Department of Mathematics and Natural Sciences, SDU University, Kaskelen 040900, Kazakhstan.
General Education Department, New Uzbekistan University, Movarounnahr Street 1, Tashkent 100000, Uzbekistan.
Math Biosci Eng. 2024 Sep 19;21(9):7103-7123. doi: 10.3934/mbe.2024314.
Mathematical modeling plays a crucial role in understanding and combating infectious diseases, offering predictive insights into disease spread and the impact of vaccination strategies. This paper explored the significance of mathematical modeling in epidemic control efforts, focusing on the interplay between vaccination strategies, disease transmission rates, and population immunity. To facilitate meaningful comparisons of vaccination strategies, we maintained a consistent framework by fixing the vaccination capacity to vary from 10 to 100% of the total population. As an example, at a 50% vaccination capacity, the pulse strategy averted approximately 45.61% of deaths, while continuous and hybrid strategies averted around 45.18 and 45.69%, respectively. Sensitivity analysis further indicated that continuous vaccination has a more direct impact on reducing the basic reproduction number $ R_0 $ compared to pulse vaccination. By analyzing key parameters such as $ R_0 $, pulse vaccination coefficients, and continuous vaccination parameters, the study underscores the value of mathematical modeling in shaping public health policies and guiding decision-making during disease outbreaks.
数学建模在理解和对抗传染病方面起着至关重要的作用,为疾病传播和疫苗接种策略的影响提供了预测性的见解。本文探讨了数学建模在疫情控制工作中的重要性,重点研究了疫苗接种策略、疾病传播率和人群免疫力之间的相互作用。为了便于对疫苗接种策略进行有意义的比较,我们通过将疫苗接种能力固定在总人口的 10%到 100%之间来保持一致的框架。例如,在 50%的疫苗接种能力下,脉冲策略避免了大约 45.61%的死亡,而连续和混合策略则分别避免了约 45.18%和 45.69%的死亡。敏感性分析进一步表明,与脉冲疫苗接种相比,连续疫苗接种对降低基本繁殖数 $ R_0 $ 有更直接的影响。通过分析关键参数,如 $ R_0 $ 、脉冲疫苗接种系数和连续疫苗接种参数,该研究强调了数学建模在制定公共卫生政策和指导疫情爆发期间决策方面的价值。