G. Epidemics with partial immunity to reinfection. Mathematical Biosciences, 228(2), 153-159, (2010).
  • [26] Hethcote, H.W. The mathematics of infectious diseases. SIAM Review, 42(4), 599-653, (2000).
  • [27] Pathak, S., Maiti, A. and Samanta, G.P.P. Rich dynamics of an SIR epidemic model. Nonlinear Analysis: Modelling and Control, 15(1), 71-81, (2010).
  • [28] Hassard, B.D., Kazarinoff, N.D. and Wan, Y.H. Theory and Applications of Hopf Bifurcation (Vol. 41). Cambridge University Press: New York, (1981).
  • [29] Zhang, Z., Kundu, S., Tripathi, J.P. and Bugalia, S. Stability and Hopf bifurcation analysis of an SVEIR epidemic model with vaccination and multiple time delays. Chaos, Solitons & Fractals, 131, 109483, (2020).
  • [30] Danchin, A. and Turinici, G. Immunity after COVID-19: Protection or sensitization? Mathematical Biosciences, 331, 108499, (2021).
  • [31] Papageorgiou, V.E. and Tsaklidis, G. A stochastic SIRD model with imperfect immunity for the evaluation of epidemics. Applied Mathematical Modelling, 124, 768-790, (2023).
  • [32] Sharma, S. and Samanta, G.P. Stability analysis and optimal control of an epidemic model with vaccination. International Journal of Biomathematics, 8(03), 1550030, (2015).
  • [33] Sasidharakurup, H., Kumar, G., Nair, B. and Diwakar, S. Mathematical modeling of severe acute respiratory syndrome coronavirus 2 infection network with cytokine storm, oxidative stress, thrombosis, insulin resistance, and nitric oxide pathways. OMICS: A Journal of Integrative Biology, 25(12), 770-781, (2021).
  • [34] Narassima, M.S., John, D., Anbuudayasankar, S.P., Jammy, G.R., Pant, R. and Choudhury, L. SHIVIR-An Agent-Based Model to assess the transmission of COVID-19 in India. MedRxiv, 1-26, (2022).
  • [35] Li, Q., Tang, B., Bragazzi, N.L., Xiao, Y. and Wu, J. Modeling the impact of mass influenza vaccination and public health interventions on COVID-19 epidemics with limited detection capability. Mathematical Biosciences, 325, 108378, (2020).
  • [36] Tchoumi, S.Y., Diagne, M.L., Rwezaura, H. and Tchuenche, J.M. Malaria and COVID-19 co-dynamics: A mathematical model and optimal control. Applied Mathematical Modelling, 99, 294-327, (2021).
  • [37] Baba, I.A., Humphries, U.W. and Rihan, F.A. Role of vaccines in controlling the spread of COVID-19: A fractional-order model. Vaccines, 11(1), 145, (2023).
  • [38] Saiprasad, V.R., Gopal, R., Chandrasekar, V.K. and Lakshmanan, M. Analysis of COVID-19 in India using a vaccine epidemic model incorporating vaccine effectiveness and herd immunity. The European Physical Journal Plus, 137, 1003, (2022).
  • [39] Balasubramaniam, P., Prakash, M. and Tamilalagan, P. Stability and Hopf bifurcation analysis of immune response delayed HIV type 1 infection model with two target cells. Mathematical Methods in the Applied Sciences, 38(17), 3653-3669, (2015).
  • [40] Balasubramaniam, P., Tamilalagan, P. and Prakash, M. Bifurcation analysis of HIV infection model with antibody and cytotoxic T-lymphocyte immune responses and Beddington–DeAngelis functional response. Mathematical Methods in the Applied Sciences, 38(7), 1330-1341, (2015).
  • [41] Khajanchi, S. Bifurcation analysis of a delayed mathematical model for tumor growth. Chaos, Solitons & Fractals, 77, 264-276, (2015).
