仓室模型

本词条由Agnes初步翻译，由Zcy初步审校

此词条暂由彩云小译翻译，未经人工整理和审校，带来阅读不便，请见谅。

Compartmental models simplify the mathematical modelling of infectious diseases. The population is assigned to compartments with labels - for example, S, I, or R, (Susceptible, Infectious, or Recovered). People may progress between compartments. The order of the labels usually shows the flow patterns between the compartments; for example SEIS means susceptible, exposed, infectious, then susceptible again.

Compartmental models simplify the mathematical modelling of infectious diseases. The population is assigned to compartments with labels - for example, S, I, or R, (Susceptible, Infectious, or Recovered). People may progress between compartments. The order of the labels usually shows the flow patterns between the compartments; for example SEIS means susceptible, exposed, infectious, then susceptible again.

仓室模型Compartmental models简化了传染病传播的数学模型。人群被划分为带有标签的类别，例如，S，I，和 R，(易感者，染病者和康复者)。不同类别人群的标签会发生变化。标签的变化反映了不同类别人群之间的转化模式，例如SEIS模型代表易感者类型可以转变为暴露者类型、暴露者类型可以转变为染病者类型，染病者类型可以再次转变回易感者类型。

==Agnes（讨论）[翻译]译者知识水平限制，通过多方查询资料对传染病模型有了基本了解之后，在“SEIS means susceptible, exposed, infectious, then susceptible again”的翻译中，选择增添了一些成分，但此部分的翻译仍然存疑

==Zcy（讨论）词条名Compartmental models in epidemiology翻译为复杂传染病是否妥当

The origin of such models is the early 20th century, with an important work being that of Kermack and McKendrick in 1927.

The origin of such models is the early 20th century, with an important work being that of Kermack and McKendrick in 1927.

这类模型起源于20世纪初，克马克和麦克德里克在1927年的一项重要工作。

The models are most often run with ordinary differential equations (which are deterministic), but can also be used with a stochastic (random) framework, which is more realistic but much more complicated to analyze.

The models are most often run with ordinary differential equations (which are deterministic), but can also be used with a stochastic (random) framework, which is more realistic but much more complicated to analyze.

这些模型通常使用确定参数的常微分方程进行分析，但也可以使用具有随机参数的随机框架进行分析，这种随机框架更加贴近现实，但分析起来要复杂得多。

Models try to predict things such as how a disease spreads, or the total number infected, or the duration of an epidemic, and to estimate various epidemiological parameters such as the reproductive number. Such models can show how different public health interventions may affect the outcome of the epidemic, e.g., what the most efficient technique is for issuing a limited number of vaccines in a given population.

Models try to predict things such as how a disease spreads, or the total number infected, or the duration of an epidemic, and to estimate various epidemiological parameters such as the reproductive number. Such models can show how different public health interventions may affect the outcome of the epidemic, e.g., what the most efficient technique is for issuing a limited number of vaccines in a given population.

模型试图预测疾病的传播方式、感染总人数、流行病持续时间等，并估计各种流行病学参数，如再生数。这些模型可以显示不同的公共卫生干预措施如何对疾病传播产生影响，例如，控制疾病传播最有效的方式是在指定人群中发放数量有限的疫苗。

The SIR model

SIR模型

The SIR model is one of the simplest compartmental models, and many models are derivatives of this basic form. The model consists of three compartments:

The SIR model is one of the simplest compartmental models, and many models are derivatives of this basic form. The model consists of three compartments:

SIR 模型是最简单的仓室模型之一，许多模型都是这种基本模型的衍生物。该模型由三种类型的人群组成:

S: The number of susceptible individuals. When a susceptible and an infectious individual come into "infectious contact", the susceptible individual contracts the disease and transitions to the infectious compartment.

S: The number of susceptible individuals. When a susceptible and an infectious individual come into "infectious contact", the susceptible individual contracts the disease and transitions to the infectious compartment.

S：易感者。当一个易感者和一个感病者产生“传染性接触”时，易感者就会感染这种疾病，并被归入染病者类别。

I: The number of infectious individuals. These are individuals who have been infected and are capable of infecting susceptible individuals.

I: The number of infectious individuals. These are individuals who have been infected and are capable of infecting susceptible individuals.

I：染病者。这类人群已经感染疾病，并且有能力感染易感者。

R for the number of removed (and immune) or deceased individuals. These are individuals who have been infected and have either recovered from the disease and entered the removed compartment, or died. It is assumed that the number of deaths is negligible with respect to the total population. This compartment may also be called "recovered" or "resistant".

R: for the number of removed (and immune) or deceased individuals. These are individuals who have been infected and have either recovered from the disease and entered the removed compartment, or died. It is assumed that the number of deaths is negligible with respect to the total population. This compartment may also be called "recovered" or "resistant".

R：康复者或病死者。这些人已经被感染过，并且已经从疾病中康复并被归入康复者类别，或者已经因感染疾病死亡。假如死亡数量与总人口数量相比可以忽略不计，这种类别人群也可称为“康复者”或“抵抗者”。

This model is reasonably predictive for infectious diseases that are transmitted from human to human, and where recovery confers lasting resistance, such as measles, mumps and rubella.

This model is reasonably predictive for infectious diseases that are transmitted from human to human, and where recovery confers lasting resistance, such as measles, mumps and rubella.

这个模型可以合理地预测传染病在人与人之间传播的情况，以及康复后会产生持续性抗体的疾病，如麻疹、腮腺炎和风疹等疾病。

图1：Spatial SIR model simulation. Each cell can infect its eight immediate neighbors.空间 SIR 模型仿真。每个单元都能感染它的八个相邻单元。

These variables (S, I, and R) represent the number of people in each compartment at a particular time. To represent that the number of susceptible, infectious and removed individuals may vary over time (even if the total population size remains constant), we make the precise numbers a function of t (time): S(t), I(t) and R(t). For a specific disease in a specific population, these functions may be worked out in order to predict possible outbreaks and bring them under control.

These variables (S, I, and R) represent the number of people in each compartment at a particular time. To represent that the number of susceptible, infectious and removed individuals may vary over time (even if the total population size remains constant), we make the precise numbers a function of t (time): S(t), I(t) and R(t). For a specific disease in a specific population, these functions may be worked out in order to predict possible outbreaks and bring them under control.

这些变量（S、I和R）表示特定时间每个类别人群的数量。为了表示易感者、染病者和康复者数量会随时间变化（总的人群规模保持不变） ，我们将这些类别人群的精确数量设为时间t的函数: S(t)、 I(t)和 R(t)。对于特定人群中的特定疾病，这些函数可以用于预测潜在的传染病暴发和控制传染病的大规模爆发。

As implied by the variable function of t, the model is dynamic in that the numbers in each compartment may fluctuate over time. The importance of this dynamic aspect is most obvious in an endemic disease with a short infectious period, such as measles in the UK prior to the introduction of a vaccine in 1968. Such diseases tend to occur in cycles of outbreaks due to the variation in number of susceptibles (S(t)) over time. During an epidemic, the number of susceptible individuals falls rapidly as more of them are infected and thus enter the infectious and removed compartments. The disease cannot break out again until the number of susceptibles has built back up, e.g. as a result of offspring being born into the susceptible compartment.

As implied by the variable function of t, the model is dynamic in that the numbers in each compartment may fluctuate over time. The importance of this dynamic aspect is most obvious in an endemic disease with a short infectious period, such as measles in the UK prior to the introduction of a vaccine in 1968. Such diseases tend to occur in cycles of outbreaks due to the variation in number of susceptibles (S(t)) over time. During an epidemic, the number of susceptible individuals falls rapidly as more of them are infected and thus enter the infectious and removed compartments. The disease cannot break out again until the number of susceptibles has built back up, e.g. as a result of offspring being born into the susceptible compartment.

从变量t可以看出，该模型是动态的，每种类别人群的数量可以随时间波动。这种动态的重要性在传染期较短的地方性疾病中表现得最为明显，如在1968年引进疫苗之前英国的麻疹。由于易感者数量[math]\displaystyle{ (S (t)) }[/math]随着时间发生变化，这类疾病往往会周期性爆发。在流行病爆发期间，由于更多的人受到感染，转变为染病者和康复者的类别，易感者数量迅速下降。这种疾病只有易感者数量增加时才能再次爆发，例如：由于后代的出生，增加易感者人群数量。

文件:Graph SIR model without vital dynamics.png

图2：Yellow=Susceptible, Maroon=Infectious, Teal=Recovered黄色=易感者，栗色=感病者，青色 =康复者

Each member of the population typically progresses from susceptible to infectious to removed. This can be shown as a flow diagram in which the boxes represent the different compartments and the arrows the transition between compartments, i.e.

