Sensitivity Analysis of the Basic Reproduction Number for Two Strains Covid-19 Model with Vaccination and Awareness Program
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515-524
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10.58578/mjms.v4i3.9382
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Authors:Anate A.O.1*, Adamu M. M.2, Adamu M.S.3, Kwami A. M.4
Copyright :Authors retain copyright and grant the journal right of first publication.
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Abstract
Sensitivity analysis of the basic reproduction number is an essential mathematical tool for identifying the parameters that most strongly influence disease transmission in epidemiological models. This study analyzes the sensitivity of the basic reproduction number in a COVID-19 transmission model comprising seven mutually exclusive compartments and incorporating vaccination and awareness interventions. The basic reproduction number, R₀, was derived using the next-generation matrix method, after which local sensitivity analysis was conducted using data obtained from the Nigeria Centre for Disease Control and other sources. The analysis produced two values of R₀ whose magnitudes depend on the model parameters, particularly those associated with vaccination and public awareness. The findings indicate that recruitment and infection rates exert strong positive effects on disease transmission, whereas vaccination and natural recovery rates have negative effects on disease progression. These results demonstrate that reducing infection-related parameters while strengthening vaccination and recovery mechanisms is critical for controlling COVID-19 transmission. The study contributes to epidemiological modeling by identifying the parameters that should be prioritized in disease-control strategies. Accordingly, policymakers should implement coordinated interventions, including quarantine, lockdown measures, vaccination programs, and effective public awareness campaigns, to reduce transmission and limit disease growth.
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References
. Kermack, W. O., & McKendrick, A. G. (1991). Contributions to the mathematical theory of epidemics—I. Bulletin of Mathematical Biology, 53(1–2), 33–55. https://doi.org/10.1007/BF02464423
. Brauer, F., & Castillo-Chavez, C. (2001). Mathematical models in population biology and epidemiology. Springer. https://doi.org/10.1007/978-1-4757-3516-1
. Branswell, H. (2020, March 11). WHO declares the coronavirus outbreak a pandemic. STAT. https://www.statnews.com/2020/03/11/who-declares-the-coronavirus-outbreak-a-pandemic/
. Di Gennaro, F., Pizzol, D., Marotta, C., Antunes, M., Racalbuto, V., Veronese, N., & Smith, L. (2020). Coronavirus diseases (COVID-19) current status and future perspectives: A narrative review. International Journal of Environmental Research and Public Health, 17(8), 2690. https://doi.org/10.3390/ijerph17082690
. World Health Organization. (n.d.). WHO COVID-19 dashboard. Retrieved April 24, 2020, from https://covid19.who.int/
. Ferretti, L., Wymant, C., Kendall, M., Zhao, L., Nurtay, A., Parker, M., Abeler-Dörner, L., Bonsall, D., & Fraser, C. (2020). Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science, 368(6491), eabb6936. https://doi.org/10.1126/science.abb6936
. Powell, D. R., Fair, J., LeClaire, R. J., Moore, L. M., & Thompson, D. (2005). Sensitivity analysis of an infectious disease model. In Proceedings of the 23rd International System Dynamics Conference (pp. 1–20). https://proceedings.systemdynamics.org/2005/proceed/papers/LECLA330.pdf
. Diekmann, O., Heesterbeek, J. A. P., & Metz, J. A. J. (1990). 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(4), 365–382. https://doi.org/10.1007/BF00178324
. Fosu, G. O., Akweittey, E., & Adu-Sackey, A. (2020). Next-generation matrices and basic reproductive numbers for all phases of the coronavirus disease. Open Journal of Mathematical Sciences, 4(1), 261–272. https://doi.org/10.30538/oms2020.0117
. van den Driessche, P., & Watmough, J. (2002). Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Mathematical Biosciences, 180(1–2), 29–48. https://doi.org/10.1016/S0025-5564(02)00108-6
. Nigeria Centre for Disease Control. (2020, February 28). First case of corona virus disease confirmed in Nigeria. https://ncdc.gov.ng/news/227/first-case-of-corona-virus-disease-confirmed-in-nigeria
. Eegunjobi, A. S., & Makinde, O. D. (2022). Mathematical analysis of two strains COVID-19 disease using SEIR model. Journal of Mathematical and Fundamental Sciences, 54(2), 211–232. https://doi.org/10.5614/j.math.fund.sci.2022.54.2.1
. Akinwande, N. I., Ashezua, T. T., Gweryina, R. I., Somma, S. A., Oguntolu, F. A., Usman, A., Abdurrahman, O. N., Kaduna, F. S., Adajime, T. P., Kuta, F. A., Abdulrahman, S., Olayiwola, R. O., Enagi, A. I., Bolarin, G. A., & Shehu, M. D. (2022). Mathematical model of COVID-19 transmission dynamics incorporating booster vaccine program and environmental contamination. Heliyon, 8(11), e11513. https://doi.org/10.1016/j.heliyon.2022.e11513