  • [42] Misra, A. K. and Singh, V. A delay mathematical model for the spread and control of water borne diseases. Journal of Theoretical Biology, 301, 49-56, (2012).
  • [43] Zhang, T., Li, Z., Ma, L. and Song, X. Threshold dynamics in a clonorchiasis model with time delays. Applied Mathematical Modelling, 102, 351-370, (2022).
  • [44] Cumsille, P., Rojas-Díaz, Ó., de Espanés, P.M. and Verdugo-Hernández, P. Forecasting COVID-19 Chile’second outbreak by a generalized SIR model with constant time delays and a fitted positivity rate. Mathematics and Computers in Simulation, 193, 1-18, (2022).
  • [45] Matrajt, L., Britton, T., Halloran, M.E. and Longini Jr, I.M. One versus two doses: What is the best use of vaccine in an influenza pandemic? Epidemics, 13, 17-27, (2015).
  • [46] Liu, X., Takeuchi, Y. and Iwami, S. SVIR epidemic models with vaccination strategies. Journal of Theoretical Biology, 253(1), 1-11, (2008).
  • [47] Kim, Y., Bae, S., Chang, H.H. and Kim, S.W. Characteristics of long COVID and the impact of COVID-19 vaccination on long COVID 2 years following COVID-19 infection: prospective cohort study. Scientific Reports, 14, 854, (2024).
  • [48] Cai, L.M., Li, Z. and Song, X. Global analysis of an epidemic model with vaccination. Journal of Applied Mathematics and Computing, 57, 605-628, (2018).
  • [49] Liu, Z., Magal, P., Seydi, O. and Webb, G. A COVID-19 epidemic model with latency period. Infectious Disease Modelling, 5, 323-337, (2020).
  • [50] FAQs on COVID-19 Vaccines and Vaccination Program-India, (2020). https://www.mohfw.gov.in/pdf/ FAQsCOVID19vaccinesvaccinationprogramWebsiteupload27Sep.pdf
  • [51] Antia, A., Ahmed, H., Handel, A., Carlson, N.E., Amanna, I.J., Antia, R. and Slifka, M. Heterogeneity and longevity of antibody memory to viruses and vaccines. PLoS Biology, 16(8), e2006601, (2018).
  • [52] Van den Driessche, P. and Watmough, J. Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Mathematical Biosciences, 180(1-2), 29-48, (2002).
  • [53] Diekmann, O., Heesterbeek, J.A.P. and Metz, J.A.J. On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations. Journal of Mathematical Biology, 28, 365-382, (1990).
  • [54] Li, C.K. and Schneider, H. Applications of Perron-Frobenius theory to population dynamics. ArXiv Prints, ArXiv:math/0109008, (2001).
  • [55] Brouwer, A.F. Why the Spectral Radius? An intuition-building introduction to the basic reproduction number. Bulletin of Mathematical Biology, 84, 96, (2022).
  • [56] Worldometer, Covid-19 Coronavirus Pandemic, (2024). https://www.worldometers.info/coronavirus/?utm_ cam-paign=homeAdvegas1?
  • [57] Countrymeters, India Population, (2025). https://countrymeters.info/en/India.
  • [58] World Bank Group, Life expectancy at birth, total (years)-India, (2023). https://data.worldbank.org/ indicator/SP.DYN.LE00.IN?locations=IN
  • [59] Wu, J., Dhingra, R., Gambhir, M. and Remais, J.V. Sensitivity analysis of infectious disease models: methods, advances and their application. Journal of The Royal Society Interface, 10(86), 20121018, (2013).
  • [60] Powell, D.R., Fair, J., LeClaire, R.J., Moore, L.M. and Thompson, D. Sensitivity analysis of an infectious disease model. In Proceedings, International System Dynamics Conference, pp. 1-20, Boston, USA, (2005, July).

