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assignmentutor-lab™ 为您的留学生涯保驾护航 在代写数学分析Mathematical Analysis方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数学分析Mathematical Analysis代写方面经验极为丰富，各种代写数学分析Mathematical Analysis相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 数学代写|数学分析代写Mathematical Analysis代考|The Model

Our model consists of susceptible, exposed, infected and recovered individuals, with the productively infected compartment split in two. The model allows for otherwise healthy individuals to contract leprosy, clear asymptomatic infections, progress from asymptomatic to symptomatic infection states, recover through MDT or relapse to symptomatic infection. Specifically, the five classes under consideration are as follows: the susceptible class ‘ $\mathrm{S}$ ‘, those with sub-clinical or asymptomatic infections ‘A’, those with symptomatic infections that they choose to disclose ‘ $\mathrm{X}$ ‘, those with symptomatic infections that they choose to conceal ‘ $\mathrm{Y}$ ‘, and those who have recovered from the disease ‘ $R$ ‘. By splitting the non-asymptomatic infection compartment into two discrete groups, the model mimics the choice, available to members of the population who develop symptomatic leprosy infections, to either conceal or disclose their infection. Likewise, the same choice is available to members of the population who relapse into symptomatic infections. Two further possibilities are accounted for by the model, in which members who originally concealed their symptomatic infection may later change their minds, disclosing their infection or are discovered. This is manifested as a path from ‘ $\mathrm{Y}$ ‘ to ‘ $\mathrm{X}$ ‘. Incorporating this split in the infection compartment is a simple way to explicitly account for the effects of stigma on the transmission of leprosy.

## 数学代写|数学分析代写Mathematical Analysis代考|Equilibria

The disease-free equilibrium (DFE) is given by $(\bar{S}, \bar{A}, \bar{X}, \bar{R})=(\pi / \mu, 0,0,0)$.
To find the endemic equilibrium, we start by setting the governing equations of our model to zero:
\begin{aligned} &0=\pi+\Omega A-\mu S-\beta_{1}(1-\eta) S A-\beta_{1} S Y-\left(\beta_{1}-\frac{\beta_{2} X}{m+X}\right) S X \ &0=\beta_{1}(1-\eta) S A+\beta_{1} S Y+\left(\beta_{1}-\frac{\beta_{2} X}{m+X}\right) S X-(\mu+\gamma+\Omega) A \ &0=f \gamma A+\sigma Y+f \delta R-\left(v_{X}+\alpha\right) X \ &0=(1-f) \gamma A+(1-f) \delta R-\left(v_{Y}+\sigma+\zeta\right) Y \ &0=\alpha X+\zeta Y-(\mu+\delta) R \end{aligned}
We rearrange Eq. (6) to obtain
$$R(X, Y)=\frac{\alpha}{\mu+\delta} X+\frac{\zeta}{\mu+\delta} Y$$
Next, we substitute Eq. (7) into Eq. (5), giving
$$0=(1-f) \gamma A+(1-f) \frac{\alpha \delta}{\mu+\delta} X+(1-f) \frac{\zeta \delta}{\mu+\delta} Y-\left(v_{Y}+\sigma+\zeta\right) Y$$

which we then rearrange to get
$$A(X, Y)=\left(\left(v_{Y}+\sigma+\zeta\right) Y-(1-f) \frac{\zeta \delta}{\mu+\delta} Y-\frac{(1-f) \alpha \delta}{\mu+\delta} X\right) \frac{1}{(1-f) \gamma}$$
Next, substituting our expressions for $A(X, Y)$ and $R(X)$ into Eq. (4), we get
$$Y(X)=\frac{\left(v_{X}+\alpha\right)(1-f)}{f\left(v_{Y}+\sigma+\zeta\right)+\sigma(1-f)} X \equiv q X$$

## 数学代写|数学分析代写Mathematical Analysis代考|Equilibria

$$0=\pi+\Omega A-\mu S-\beta_{1}(1-\eta) S A-\beta_{1} S Y-\left(\beta_{1}-\frac{\beta_{2} X}{m+X}\right) S X \quad 0=\beta_{1}(1-\eta) S A+\beta_{1} S Y+\left(\beta_{1}-\frac{\beta_{2} X}{m+X}\right) S X-(\mu+\gamma+\Omega) A 0=f \gamma A+$$

$$R(X, Y)=\frac{\alpha}{\mu+\delta} X+\frac{\zeta}{\mu+\delta} Y$$

$$0=(1-f) \gamma A+(1-f) \frac{\alpha \delta}{\mu+\delta} X+(1-f) \frac{\zeta \delta}{\mu+\delta} Y-\left(v_{Y}+\sigma+\zeta\right) Y$$

$$A(X, Y)=\left(\left(v_{Y}+\sigma+\zeta\right) Y-(1-f) \frac{\zeta \delta}{\mu+\delta} Y-\frac{(1-f) \alpha \delta}{\mu+\delta} X\right) \frac{1}{(1-f) \gamma}$$

$$Y(X)=\frac{\left(v_{X}+\alpha\right)(1-f)}{f\left(v_{Y}+\sigma+\zeta\right)+\sigma(1-f)} X \equiv q X$$

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

assignmentutor™您的专属作业导师
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