Each member of the population typically progresses from susceptible to infectious to removed. This can be shown as a flow diagram in which the boxes represent the different compartments and the arrows the transition between compartments, i.e.

人群中的每个成员通常由易感者转变为感染者，然后转化为康复者。这可以显示为一个流程图，在这个流程图中，方框代表不同的类别，箭头代表类别之间的过渡，即：

文件:SIR Flow Diagram.png

图3：States in an SIR epidemic model and the rates at which individuals transition between them SIR流行病模型中的状态，以及个人在不同类别之间转换的比率

Transition rates

传染率

For the full specification of the model, the arrows should be labeled with the transition rates between compartments. Between S and I, the transition rate is assumed to be d(S/N)/dt = -βSI/N², where N is the total population, β is the average number of contacts per person per time, multiplied by the probability of disease transmission in a contact between a susceptible and an infectious subject, and SI/N² is the fraction of those contacts between an infectious and susceptible individual which result in the susceptible person becoming infected. (This is mathematically similar to the law of mass action in chemistry in which random collisions between molecules result in a chemical reaction and the fractional rate is proportional to the concentration of the two reactants).

For the full specification of the model, the arrows should be labeled with the transition rates between compartments. Between S and I, the transition rate is assumed to be d(S/N)/dt = -βSI/N², where N is the total population, β is the average number of contacts per person per time, multiplied by the probability of disease transmission in a contact between a susceptible and an infectious subject, and SI/N² is the fraction of those contacts between an infectious and susceptible individual which result in the susceptible person becoming infected. (This is mathematically similar to the law of mass action in chemistry in which random collisions between molecules result in a chemical reaction and the fractional rate is proportional to the concentration of the two reactants).

为了完整说明这个模型，箭头上标明了类别之间的传染率。在S和I之间，传染率假定为[math]\displaystyle{ d\left( S/N \right) /dt=-\beta SI/N^2 }[/math]，其中 [math]\displaystyle{ N }[/math] 是总人口，[math]\displaystyle{ β }[/math]是平均每人每次接触的数量乘以易感者和感病者之间接触传播疾病的概率，[math]\displaystyle{ SI/N^2 }[/math] 是易感个体和染病个体之间接触的比例，这种接触导致易感个体转化为染病个体。(这在数学上与化学中的质量作用定律the law of mass action类似，在这个定律中，分子之间的随机碰撞导致化学反应，反应比例与两种反应物的浓度成正比)。

Between I and R, the transition rate is assumed to be proportional to the number of infectious individuals which is γI. This is equivalent to assuming that the probability of an infectious individual recovering in any time interval dt is simply γdt. If an individual is infectious for an average time period D, then γ = 1/D. This is also equivalent to the assumption that the length of time spent by an individual in the infectious state is a random variable with an exponential distribution. The "classical" SIR model may be modified by using more complex and realistic distributions for the I-R transition rate (e.g the Erlang distribution).

Between I and R, the transition rate is assumed to be proportional to the number of infectious individuals which is γI. This is equivalent to assuming that the probability of an infectious individual recovering in any time interval dt is simply γdt. If an individual is infectious for an average time period D, then γ = 1/D. This is also equivalent to the assumption that the length of time spent by an individual in the infectious state is a random variable with an exponential distribution. The "classical" SIR model may be modified by using more complex and realistic distributions for the I-R transition rate (e.g the Erlang distribution).

在 I 和 R 之间，假设转化率与染病者的数目成正比，即[math]\displaystyle{ γI }[/math]。这相当于假设一个染病者在任何时间间隔[math]\displaystyle{ dt }[/math]内恢复的概率简化为[math]\displaystyle{ γdt }[/math]。如果平均每个人在时间段[math]\displaystyle{ D }[/math]内具有传染性，那么[math]\displaystyle{ γ= 1/D }[/math]。这也相当于假设一个人在感染状态下的持续时间长度是一个服从指数分布的随机变量。“经典的” SIR 模型可以通过更加复杂和贴近现实的分布来修正I-R 传染率(例如爱尔郎分布Erlang distribution)。

For the special case in which there is no removal from the infectious compartment (γ=0), the SIR model reduces to a very simple SI model, which has a logistic solution, in which every individual eventually becomes infected.

For the special case in which there is no removal from the infectious compartment (γ=0), the SIR model reduces to a very simple SI model, which has a logistic solution, in which every individual eventually becomes infected.

对于没有染病者康复的特殊情况（[math]\displaystyle{ γ=0 }[/math]） ，SIR 模型就简化为一个非常简单的 SI 模型，该模型具有一个逻辑解，即其中每个个体最终都会被感染。

The SIR model without vital dynamics

缺少生命动力学的SIR模型

The dynamics of an epidemic, for example, the flu, are often much faster than the dynamics of birth and death, therefore, birth and death are often omitted in simple compartmental models. The SIR system without so-called vital dynamics (birth and death, sometimes called demography) described above can be expressed by the following set of ordinary differential equations:

The dynamics of an epidemic, for example, the flu, are often much faster than the dynamics of birth and death, therefore, birth and death are often omitted in simple compartmental models. The SIR system without so-called vital dynamics (birth and death, sometimes called demography) described above can be expressed by the following set of ordinary differential equations:

流行病导致的动态变化，例如流感，往往比出生和死亡的导致的动态变化更快，因此，出生和死亡往往被简单的仓室模型comparenmental models所忽略。没有上述所谓的生命动力学(出生和死亡，有时称为人口统计学)的 SIR 系统可以用下列一组常微分方程表示:

[math]\displaystyle{ \begin{align} & \frac{dS}{dt} = - \frac{\beta I S}{N}, \\[6pt] & \frac{dI}{dt} = \frac{\beta I S}{N}- \gamma I, \\[6pt] & \frac{dR}{dt} = \gamma I, \end{align} }[/math]

where [math]\displaystyle{ S }[/math] is the stock of susceptible population, [math]\displaystyle{ I }[/math] is the stock of infected, [math]\displaystyle{ R }[/math] is the stock of removed population (either by death or recovery), and [math]\displaystyle{ N }[/math] is the sum of these three.

where [math]\displaystyle{ S }[/math] is the stock of susceptible population, [math]\displaystyle{ I }[/math] is the stock of infected, [math]\displaystyle{ R }[/math] is the stock of removed population (either by death or recovery), and [math]\displaystyle{ N }[/math] is the sum of these three.

其中，[math]\displaystyle{ S }[/math] 是易感人群的数量，[math]\displaystyle{ I }[/math] 是染病人群的数量，[math]\displaystyle{ R }[/math] 是康复人群的数量(死亡或康复) ，[math]\displaystyle{ N }[/math] 是这三者的总和。

This model was for the first time proposed by William Ogilvy Kermack and Anderson Gray McKendrick as a special case of what we now call Kermack–McKendrick theory, and followed work McKendrick had done with Ronald Ross.

This model was for the first time proposed by William Ogilvy Kermack and Anderson Gray McKendrick as a special case of what we now call Kermack–McKendrick theory, and followed work McKendrick had done with Ronald Ross.

这个模型是William Ogilvy Kermack和Anderson Gray McKendrick首次提出的，我们这里的模型是Kermark - McKendrick理论的一个特例，它是在McKendrick与Ronald Ross合作的基础上提出的。

This system is non-linear, however it is possible to derive its analytic solution in implicit form. Firstly note that from:

This system is non-linear, however it is possible to derive its analytic solution in implicit form. Firstly note that from:

这个系统是非线性的，但是可以推导出隐式的解析解analytic solution。首先:

[math]\displaystyle{ \frac{dS}{dt} + \frac{dI}{dt} + \frac{dR}{dt} = 0, }[/math]

it follows that:

it follows that:

接下来是:

[math]\displaystyle{ S(t) + I(t) + R(t) = \text{constant} = N, }[/math]

expressing in mathematical terms the constancy of population [math]\displaystyle{ N }[/math]. Note that the above relationship implies that one need only study the equation for two of the three variables.

expressing in mathematical terms the constancy of population [math]\displaystyle{ N }[/math]. Note that the above relationship implies that one need only study the equation for two of the three variables.