Temporal dynamics of immunity: modeling susceptibility delay in antibody-shielded populations

Year 2025, Volume: 5 Issue: 2, 280 - 306, 30.06.2025
https://doi.org/10.53391/mmnsa.1567457

Abstract

This study presents a mathematical model that incorporates multiple time delays and a distinct compartment for antibody-protected immune individuals to analyze the transmission dynamics of infectious diseases. We ensure through analytical results that our model produced positive and bounded solutions, which is essential for realistic predictions. Parameter estimation is performed using real-time data to accurately determine the time delays associated with the system. In the absence of time delays, the analysis demonstrates that the disease transmission rate ($\beta$) plays a critical role in determining the system's behavior. When $\beta$ exceeds a threshold value ($\beta_c$), a forward bifurcation occurs. The study further investigates the impact of time delays on the stability of disease-free and endemic equilibria and identifies conditions under which the system undergoes a Hopf bifurcation, resulting in periodic oscillations. Numerical simulations are conducted to validate the theoretical findings, providing insights into the influence of immunity delays on disease persistence and intervention strategies.

References

  • [1] Goel, K., Kumar, A. and Nilam. A deterministic time-delayed SVIRS epidemic model with incidences and saturated treatment. Journal of Engineering Mathematics, 121, 19-38, (2020).
  • [2] Guo, S., Xue, Y., Yuan, R. and Liu, M. An improved method of global dynamics: Analyzing the COVID-19 model with time delays and exposed infection. Chaos: An Interdisciplinary Journal of Nonlinear Science, 33(5), 053116, (2023).
  • [3] Mvogo, A., Tiomela, S.A., Macías-Díaz, J.E. and Bertrand, B. Dynamics of a cross-superdiffusive SIRS model with delay effects in transmission and treatment. Nonlinear Dynamics, 111, 13619-13639, (2023).
  • [4] Singh, A. and Arquam, M. Epidemiological modeling for COVID-19 spread in India with the effect of testing. Physica A: Statistical Mechanics and its Applications, 592, 126774, (2022).
  • [5] Tamilalagan, P., Krithika, B., Manivannan, P. and Karthiga, S. Is reinfection negligible effect in COVID-19? A mathematical study on the effects of reinfection in COVID-19. Mathematical Methods in the Applied Sciences, 46(18), 19115-19134, (2023).
  • [6] Garba, S.M., Lubuma, J.M.S. and Tsanou, B. Modeling the transmission dynamics of the COVID-19 Pandemic in South Africa. Mathematical Biosciences, 328, 108441, (2020).
  • [7] Taboe, H.B., Salako, K.V., Tison, J.M., Ngonghala, C.N. and Kakaï, R.G. Predicting COVID-19 spread in the face of control measures in West Africa. Mathematical Biosciences, 328, 108431, (2020).
  • [8] Ngonghala, C.N., Taboe, H.B., Safdar, S. and Gumel, A.B. Unraveling the dynamics of the Omicron and Delta variants of the 2019 coronavirus in the presence of vaccination, mask usage, and antiviral treatment. Applied Mathematical Modelling, 114, 447-465, (2023).
  • [9] Wang, K., Fan, H. and Zhu, Y. Dynamics and application of a generalized SIQR epidemic model with vaccination and treatment. Applied Mathematical Modelling, 120, 382-399, (2023).
  • [10] Ojo, M.M., Peter, O.J., Goufo, E.F.D. and Nisar, K.S. A mathematical model for the co-dynamics of COVID-19 and tuberculosis. Mathematics and Computers in Simulation, 207, 499-520, (2023).
