宫颈癌是最常见的癌症类型之一,它主要由高风险的人乳头瘤病毒(HPV)引起,并继续以惊人的速度传播。虽然HPV的影响之前已经调查过,目前只有少量的数学模型来说明HPV在宫颈癌中的动态作用.目标是开发一个宿主内密度依赖性确定性模型,用于基底细胞的动力学含义。病毒体,和结合免疫和功能反应的淋巴细胞。使用基本复制数等流行病学模型技术对模型进行分析,并使用MatlabODE求解器对模型进行仿真。在模型中考虑了六个区室;易感细胞(S),感染细胞(I),癌前细胞(P),癌细胞(C),病毒体(V),和淋巴细胞(L)。下一代矩阵(NGM),生存功能,和特征多项式方法用于确定表示为R0的基本再现数。R0是使用三种方法获得的,因为NGM有一些弱点,因此需要其他两种方法。这项研究的结果表明,当R0*<1时,无疾病平衡点是局部渐近稳定的,如果R0*≤1,则局部渐近稳定,如果R0*>1,则局部平衡是全局渐近稳定的。获得的结果表明,癌前细胞向癌细胞的进展速率(θ)对模型具有最直接的影响。当(θ)增加0.08时,该模型能够将患者的寿命估计为10天。这项研究的结果将有助于医疗保健提供者,公共卫生当局,和非政府健康团体制定有效的预防策略来减缓宫颈癌的发展。应该做更多的研究来确定可能导致宫颈癌患者死亡的癌细胞的确切数量,因为本文估计比例为75%。
Cervical cancer is one of the most common types of cancer and it is caused mostly by high-risk Human Papillomavirus (HPV) and continues to spread at an alarming rate. While HPV impacts have been investigated before, there are currently only a scanty number of mathematical models that account for HPV\'s dynamic role in cervical cancer. The objectives were to develop an in-host density-dependent deterministic model for the dynamics implications of basal cells, virions, and lymphocytes incorporating immunity and functional responses. Analyze the model using techniques of epidemiological models such as basic reproduction number and simulate the model using Matlab ODE solver. Six compartments are considered in the model that is; Susceptible cells (S), Infected cells (I), Precancerous cells (P), Cancerous cells (C), Virions (V), and Lymphocytes (L). Next generation matrix (NGM), survival function, and characteristic polynomial method were used to determine the basic reproduction number denoted as R 0 . R 0 was obtained using three methods because NGM has some weaknesses hence the need for the other two methods. The findings from this research indicated that Disease-Free Equilibrium point is locally asymptotically stable whenever R 0 * < 1 and globally asymptotically stable if R 0 * ≤ 1 and the Endemic Equilibrium is globally asymptotically stable if R 0 * > 1 . The results obtained shows that the progression rate of precancerous cells to cancerous cells ( θ ) has the most direct impact on the model. The model was able to estimate the longevity of a patient as 10 days when ( θ ) increases by 0.08 . The findings of this research will help healthcare providers, public health authorities, and non-governmental health groups in creating effective prevention strategies to slow the development of cervical cancer. More research should be done to determine the exact number of cancerous cells that can lead to the death of a cervical cancer patient since this paper estimated a proportion of 75 % .