用数学形式来体现人口 [math]\displaystyle{ N }[/math]的稳定性。注意，上述关系意味着只需要在方程中研究三个变量中的两个。

Secondly, we note that the dynamics of the infectious class depends on the following ratio:

Secondly, we note that the dynamics of the infectious class depends on the following ratio:

其次，我们注意到不同种类人群的动态取决于以下比例:

[math]\displaystyle{ R_0 = \frac{\beta}{\gamma}, }[/math]

the so-called basic reproduction number (also called basic reproduction ratio). This ratio is derived as the expected number of new infections (these new infections are sometimes called secondary infections) from a single infection in a population where all subjects are susceptible. This idea can probably be more readily seen if we say that the typical time between contacts is [math]\displaystyle{ T_{c} = \beta^{-1} }[/math], and the typical time until removal is [math]\displaystyle{ T_{r} = \gamma^{-1} }[/math]. From here it follows that, on average, the number of contacts by an infectious individual with others before the infectious has been removed is: [math]\displaystyle{ T_{r}/T_{c}. }[/math]

the so-called basic reproduction number (also called basic reproduction ratio). This ratio is derived as the expected number of new infections (these new infections are sometimes called secondary infections) from a single infection in a population where all subjects are susceptible. This idea can probably be more readily seen if we say that the typical time between contacts is [math]\displaystyle{ T_{c} = \beta^{-1} }[/math], and the typical time until removal is [math]\displaystyle{ T_{r} = \gamma^{-1} }[/math]. From here it follows that, on average, the number of contacts by an infectious individual with others before the infectious has been removed is: [math]\displaystyle{ T_{r}/T_{c}. }[/math]

所谓的基本再生数basic reprodution numer(亦称基本再生率)。该比率是在所有受试者都是易感类别的人群中，由一次感染引起的新感染的预期数量(这些新感染有时称为二次感染)得出的。如果我们说两次感染之间的典型时间是 [math]\displaystyle{ T_{c}=\beta^{-1} }[/math]，而康复之前的典型时间是 [math]\displaystyle{ T_{r}=\gamma^{-1} }[/math] ，那么这个想法可能更容易被理解。由此可以得出，平均而言，在感染者康复之前，感染者与其他人的接触次数为: [math]\displaystyle{ T_{r}/T_{c}. }[/math]

By dividing the first differential equation by the third, separating the variables and integrating we get

By dividing the first differential equation by the third, separating the variables and integrating we get

通过将第一个微分方程differential equation除以第三个微分方程，分离变量并进行积分，我们得到

[math]\displaystyle{ S(t) = S(0) e^{-R_0(R(t) - R(0))/N}, }[/math]

where [math]\displaystyle{ S(0) }[/math] and [math]\displaystyle{ R(0) }[/math] are the initial numbers of, respectively, susceptible and removed subjects.

where [math]\displaystyle{ S(0) }[/math] and [math]\displaystyle{ R(0) }[/math] are the initial numbers of, respectively, susceptible and removed subjects.

其中，[math]\displaystyle{ S(0) }[/math]和[math]\displaystyle{ R(0) }[/math]分别是易感者和康复者的初始人数。

Writing [math]\displaystyle{ s_0 = S(0) / N }[/math] for the initial proportion of susceptible individuals, and

Writing [math]\displaystyle{ s_0 = S(0) / N }[/math] for the initial proportion of susceptible individuals, and

易感人群的初始比例[math]\displaystyle{ S_0=S(0)/N }[/math]，以及

[math]\displaystyle{ s_\infty = S(\infty) / N }[/math] and [math]\displaystyle{ r_\infty = R(\infty) / N }[/math] for the proportion of susceptible and removed individuals respectively in the limit [math]\displaystyle{ t \to \infty, }[/math] one has

[math]\displaystyle{ s_\infty = S(\infty) / N }[/math] and [math]\displaystyle{ r_\infty = R(\infty) / N }[/math] for the proportion of susceptible and removed individuals respectively in the limit [math]\displaystyle{ t \to \infty, }[/math] one has

在[math]\displaystyle{ t \to \infty, }[/math]的极限条件下，易感个体和染病个体的比例分别为[math]\displaystyle{ s_\infty = S(\infty) / N }[/math] 和 [math]\displaystyle{ r_\infty = R(\infty) / N }[/math]。此时有

[math]\displaystyle{ s_\infty = 1 - r_\infty = s_0 e^{-R_0(r_\infty - r_0)} }[/math]

(note that the infectious compartment empties in this limit).

(note that the infectious compartment empties in this limit).

(请注意，在这个极限时，没有染病者)。

This transcendental equation has a solution in terms of the Lambert W function, namely

This transcendental equation has a solution in terms of the Lambert function, namely

这个超越方程有一个Lambert函数解，即

[math]\displaystyle{ s_\infty = 1-r_\infty = - R_0^{-1}\, W(-s_0 R_0 e^{-R_0(1-r_0)}). }[/math]

This shows that at the end of an epidemic, unless [math]\displaystyle{ s_0=0 }[/math], not all individuals of the population have been removed, so some must remain susceptible. This means that the end of an epidemic is caused by the decline in the number of infectious individuals rather than an absolute lack of susceptible subjects.

This shows that at the end of an epidemic, unless [math]\displaystyle{ s_0=0 }[/math], not all individuals of the population have been removed, so some must remain susceptible. This means that the end of an epidemic is caused by the decline in the number of infectious individuals rather than an absolute lack of susceptible subjects.

这表明，在流行病结束时，除非[math]\displaystyle{ s_0=0 }[/math]，否则一定有个体没有转化为康复状态，一些人仍然是易感类别。这意味着，传染病的结束是由于染病者的减少，而不是完全由于没有易感者。

The role of both the basic reproduction number and the initial susceptibility are extremely important. In fact, upon rewriting the equation for infectious individuals as follows:

The role of both the basic reproduction number and the initial susceptibility are extremely important. In fact, upon rewriting the equation for infectious individuals as follows:

基本再生数basic reprodution numer和最初的易感人群比例的作用都极其重要。事实上，将易感者人群数量变化的等式可以重写成如下形式:

[math]\displaystyle{ \frac{dI}{dt} = \left(R_0 \frac{S}{N} - 1\right) \gamma I, }[/math]

it yields that if:

it yields that if:

如果:

[math]\displaystyle{ R_{0} \cdot S(0) \gt N, }[/math]

then:

then:

那么：

[math]\displaystyle{ \frac{dI}{dt}(0) \gt 0 , }[/math]

i.e., there will be a proper epidemic outbreak with an increase of the number of the infectious (which can reach a considerable fraction of the population). On the contrary, if

i.e., there will be a proper epidemic outbreak with an increase of the number of the infectious (which can reach a considerable fraction of the population). On the contrary, if

也就是说，随着染病者数量的增加(达到人口相当大的一个比例) ，将会有一场流行病的爆发。相反，如果

[math]\displaystyle{ R_{0} \cdot S(0) \lt N, }[/math]

then

then

那么

[math]\displaystyle{ \frac{dI}{dt}(0) \lt 0 , }[/math]

i.e., independently from the initial size of the susceptible population the disease can never cause a proper epidemic outbreak. As a consequence, it is clear that both the basic reproduction number and the initial susceptibility are extremely important.

i.e., independently from the initial size of the susceptible population the disease can never cause a proper epidemic outbreak. As a consequence, it is clear that both the basic reproduction number and the initial susceptibility are extremely important.

也就是说，这种情况与易感者的初始规模无关，这时疾病将不会引起流行病的爆发。因此，很明显，基本再生数basic reprodution numer和最初的易感者人群数量都极其重要。

The force of infection

感染力

Note that in the above model the function:

Note that in the above model the function:

注意，在上面的模型中，函数:

[math]\displaystyle{ F = \beta I, }[/math]

models the transition rate from the compartment of susceptible individuals to the compartment of infectious individuals, so that it is called the force of infection. However, for large classes of communicable diseases it is more realistic to consider a force of infection that does not depend on the absolute number of infectious subjects, but on their fraction (with respect to the total constant population [math]\displaystyle{ N }[/math]):

models the transition rate from the compartment of susceptible individuals to the compartment of infectious individuals, so that it is called the force of infection. However, for large classes of communicable diseases it is more realistic to consider a force of infection that does not depend on the absolute number of infectious subjects, but on their fraction (with respect to the total constant population [math]\displaystyle{ N }[/math]):

建立了从易感者到染病者的传染率模型，这被称为感染力the force of infection。然而，对于大部分传染病来说，更现实的做法是考虑一种感染力the force of infection，这种传染力并不取决于染病者人群的绝对数量，而是取决于染病者人群的比例(就总人口[math]\displaystyle{ N }[/math]而言) :

[math]\displaystyle{ F = \beta \frac{I}{N} . }[/math]

Capasso and, afterwards, other authors have proposed nonlinear forces of infection to model more realistically the contagion process.