  • [11] Chen, Z., Feng, L., Lay Jr, H.A., Furati, K. and Khaliq, A. SEIR model with unreported infected population and dynamic parameters for the spread of COVID-19. Mathematics and Computers in Simulation, 198, 31-46, (2022).
  • [12] Saha, S., Samanta, G. and Nieto, J.J. Impact of optimal vaccination and social distancing on COVID-19 pandemic. Mathematics and Computers in Simulation, 200, 285-314, (2022).
  • [13] Peter, O.J., Abidemi, A., Fatmawati, F., Ojo, M.M. and Oguntolu, F.A. Optimizing tuberculosis control: a comprehensive simulation of integrated interventions using a mathematical model. Mathematical Modelling and Numerical Simulation with Applications, 4(3), 238-255, (2024).
  • [14] Ouaziz, S.I. and El Khomssi, M. Mathematical approaches to controlling COVID-19: optimal control and financial benefits. Mathematical Modelling and Numerical Simulation with Applications, 4(1), 1-36, (2024).
  • [15] Mustapha, U.T., Ado, A., Yusuf, A., Qureshi, S. and Musa, S.S. Mathematical dynamics for HIV infections with public awareness and viral load detectability. Mathematical Modelling and Numerical Simulation with Applications, 3(3), 256-280, (2023).
  • [16] Pérez, A.G.C. and Oluyori, D.A. A model for COVID-19 and bacterial pneumonia coinfection with community hospital-acquired infections. Mathematical Modelling and Numerical Simulation with Applications, 2(4), 197-210, (2022).
  • [17] Turan, M., Sevinik Adıgüzel, R. and Koç, F. Stability analysis of an epidemic model with vaccination and time delay. Mathematical Methods in the Applied Sciences, 46(14), 14828-14840, (2023).
  • [18] Zhang, Z., Zhang, W., Nisar, K.S., Gul, N., Zeb, A. and Vijayakumar, V. Dynamical aspects of a tuberculosis transmission model incorporating vaccination and time delay. Alexandria Engineering Journal, 66, 287-300, (2023).
  • [19] Sepulveda, G., Arenas, A.J. and González-Parra, G. Mathematical modeling of COVID-19 dynamics under two vaccination doses and delay effects. Mathematics, 11(2), 369, (2023).
  • [20] Marot, S., Malet, I., Leducq, V., Zafilaza, K., Sterlin, D., Planas, D. et al. Rapid decline of neutralizing antibodies against SARS-CoV-2 among infected healthcare workers. Nature Communications, 12, 844, (2021).
  • [21] Prather, A.A., Dutcher, E.G., Robinson, J., Lin, J., Blackburn, E., Hecht, F.M. et al. Predictors of long-term neutralizing antibody titers following COVID-19 vaccination by three vaccine types: the BOOST study. Scientific Reports, 13, 6505, (2023).
  • [22] Puthiyedath, R., Kataria, S., Payyappallimana, U., Mangalath, P., Nampoothiri, V., Sharma, P. et al. Ayurvedic clinical profile of COVID-19–a preliminary report. Journal of Ayurveda and Integrative Medicine, 13(1), 100326, (2022).
  • [23] Tamilalagan, P., Karthiga, S. and Manivannan, P. Dynamics of fractional order HIV infection model with antibody and cytotoxic T-lymphocyte immune responses. Journal of Computational and Applied Mathematics, 382, 113064, (2021).
  • [24] Cerón Gómez, M. and Yang, H.M. A simple mathematical model to describe antibody-dependent enhancement in heterologous secondary infection in dengue. Mathematical Medicine and Biology: A Journal of the IMA, 36(4), 411-438, (2019).
  • [25] Katriel, G. Epidemics with partial immunity to reinfection. Mathematical Biosciences, 228(2), 153-159, (2010).
  • [26] Hethcote, H.W. The mathematics of infectious diseases. SIAM Review, 42(4), 599-653, (2000).
  • [27] Pathak, S., Maiti, A. and Samanta, G.P.P. Rich dynamics of an SIR epidemic model. Nonlinear Analysis: Modelling and Control, 15(1), 71-81, (2010).