Capasso and, afterwards, other authors have proposed nonlinear forces of infection to model more realistically the contagion process.

Capasso和后来的其他作者提出了非线性的感染力，以建模更现实的传染过程。

Exact analytical solutions to the SIR model

SIR 模型的精确解析解

In 2014, Harko and coauthors derived an exact analytical solution to the SIR model. In the case without vital dynamics setup, for [math]\displaystyle{ \mathcal{S}(u)=S(t) }[/math], etc., it corresponds to the following time parametrization

In 2014, Harko and coauthors derived an exact analytical solution to the SIR model. In the case without vital dynamics setup, for [math]\displaystyle{ \mathcal{S}(u)=S(t) }[/math], etc., it corresponds to the following time parametrization

2014年，Harko 和合作者推导出了 SIR 模型的精确解析解analytical solution。在没有考虑正常出生和死亡生命动力学的情况下，对于 [math]\displaystyle{ \mathcal{S}(u) =S(t) }[/math] 等，它对应以下时间参数化

[math]\displaystyle{ \mathcal{S}(u)= S(0)u }[/math]

[math]\displaystyle{ \mathcal{I}(u)= N -\mathcal{R}(u)-\mathcal{S}(u) }[/math]

[math]\displaystyle{ \mathcal{R}(u)=R(0) -\rho \ln(u) }[/math]

for

for

为了

[math]\displaystyle{ t= \frac{N}{\beta}\int_u^1 \frac{du^*}{u^*\mathcal{I}(u^*)} , \quad \rho=\frac{\gamma N}{\beta}, }[/math]

with initial conditions

with initial conditions

有初始条件

[math]\displaystyle{ (\mathcal{S}(1),\mathcal{I}(1),\mathcal{R}(1))=(S(0),N -R(0)-S(0),R(0)), \quad u_T\lt u\lt 1, }[/math]

where [math]\displaystyle{ u_T }[/math] satisfies [math]\displaystyle{ \mathcal{I}(u_T)=0 }[/math]. By the transcendental equation for [math]\displaystyle{ R_{\infty} }[/math] above, it follows that [math]\displaystyle{ u_T=e^{-(R_{\infty}-R(0))/\rho}(=S_{\infty}/S(0) }[/math], if [math]\displaystyle{ S(0) \neq 0) }[/math] and [math]\displaystyle{ I_{\infty}=0 }[/math].

where [math]\displaystyle{ u_T }[/math] satisfies [math]\displaystyle{ \mathcal{I}(u_T)=0 }[/math]. By the transcendental equation for [math]\displaystyle{ R_{\infty} }[/math] above, it follows that [math]\displaystyle{ u_T=e^{-(R_{\infty}-R(0))/\rho}(=S_{\infty}/S(0) }[/math], if [math]\displaystyle{ S(0) \neq 0) }[/math] and [math]\displaystyle{ I_{\infty}=0 }[/math].

其中[math]\displaystyle{ u_T }[/math]满足[math]\displaystyle{ \mathcal{I}(u_T)=0 }[/math]。根据上面的超越方程transcendental equation [math]\displaystyle{ R_{\infty} }[/math] ，如果[math]\displaystyle{ S(0) \neq 0) }[/math] and [math]\displaystyle{ I_{\infty}=0 }[/math]，那么[math]\displaystyle{ u_T=e^{-(R_{\infty}-R(0))/\rho}(=S_{\infty}/S(0) }[/math]。

An equivalent analytical solution found by Miller yields

An equivalent analytical solution found by Miller yields

等价的解析解analytical solution由米勒发现

[math]\displaystyle{ \begin{align} S(t) & = S(0) e^{-\xi(t)} \\[8pt] I(t) & = N-S(t)-R(t) \\[8pt] R(t) & = R(0) + \rho \xi(t) \\[8pt] \xi(t) & = \frac{\beta}{N}\int_0^t I(t^*) \, dt^* \end{align} }[/math]

Here [math]\displaystyle{ \xi(t) }[/math] can be interpreted as the expected number of transmissions an individual has received by time [math]\displaystyle{ t }[/math]. The two solutions are related by [math]\displaystyle{ e^{-\xi(t)} = u }[/math].

Here [math]\displaystyle{ \xi(t) }[/math] can be interpreted as the expected number of transmissions an individual has received by time [math]\displaystyle{ t }[/math]. The two solutions are related by [math]\displaystyle{ e^{-\xi(t)} = u }[/math].

在这里，[math]\displaystyle{ \xi(t) }[/math]可以解释为随着时间[math]\displaystyle{ t }[/math]变化，一个人预期接触到的染病者数量。这两个解是通过[math]\displaystyle{ e^{-\xi(t)} = u }[/math]相关联的。

Effectively the same result can be found in the original work by Kermack and McKendrick.

Effectively the same result can be found in the original work by Kermack and McKendrick.

实际上，同样的结果可以在Kermack 和 McKendrick的原著中找到。

These solutions may be easily understood by noting that all of the terms on the right-hand sides of the original differential equations are proportional to [math]\displaystyle{ I }[/math]. The equations may thus be divided through by [math]\displaystyle{ I }[/math], and the time rescaled so that the differential operator on the left-hand side becomes simply [math]\displaystyle{ d/d\tau }[/math], where [math]\displaystyle{ d\tau=I dt }[/math], i.e. [math]\displaystyle{ \tau=\int I dt }[/math]. The differential equations are now all linear, and the third equation, of the form [math]\displaystyle{ dR/d\tau = }[/math] const., shows that [math]\displaystyle{ \tau }[/math] and [math]\displaystyle{ R }[/math] (and [math]\displaystyle{ \xi }[/math] above) are simply linearly related.

These solutions may be easily understood by noting that all of the terms on the right-hand sides of the original differential equations are proportional to [math]\displaystyle{ I }[/math]. The equations may thus be divided through by [math]\displaystyle{ I }[/math], and the time rescaled so that the differential operator on the left-hand side becomes simply [math]\displaystyle{ d/d\tau }[/math], where [math]\displaystyle{ d\tau=I dt }[/math], i.e. [math]\displaystyle{ \tau=\int I dt }[/math]. The differential equations are now all linear, and the third equation, of the form [math]\displaystyle{ dR/d\tau = }[/math] const., shows that [math]\displaystyle{ \tau }[/math] and [math]\displaystyle{ R }[/math] (and [math]\displaystyle{ \xi }[/math] above) are simply linearly related.

注意到原微分方程右边的所有项都与[math]\displaystyle{ I }[/math]成正比，这些解就很容易理解了。这样，方程组就可以通过[math]\displaystyle{ I }[/math]来分解，通过时间重缩放，使得左边的微分算子变成[math]\displaystyle{ d/d\tau }[/math]，其中[math]\displaystyle{ d\tau=I dt }[/math]，即 [math]\displaystyle{ \tau=\int I dt }[/math]。这些微分方程现在都是线性的，而第三个方程，即形式为[math]\displaystyle{ dR/d\tau = }[/math] 的常量，表明 [math]\displaystyle{ \tau }[/math]和[math]\displaystyle{ R }[/math](和[math]\displaystyle{ \xi }[/math])仅仅是线性关系。

The SIR model with vital dynamics and constant population

具有生命动力学和稳定人口的SIR模型

Consider a population characterized by a death rate [math]\displaystyle{ \mu }[/math] and birth rate [math]\displaystyle{ \Lambda }[/math], and where a communicable disease is spreading. The model with mass-action transmission is:

Consider a population characterized by a death rate [math]\displaystyle{ \mu }[/math] and birth rate [math]\displaystyle{ \Lambda }[/math], and where a communicable disease is spreading. The model with mass-action transmission is:

考虑有死亡率[math]\displaystyle{ \mu }[/math]和出生率[math]\displaystyle{ \Lambda }[/math]的人群，以及正在传播的传染病。具有质量作用传递的模型是:

[math]\displaystyle{ \begin{align} \frac{dS}{dt} & = \Lambda - \mu S - \frac{\beta I S}{N} \\[8pt] \frac{dI}{dt} & = \frac{\beta I S}{N} - \gamma I -\mu I \\[8pt] \frac{dR}{dt} & = \gamma I - \mu R \end{align} }[/math]

for which the disease-free equilibrium (DFE) is:

for which the disease-free equilibrium (DFE) is:

其无病平衡点the disease-free quilibrium为:

[math]\displaystyle{ \left(S(t),I(t),R(t)\right) =\left(\frac{\Lambda}{\mu},0,0\right). }[/math]

In this case, we can derive a basic reproduction number:

In this case, we can derive a basic reproduction number:

在这种情况下，我们可以得出一个基本再生数:

[math]\displaystyle{ R_0 = \frac{ \beta\Lambda }{\mu(\mu+\gamma)}, }[/math]

which has threshold properties. In fact, independently from biologically meaningful initial values, one can show that:

which has threshold properties. In fact, independently from biologically meaningful initial values, one can show that:

这种基本再生数具有临界性质。事实上，独立于具有生物学意义的初始值，我们可以证明:

[math]\displaystyle{ R_0 \le 1 \Rightarrow \lim_{t \to \infty} (S(t),I(t),R(t)) = \textrm{DFE} = \left(\frac{\Lambda}{\mu},0,0\right) }[/math]

[math]\displaystyle{ R_0 \gt 1 , I(0)\gt 0 \Rightarrow \lim_{t \to \infty} (S(t),I(t),R(t)) = \textrm{EE} = \left(\frac{\gamma+\mu}{\beta},\frac{\mu}{\beta}\left(R_0-1\right), \frac{\gamma}{\beta} \left(R_0-1\right)\right). }[/math]

The point EE is called the Endemic Equilibrium (the disease is not totally eradicated and remains in the population). With heuristic arguments, one may show that [math]\displaystyle{ R_{0} }[/math] may be read as the average number of infections caused by a single infectious subject in a wholly susceptible population, the above relationship biologically means that if this number is less than or equal to one the disease goes extinct, whereas if this number is greater than one the disease will remain permanently endemic in the population.

The point EE is called the Endemic Equilibrium (the disease is not totally eradicated and remains in the population). With heuristic arguments, one may show that [math]\displaystyle{ R_{0} }[/math] may be read as the average number of infections caused by a single infectious subject in a wholly susceptible population, the above relationship biologically means that if this number is less than or equal to one the disease goes extinct, whereas if this number is greater than one the disease will remain permanently endemic in the population.

EE点被称为地方病平衡点(这种疾病还没有完全根除，仍然存在于人群中)。通过启发式的论证，表明[math]\displaystyle{ R_{0} }[/math]可以理解为在完全易感人群中，由一个染病者引起的平均感染人数，上述关系在生物学上意味着，如果这个数字小于或等于1，这种疾病就会灭绝，而如果这个数字大于1，这种疾病就会在人群中永久地流行下去。

Variations on the basic SIR model

基础SIR模型的变化

The SIS model

SIS模型

图4：Yellow=Susceptible, Maroon=Infected黄色=易感，栗色=感染

Some infections, for example, those from the common cold and influenza, do not confer any long-lasting immunity. Such infections do not give immunity upon recovery from infection, and individuals become susceptible again.

Some infections, for example, those from the common cold and influenza, do not confer any long-lasting immunity. Such infections do not give immunity upon recovery from infection, and individuals become susceptible again.

有些传染病，例如普通感冒和流感，并不能产生持久的免疫力。这种传染病在感染康复后不会产生免疫力，个体会再次变为易感染类型。

SIS compartmental model

【图5：SIS compartmental modelSIS传染病模型】

We have the model:

We have the model:

我们有以下模型:

[math]\displaystyle{ \begin{align} \frac{dS}{dt} & = - \frac{\beta S I}{N} + \gamma I \\[6pt] \frac{dI}{dt} & = \frac{\beta S I}{N} - \gamma I \end{align} }[/math]

Note that denoting with N the total population it holds that:

Note that denoting with N the total population it holds that:

注意，用N表示人群总数:

[math]\displaystyle{ \frac{dS}{dt} + \frac{dI}{dt} = 0 \Rightarrow S(t)+I(t) = N }[/math].

It follows that:

It follows that:

由此可见:

[math]\displaystyle{ \frac{dI}{dt} = (\beta - \gamma) I - \frac{\beta}{N} I^2 }[/math],

i.e. the dynamics of infectious is ruled by a logistic function, so that [math]\displaystyle{ \forall I(0) \gt 0 }[/math]:

i.e. the dynamics of infectious is ruled by a logistic function, so that [math]\displaystyle{ \forall I(0) \gt 0 }[/math]:

也就是。传染病的动态性是由Logistic函数控制的，所以对于所有的[math]\displaystyle{ \forall I(0) \gt 0 }[/math]:

[math]\displaystyle{ \begin{align} & \frac{\beta}{\gamma} \le 1 \Rightarrow \lim_{t \to +\infty}I(t)=0, \\[6pt] & \frac{\beta}{\gamma} \gt 1 \Rightarrow \lim_{t \to +\infty}I(t) = \left(1 - \frac{\gamma}{\beta} \right) N. \end{align} }[/math]

It is possible to find an analytical solution to this model (by making a transformation of variables: [math]\displaystyle{ I = y^{-1} }[/math] and substituting this into the mean-field equations), such that the basic reproduction rate is greater than unity. The solution is given as

It is possible to find an analytical solution to this model (by making a transformation of variables: [math]\displaystyle{ I = y^{-1} }[/math] and substituting this into the mean-field equations), such that the basic reproduction rate is greater than unity. The solution is given as

这个模型可以找到一个解析解analytical solution(通过对变量进行变换：[math]\displaystyle{ I = y^{-1} }[/math] 并将其代入平均场方程the mean-field euations) ，使基本再生率大于单位数。给出了解如下:

[math]\displaystyle{ I(t) = \frac{I_\infty}{1+V e^{-\chi t}} }[/math].

where [math]\displaystyle{ I_\infty = (1 -\gamma/\beta)N }[/math] is the endemic infectious population, [math]\displaystyle{ \chi = \beta-\gamma }[/math], and [math]\displaystyle{ V = I_\infty/I_0 - 1 }[/math]. As the system is assumed to be closed, the susceptible population is then [math]\displaystyle{ S(t) = N - I(t) }[/math].

where [math]\displaystyle{ I_\infty = (1 -\gamma/\beta)N }[/math] is the endemic infectious population, [math]\displaystyle{ \chi = \beta-\gamma }[/math], and [math]\displaystyle{ V = I_\infty/I_0 - 1 }[/math]. As the system is assumed to be closed, the susceptible population is then [math]\displaystyle{ S(t) = N - I(t) }[/math].

其中 [math]\displaystyle{ I_\infty = (1 -\gamma/\beta)N }[/math]是地方性传染病人群数量，[math]\displaystyle{ \chi = \beta-\gamma }[/math]， [math]\displaystyle{ V = I_\infty/I_0 - 1 }[/math]。假设系统是封闭的，那么易感者人群数量是[math]\displaystyle{ S(t) = N - I(t) }[/math]。

As a special case, one obtains the usual logistic function by assuming [math]\displaystyle{ \gamma=0 }[/math]. This can be also considered in the SIR model with [math]\displaystyle{ R=0 }[/math], i.e. no removal will take place. That is the SI model. The differential equation system using [math]\displaystyle{ S=N-I }[/math] thus reduces to:

As a special case, one obtains the usual logistic function by assuming [math]\displaystyle{ \gamma=0 }[/math]. This can be also considered in the SIR model with [math]\displaystyle{ R=0 }[/math], i.e. no removal will take place. That is the SI model. The differential equation system using [math]\displaystyle{ S=N-I }[/math] thus reduces to:

作为一种特殊情况，通过假设[math]\displaystyle{ R=0 }[/math]得到通常的 Logistic函数。这也可以在 SIR 模型中考虑，该模型有[math]\displaystyle{ R=0 }[/math]，即没有康复者。这就是SI模型。微分方程系统使用[math]\displaystyle{ S=N-I }[/math]，因此可以简化为:

[math]\displaystyle{ \frac{dI}{dt} \propto I\cdot (N-I). }[/math]

In the long run, in this model, all individuals will become infected.