  • [28] Hassard, B.D., Kazarinoff, N.D. and Wan, Y.H. Theory and Applications of Hopf Bifurcation (Vol. 41). Cambridge University Press: New York, (1981).
  • [29] Zhang, Z., Kundu, S., Tripathi, J.P. and Bugalia, S. Stability and Hopf bifurcation analysis of an SVEIR epidemic model with vaccination and multiple time delays. Chaos, Solitons & Fractals, 131, 109483, (2020).
  • [30] Danchin, A. and Turinici, G. Immunity after COVID-19: Protection or sensitization? Mathematical Biosciences, 331, 108499, (2021).
  • [31] Papageorgiou, V.E. and Tsaklidis, G. A stochastic SIRD model with imperfect immunity for the evaluation of epidemics. Applied Mathematical Modelling, 124, 768-790, (2023).
  • [32] Sharma, S. and Samanta, G.P. Stability analysis and optimal control of an epidemic model with vaccination. International Journal of Biomathematics, 8(03), 1550030, (2015).
  • [33] Sasidharakurup, H., Kumar, G., Nair, B. and Diwakar, S. Mathematical modeling of severe acute respiratory syndrome coronavirus 2 infection network with cytokine storm, oxidative stress, thrombosis, insulin resistance, and nitric oxide pathways. OMICS: A Journal of Integrative Biology, 25(12), 770-781, (2021).
  • [34] Narassima, M.S., John, D., Anbuudayasankar, S.P., Jammy, G.R., Pant, R. and Choudhury, L. SHIVIR-An Agent-Based Model to assess the transmission of COVID-19 in India. MedRxiv, 1-26, (2022).
  • [35] Li, Q., Tang, B., Bragazzi, N.L., Xiao, Y. and Wu, J. Modeling the impact of mass influenza vaccination and public health interventions on COVID-19 epidemics with limited detection capability. Mathematical Biosciences, 325, 108378, (2020).
  • [36] Tchoumi, S.Y., Diagne, M.L., Rwezaura, H. and Tchuenche, J.M. Malaria and COVID-19 co-dynamics: A mathematical model and optimal control. Applied Mathematical Modelling, 99, 294-327, (2021).
  • [37] Baba, I.A., Humphries, U.W. and Rihan, F.A. Role of vaccines in controlling the spread of COVID-19: A fractional-order model. Vaccines, 11(1), 145, (2023).
  • [38] Saiprasad, V.R., Gopal, R., Chandrasekar, V.K. and Lakshmanan, M. Analysis of COVID-19 in India using a vaccine epidemic model incorporating vaccine effectiveness and herd immunity. The European Physical Journal Plus, 137, 1003, (2022).
  • [39] Balasubramaniam, P., Prakash, M. and Tamilalagan, P. Stability and Hopf bifurcation analysis of immune response delayed HIV type 1 infection model with two target cells. Mathematical Methods in the Applied Sciences, 38(17), 3653-3669, (2015).
  • [40] Balasubramaniam, P., Tamilalagan, P. and Prakash, M. Bifurcation analysis of HIV infection model with antibody and cytotoxic T-lymphocyte immune responses and Beddington–DeAngelis functional response. Mathematical Methods in the Applied Sciences, 38(7), 1330-1341, (2015).
  • [41] Khajanchi, S. Bifurcation analysis of a delayed mathematical model for tumor growth. Chaos, Solitons & Fractals, 77, 264-276, (2015).
  • [42] Misra, A. K. and Singh, V. A delay mathematical model for the spread and control of water borne diseases. Journal of Theoretical Biology, 301, 49-56, (2012).
  • [43] Zhang, T., Li, Z., Ma, L. and Song, X. Threshold dynamics in a clonorchiasis model with time delays. Applied Mathematical Modelling, 102, 351-370, (2022).
  • [44] Cumsille, P., Rojas-Díaz, Ó., de Espanés, P.M. and Verdugo-Hernández, P. Forecasting COVID-19 Chile’second outbreak by a generalized SIR model with constant time delays and a fitted positivity rate. Mathematics and Computers in Simulation, 193, 1-18, (2022).