In the long run, in this model, all individuals will become infected.

从长远来看，在这种模式下，所有的个体都会被感染。

The SIRD model

SIRD模型

Diagram of the SIRD model with initial values [math]\displaystyle{ S(0)=997, I(0)=3, R(0)=0 }[/math] and the rates for infection [math]\displaystyle{ \beta=0.4 }[/math], recovery [math]\displaystyle{ \gamma=0.035 }[/math] and mortality [math]\displaystyle{ \mu=0.005 }[/math]

Diagram of the SIRD model with initial values [math]\displaystyle{ S(0)=997, I(0)=3, R(0)=0 }[/math] and the rates for infection [math]\displaystyle{ \beta=0.4 }[/math], recovery [math]\displaystyle{ \gamma=0.035 }[/math] and mortality [math]\displaystyle{ \mu=0.005 }[/math]

【图6：Diagram of the SIRD model with initial values [math]\displaystyle{ S(0)=997, I(0)=3, R(0)=0 }[/math] and the rates for infection [math]\displaystyle{ \beta=0.4 }[/math], recovery [math]\displaystyle{ \gamma=0.035 }[/math] and mortality [math]\displaystyle{ \mu=0.005 }[/math]SIRD模型示意图，初始值[math]\displaystyle{ S(0)=997，I(0)=3, R(0)=0 }[/math]，感染率[math]\displaystyle{ \beta=0.4 }[/math]，康复率[math]\displaystyle{ \gamma=0.035 }[/math]，死亡率 [math]\displaystyle{ \mu=0.005 }[/math]】

Animation of the SIRD model with initial values [math]\displaystyle{ S(0)=997, I(0)=3, R(0)=0 }[/math], initial rate for infection [math]\displaystyle{ \beta=0.5 }[/math] and constant rates for recovery [math]\displaystyle{ \gamma=0.035 }[/math] and mortality [math]\displaystyle{ \mu=0.005 }[/math]. If there is neither medicine nor vaccination available, it is only possible to reduce the infection rate (often referred to as „flattening the curve“) by appropriate measures (e. g. „social distancing“). This animation shows the impact of reducing the infection rate by 76 % (from [math]\displaystyle{ \beta=0.5 }[/math] down to [math]\displaystyle{ \beta=0.12 }[/math]).

Animation of the SIRD model with initial values [math]\displaystyle{ S(0)=997, I(0)=3, R(0)=0 }[/math], initial rate for infection [math]\displaystyle{ \beta=0.5 }[/math] and constant rates for recovery [math]\displaystyle{ \gamma=0.035 }[/math] and mortality [math]\displaystyle{ \mu=0.005 }[/math]. If there is neither medicine nor vaccination available, it is only possible to reduce the infection rate (often referred to as „flattening the curve“) by appropriate measures (e. g. „social distancing“). This animation shows the impact of reducing the infection rate by 76 % (from [math]\displaystyle{ \beta=0.5 }[/math] down to [math]\displaystyle{ \beta=0.12 }[/math]).

【图7：Animation of the SIRD model with initial values [math]\displaystyle{ S(0)=997, I(0)=3, R(0)=0 }[/math], initial rate for infection [math]\displaystyle{ \beta=0.5 }[/math] and constant rates for recovery [math]\displaystyle{ \gamma=0.035 }[/math] and mortality [math]\displaystyle{ \mu=0.005 }[/math]. If there is neither medicine nor vaccination available, it is only possible to reduce the infection rate (often referred to as „flattening the curve“) by appropriate measures (e. g. „social distancing“). This animation shows the impact of reducing the infection rate by 76 % (from [math]\displaystyle{ \beta=0.5 }[/math] down to [math]\displaystyle{ \beta=0.12 }[/math])SIRD模型动画，初始值[math]\displaystyle{ S(0)=997I(0)=3, R(0)=0 }[/math]，初始感染率 [math]\displaystyle{ \beta=0.5 }[/math]和恒定康复率 [math]\displaystyle{ \gamma=0.035 }[/math] 和死亡率[math]\displaystyle{ \mu=0.005 }[/math]。如果既没有药物也没有疫苗可用，只有通过适当的措施(例如“社会距离”)才有可能降低感染率(通常称为“平缓曲线”)。这个动画展示了降低感染率76% 的效果(从 [math]\displaystyle{ \beta=0.5 }[/math]下降到[math]\displaystyle{ \beta=0.12 }[/math])。】

The Susceptible-Infectious-Recovered-Deceased-Model differentiates between Recovered (meaning specifically individuals having survived the disease and now immune) and Deceased.模板:Cn This model uses the following system of differential equations:

The Susceptible-Infectious-Recovered-Deceased-Model differentiates between Recovered (meaning specifically individuals having survived the disease and now immune) and Deceased. This model uses the following system of differential equations:

易感-染病-康复-死亡-模型区分了康复者(特别是指从疾病中存活下来并且现已免疫的个体)和死亡者。该模型使用了下列微分方程组:

[math]\displaystyle{ \begin{align} & \frac{dS}{dt} = - \frac{\beta I S}{N}, \\[6pt] & \frac{dI}{dt} = \frac{\beta I S}{N} - \gamma I - \mu I, \\[6pt] & \frac{dR}{dt} = \gamma I, \\[6pt] & \frac{dD}{dt} = \mu I, \end{align} }[/math]

where [math]\displaystyle{ \beta, \gamma, \mu }[/math] are the rates of infection, recovery, and mortality, respectively.

where [math]\displaystyle{ \beta, \gamma, \mu }[/math] are the rates of infection, recovery, and mortality, respectively.

这里[math]\displaystyle{ \beta, \gamma, \mu }[/math]分别是感染率，康复率和死亡率。

The MSIR model

MSIR模型

For many infections, including measles, babies are not born into the susceptible compartment but are immune to the disease for the first few months of life due to protection from maternal antibodies (passed across the placenta and additionally through colostrum). This is called passive immunity. This added detail can be shown by including an M class (for maternally derived immunity) at the beginning of the model.

For many infections, including measles, babies are not born into the susceptible compartment but are immune to the disease for the first few months of life due to protection from maternal antibodies (passed across the placenta and additionally through colostrum). This is called passive immunity. This added detail can be shown by including an M class (for maternally derived immunity) at the beginning of the model.

对于包括麻疹在内的许多传染病，婴儿出生时并不易感染，由于母体抗体的保护(通过胎盘和初乳传播) ，婴儿在出生后的头几个月对该疾病免疫。这叫做被动免疫。这个额外的细节可以通过在模型的开始加入一个M类型(用于母系免疫)来显示。

To indicate this mathematically, an additional compartment is added, M(t). This results in the following differential equations:

MSIR compartmental modelTo indicate this mathematically, an additional compartment is added, M(t). This results in the following differential equations:

【图8：MSIR compartmental model. To indicate this mathematically, an additional compartment is added, M(t). This results in the following differential equations:MSIR 仓室模型为了从数学上表示这一点，增加了一个额外的分类，M(t)。这导致了下列微分方程:】

[math]\displaystyle{ \begin{align} \frac{dM}{dT} & = \Lambda - \delta M - \mu M\\[8pt] \frac{dS}{dT} & = \delta M - \frac{\beta SI}{N} - \mu S\\[8pt] \frac{dI}{dT} & = \frac{\beta SI}{N} - \gamma I - \mu I\\[8pt] \frac{dR}{dT} & = \gamma I - \mu R \end{align} }[/math]

Carrier state

病原携带状态

Some people who have had an infectious disease such as tuberculosis never completely recover and continue to carry the infection, whilst not suffering the disease themselves. They may then move back into the infectious compartment and suffer symptoms (as in tuberculosis) or they may continue to infect others in their carrier state, while not suffering symptoms. The most famous example of this is probably Mary Mallon, who infected 22 people with typhoid fever. The carrier compartment is labelled C.

Some people who have had an infectious disease such as tuberculosis never completely recover and continue to carry the infection, whilst not suffering the disease themselves. They may then move back into the infectious compartment and suffer symptoms (as in tuberculosis) or they may continue to infect others in their carrier state, while not suffering symptoms. The most famous example of this is probably Mary Mallon, who infected 22 people with typhoid fever. The carrier compartment is labelled C.

一些患有肺结核等传染病的人永远不会完全康复，而是继续携带这种传染病，同时他们自己也不会患上这种疾病。他们可能回归到染病者状态并出现症状（如肺结核），或者他们可能继续以携带者的状态传染给其他人，而不出现症状。最著名的例子可能是Mary Mallon，她将伤寒传染给了22个人。携带者被标记为C。

A simple modification of previous image by Viki Male to make the word "Carrier" plainly visible.

【图9：A simple modification of previous image by Viki Male to make the word "Carrier" plainly visible.对维基马累之前形象的一个简单修改，使单词“携带者”清晰可见。】

The SEIR model

SEIR模型

For many important infections, there is a significant incubation period during which individuals have been infected but are not yet infectious themselves. During this period the individual is in compartment E (for exposed).

For many important infections, there is a significant incubation period during which individuals have been infected but are not yet infectious themselves. During this period the individual is in compartment E (for exposed).

对于许多传染病，有一段很长的疾病潜伏期，在此期间个人已经被感染，但他们自己还没有感病。在此期间，这个人是属于类别E(暴露者类型)。

SEIR compartmental model

【图10：SEIR compartmental modelSEIR传染病模型】

Assuming that the incubation period is a random variable with exponential distribution with parameter [math]\displaystyle{ a }[/math] (i.e. the average incubation period is [math]\displaystyle{ a^{-1} }[/math]), and also assuming the presence of vital dynamics with birth rate [math]\displaystyle{ \Lambda }[/math] equal to death rate [math]\displaystyle{ \mu }[/math], we have the model:

Assuming that the incubation period is a random variable with exponential distribution with parameter [math]\displaystyle{ a }[/math] (i.e. the average incubation period is [math]\displaystyle{ a^{-1} }[/math]), and also assuming the presence of vital dynamics with birth rate [math]\displaystyle{ \Lambda }[/math] equal to death rate [math]\displaystyle{ \mu }[/math], we have the model:

假设疾病潜伏期是一个随机变量，这个随机变量服从一个带有参数[math]\displaystyle{ ''a'' }[/math]的指数分布。（疾病平均潜伏期是[math]\displaystyle{ a^{-1} }[/math]），并且假设存在出生率[math]\displaystyle{ \Lambda }[/math]等于死亡率[math]\displaystyle{ \mu }[/math]的生命动力学，我们有这样的模型:

[math]\displaystyle{ \begin{align} \frac{dS}{dt} & = \Lambda - \mu S - \frac{\beta I S}{N} \\[8pt] \frac{dE}{dt} & = \frac{\beta I S}{N} - (\mu +a ) E \\[8pt] \frac{dI}{dt} & = a E - (\gamma +\mu ) I \\[8pt] \frac{dR}{dt} & = \gamma I - \mu R. \end{align} }[/math]

We have [math]\displaystyle{ S+E+I+R=N, }[/math] but this is only constant because of the (degenerate) assumption that birth and death rates are equal; in general [math]\displaystyle{ N }[/math] is a variable.

We have [math]\displaystyle{ S+E+I+R=N, }[/math] but this is only constant because of the (degenerate) assumption that birth and death rates are equal; in general [math]\displaystyle{ N }[/math] is a variable.

我们有[math]\displaystyle{ S+E+I+R=N, }[/math]，但是这是常量，因为(退化的)假设出生率和死亡率是相等的; 一般来说[math]\displaystyle{ N }[/math]是一个变量。

For this model, the basic reproduction number is:

For this model, the basic reproduction number is:

对于这种模式，基本再生数basic reproduction number是:

[math]\displaystyle{ R_0 = \frac{a}{\mu+a}\frac{\beta}{\mu+\gamma}. }[/math]

Similarly to the SIR model, also, in this case, we have a Disease-Free-Equilibrium (N,0,0,0) and an Endemic Equilibrium EE, and one can show that, independently from biologically meaningful initial conditions

Similarly to the SIR model, also, in this case, we have a Disease-Free-Equilibrium (N,0,0,0) and an Endemic Equilibrium EE, and one can show that, independently from biologically meaningful initial conditions

类似于 SIR 模型，在这种情况下，我们有一个无病平衡(N,0,0,0)和一个地方病平衡 EE，可以证明，这独立于生物学意义上的初始条件

[math]\displaystyle{ \left(S(0),E(0),I(0),R(0)\right) \in \left\{(S,E,I,R)\in [0,N]^4 : S \ge 0, E \ge 0, I\ge 0, R\ge 0, S+E+I+R = N \right\} }[/math]

it holds that:

it holds that:

它认为:

[math]\displaystyle{ R_0 \le 1 \Rightarrow \lim_{t \to +\infty} \left(S(t),E(t),I(t),R(t)\right) = DFE = (N,0,0,0), }[/math]

[math]\displaystyle{ R_0 \gt 1 , I(0)\gt 0 \Rightarrow \lim_{t \to +\infty} \left(S(t),E(t),I(t),R(t)\right) = EE. }[/math]

In case of periodically varying contact rate [math]\displaystyle{ \beta(t) }[/math] the condition for the global attractiveness of DFE is that the following linear system with periodic coefficients:

In case of periodically varying contact rate [math]\displaystyle{ \beta(t) }[/math] the condition for the global attractiveness of DFE is that the following linear system with periodic coefficients:

当接触率 < math > beta (t) </math >周期性变化时， DFE 全局吸引性的条件是，以下线性系统具有周期系数:

[math]\displaystyle{ \begin{align} \frac{dE_1}{dt} & = \beta(t) I_1 - (\gamma +a ) E_1 \\[8pt] \frac{dI_1}{dt} & = a E_1 - (\gamma +\mu ) I_1 \end{align} }[/math]

is stable (i.e. it has its Floquet's eigenvalues inside the unit circle in the complex plane).

is stable (i.e. it has its Floquet's eigenvalues inside the unit circle in the complex plane).

是稳定的(它在复平面的单位圆内有它的 Floquet 特征值)。

The SEIS model

SEIS模型

The SEIS model is like the SEIR model (above) except that no immunity is acquired at the end.

The SEIS model is like the SEIR model (above) except that no immunity is acquired at the end.

SEIS 模型类似于之前的 SEIR 模型，只是最终不会转化为免疫属性。

[math]\displaystyle{ {\color{blue}{\mathcal{S} \to \mathcal{E} \to \mathcal{I} \to \mathcal{S}}} }[/math]

In this model an infection does not leave any immunity thus individuals that have recovered return to being susceptible, moving back into the S(t) compartment. The following differential equations describe this model:

In this model an infection does not leave any immunity thus individuals that have recovered return to being susceptible, moving back into the S(t) compartment. The following differential equations describe this model:

在这个模型中，感染不会留下任何免疫性，因此染病个体恢复到易感状态，移回 s (t)状态。下列微分方程描述了这一模型:

[math]\displaystyle{ \begin{align} \frac{dS}{dT} & = \Lambda - \frac{\beta SI}{N} - \mu S + \gamma I \\[6pt] \frac{dE}{dT} & = \frac{\beta SI}{N} - (\epsilon + \mu)E \\[6pt] \frac{dI}{dT} & = \varepsilon E - (\gamma + \mu)I \end{align} }[/math]

The MSEIR model

MSEIR模型

For the case of a disease, with the factors of passive immunity, and a latency period there is the MSEIR model.

For the case of a disease, with the factors of passive immunity, and a latency period there is the MSEIR model.

考虑被动免疫和潜伏期的因素情况下的传染病，建立了 MSEIR 模型。

[math]\displaystyle{ \color{blue}{\mathcal{M} \to \mathcal{S} \to \mathcal{E} \to \mathcal{I} \to \mathcal{R}} }[/math]

[math]\displaystyle{ \begin{align} \frac{dM}{dT} & = \Lambda - \delta M - \mu M \\[6pt] \frac{dS}{dT} & = \delta M - \frac{\beta SI}{N} - \mu S \\[6pt] \frac{dE}{dT} & = \frac{\beta SI}{N} - (\varepsilon + \mu)E \\[6pt] \frac{dI}{dT} & = \varepsilon E - (\gamma + \mu)I \\[6pt] \frac{dR}{dT} & = \gamma I - \mu R \end{align} }[/math]

The MSEIRS model

MSEIRS模型

An MSEIRS model is similar to the MSEIR, but the immunity in the R class would be temporary, so that individuals would regain their susceptibility when the temporary immunity ended.