  • [45] Matrajt, L., Britton, T., Halloran, M.E. and Longini Jr, I.M. One versus two doses: What is the best use of vaccine in an influenza pandemic? Epidemics, 13, 17-27, (2015).
  • [46] Liu, X., Takeuchi, Y. and Iwami, S. SVIR epidemic models with vaccination strategies. Journal of Theoretical Biology, 253(1), 1-11, (2008).
  • [47] Kim, Y., Bae, S., Chang, H.H. and Kim, S.W. Characteristics of long COVID and the impact of COVID-19 vaccination on long COVID 2 years following COVID-19 infection: prospective cohort study. Scientific Reports, 14, 854, (2024).
  • [48] Cai, L.M., Li, Z. and Song, X. Global analysis of an epidemic model with vaccination. Journal of Applied Mathematics and Computing, 57, 605-628, (2018).
  • [49] Liu, Z., Magal, P., Seydi, O. and Webb, G. A COVID-19 epidemic model with latency period. Infectious Disease Modelling, 5, 323-337, (2020).
  • [50] FAQs on COVID-19 Vaccines and Vaccination Program-India, (2020). https://www.mohfw.gov.in/pdf/ FAQsCOVID19vaccinesvaccinationprogramWebsiteupload27Sep.pdf
  • [51] Antia, A., Ahmed, H., Handel, A., Carlson, N.E., Amanna, I.J., Antia, R. and Slifka, M. Heterogeneity and longevity of antibody memory to viruses and vaccines. PLoS Biology, 16(8), e2006601, (2018).
  • [52] Van den Driessche, P. and Watmough, J. Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Mathematical Biosciences, 180(1-2), 29-48, (2002).
  • [53] Diekmann, O., Heesterbeek, J.A.P. and Metz, J.A.J. On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations. Journal of Mathematical Biology, 28, 365-382, (1990).
  • [54] Li, C.K. and Schneider, H. Applications of Perron-Frobenius theory to population dynamics. ArXiv Prints, ArXiv:math/0109008, (2001).
  • [55] Brouwer, A.F. Why the Spectral Radius? An intuition-building introduction to the basic reproduction number. Bulletin of Mathematical Biology, 84, 96, (2022).
  • [56] Worldometer, Covid-19 Coronavirus Pandemic, (2024). https://www.worldometers.info/coronavirus/?utm_ cam-paign=homeAdvegas1?
  • [57] Countrymeters, India Population, (2025). https://countrymeters.info/en/India.
  • [58] World Bank Group, Life expectancy at birth, total (years)-India, (2023). https://data.worldbank.org/ indicator/SP.DYN.LE00.IN?locations=IN
  • [59] Wu, J., Dhingra, R., Gambhir, M. and Remais, J.V. Sensitivity analysis of infectious disease models: methods, advances and their application. Journal of The Royal Society Interface, 10(86), 20121018, (2013).
  • [60] Powell, D.R., Fair, J., LeClaire, R.J., Moore, L.M. and Thompson, D. Sensitivity analysis of an infectious disease model. In Proceedings, International System Dynamics Conference, pp. 1-20, Boston, USA, (2005, July).
There are 60 citations in total.

Details

Primary Language English
Subjects Biological Mathematics
Journal Section Research Articles
Authors

B. Krithika This is me 0009-0005-3660-6893

P. Tamilalagan 0000-0002-6291-9835

Early Pub Date July 15, 2025
Publication Date June 30, 2025
Submission Date October 15, 2024
Acceptance Date June 12, 2025
Published in Issue Year 2025 Volume: 5 Issue: 2

Cite

APA Krithika, B., & Tamilalagan, P. (2025). Temporal dynamics of immunity: modeling susceptibility delay in antibody-shielded populations. Mathematical Modelling and Numerical Simulation With Applications, 5(2), 280-306. https://doi.org/10.53391/mmnsa.1567457


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