An MSEIRS model is similar to the MSEIR, but the immunity in the R class would be temporary, so that individuals would regain their susceptibility when the temporary immunity ended.

MSEIRS 模型与 MSEIR 模型相似，但 r 类型的免疫效果是暂时的，因此当临时免疫结束时，个体将恢复其易感性。

[math]\displaystyle{ {\color{blue}{\mathcal{M} \to \mathcal{S} \to \mathcal{E} \to \mathcal{I} \to \mathcal{R} \to \mathcal{S}}} }[/math]

Variable contact rates

可变接触率

It is well known that the probability of getting a disease is not constant in time. As a pandemic progresses, reactions to the pandemic may change the contact rates which are assumed constant in the simpler models. Counter-measures such as masks, social distancing and lockdown will alter the contact rate in a way to reduce the speed of the pandemic.

It is well known that the probability of getting a disease is not constant in time. As a pandemic progresses, reactions to the pandemic may change the contact rates which are assumed constant in the simpler models. Counter-measures such as masks, social distancing and lockdown will alter the contact rate in a way to reduce the speed of the pandemic.

众所周知，患病的概率在时间上并不是一成不变的。随着大流行的发展，相关应对措施可能会改变接触率，而较简单模型中将接触率假定为恒定。口罩、社交距离和封锁等应对措施将改变接触率，从而降低大流行的速度。

In addition, Some diseases are seasonal, such as the common cold viruses, which are more prevalent during winter. With childhood diseases, such as measles, mumps, and rubella, there is a strong correlation with the school calendar, so that during the school holidays the probability of getting such a disease dramatically decreases. As a consequence, for many classes of diseases, one should consider a force of infection with periodically ('seasonal') varying contact rate

In addition, Some diseases are seasonal, such as the common cold viruses, which are more prevalent during winter. With childhood diseases, such as measles, mumps, and rubella, there is a strong correlation with the school calendar, so that during the school holidays the probability of getting such a disease dramatically decreases. As a consequence, for many classes of diseases, one should consider a force of infection with periodically ('seasonal') varying contact rate

此外，有些疾病是季节性的，例如普通感冒病毒，这些病毒在冬季更为普遍。儿童疾病，如麻疹、腮腺炎和风疹，与上学日期有很强的相关性，因此在学校假期内患这种疾病的可能性大大降低。因此，对于许多种类的疾病，人们应该考虑周期性（“季节性”）变化接触率的感染力

[math]\displaystyle{ F = \beta(t) \frac{I}{N} , \quad \beta(t+T)=\beta(t) }[/math]

with period T equal to one year.

with period T equal to one year.

周期T等于一年。

Thus, our model becomes

Thus, our model becomes

因此，我们的模型变成了

[math]\displaystyle{ \begin{align} \frac{dS}{dt} & = \mu N - \mu S - \beta(t) \frac{I}{N} S \\[8pt] \frac{dI}{dt} & = \beta(t) \frac{I}{N} S - (\gamma +\mu ) I \end{align} }[/math]

(the dynamics of recovered easily follows from [math]\displaystyle{ R=N-S-I }[/math]), i.e. a nonlinear set of differential equations with periodically varying parameters. It is well known that this class of dynamical systems may undergo very interesting and complex phenomena of nonlinear parametric resonance. It is easy to see that if:

(the dynamics of recovered easily follows from [math]\displaystyle{ R=N-S-I }[/math]), i.e. a nonlinear set of differential equations with periodically varying parameters. It is well known that this class of dynamical systems may undergo very interesting and complex phenomena of nonlinear parametric resonance. It is easy to see that if:

(康复的动力学很容易从[math]\displaystyle{ R=N-S-I }[/math]得出)，也就是具有周期变化参数的非线性微分方程组。众所周知，这类动力系统可能会出现非常有趣和复杂的非线性参数共振现象。显然，如果:

[math]\displaystyle{ \frac 1 T \int_0^T \frac{\beta(t)}{\mu+\gamma} \, dt \lt 1 \Rightarrow \lim_{t \to +\infty} (S(t),I(t)) = DFE = (N,0), }[/math]

whereas if the integral is greater than one the disease will not die out and there may be such resonances. For example, considering the periodically varying contact rate as the 'input' of the system one has that the output is a periodic function whose period is a multiple of the period of the input.

whereas if the integral is greater than one the disease will not die out and there may be such resonances. For example, considering the periodically varying contact rate as the 'input' of the system one has that the output is a periodic function whose period is a multiple of the period of the input.

然而，如果积分大于1，疾病就不会消失，可能会有这样的共振。例如，将周期性变化的接触率作为系统的输入，输出是一个周期函数，其周期是输入周期的倍数。

This allowed to give a contribution to explain the poly-annual (typically biennial) epidemic outbreaks of some infectious diseases as interplay between the period of the contact rate oscillations and the pseudo-period of the damped oscillations near the endemic equilibrium. Remarkably, in some cases, the behavior may also be quasi-periodic or even chaotic.

This allowed to give a contribution to explain the poly-annual (typically biennial) epidemic outbreaks of some infectious diseases as interplay between the period of the contact rate oscillations and the pseudo-period of the damped oscillations near the endemic equilibrium. Remarkably, in some cases, the behavior may also be quasi-periodic or even chaotic.

这就可以解释某些传染病的每年(常见为两年)流行病爆发，是由于接触率振荡周期和地方病平衡点附近阻尼振荡的伪周期之间的相互作用。值得注意的是，在某些情况下，这种行为也可能是准周期的，甚至是混沌的。

Modelling vaccination

疫苗接种模型

The SIR model can be modified to model vaccination

In the case of some diseases such as AIDS and Hepatitis B, it is possible for the offspring of infected parents to be born infected. This transmission of the disease down from the mother is called Vertical Transmission. The influx of additional members into the infected category can be considered within the model by including a fraction of the newborn members in the infected compartment.

就艾滋病和乙型肝炎等疾病而言，受感染父母的子女有可能在出生时就受到感染。这种从母体向下传染的疾病叫做垂直传染。在模型中，通过将一部分新生成员包括在染病人群中，可以在模型中考虑其他成员涌入染病类别人群的情况。

Vector transmission

水平传染

Diseases transmitted from human to human indirectly, i.e. malaria spread by way of mosquitoes, are transmitted through a vector. In these cases, the infection transfers from human to insect and an epidemic model must include both species, generally requiring many more compartments than a model for direct transmission.

Diseases transmitted from human to human indirectly, i.e. malaria spread by way of mosquitoes, are transmitted through a vector. In these cases, the infection transfers from human to insect and an epidemic model must include both species, generally requiring many more compartments than a model for direct transmission.

人与人之间间接传播的疾病，如通过蚊子传播的疟疾，通过中间媒介传播。在这些情况下，传染病会从人传播到昆虫，并且传播模型必须包括这两种物种，通常比直接传播模型需要更多的类别区分。

Others

其他

Other occurrences which may need to be considered when modeling an epidemic include things such as the following:

Other occurrences which may need to be considered when modeling an epidemic include things such as the following:

在建立流行病模型时可能需要考虑的其他事件包括以下事项:

• Non-homogeneous mixing

不同类型的疾病混合

• Variable infectivity

动态变化的传染率

• Distributions that are spatially non-uniform

空间上的不均匀分布

• Diseases caused by macroparasites

由大型寄生虫引起的疾病

Deterministic versus stochastic epidemic models

确定性和随机流行病模型 It is important to stress that the deterministic models presented here are valid only in case of sufficiently large populations, and as such should be used cautiously.

It is important to stress that the deterministic models presented here are valid only in case of sufficiently large populations, and as such should be used cautiously.

必须强调的是，这里提出的确定性模型只有在人群数量足够大的情况下才有效，因此应该谨慎使用。

To be more precise, these models are only valid in the thermodynamic limit, where the population is effectively infinite. In stochastic models, the long-time endemic equilibrium derived above, does not hold, as there is a finite probability that the number of infected individuals drops below one in a system. In a true system then, the pathogen may not propagate, as no host will be infected. But, in deterministic mean-field models, the number of infected can take on real, namely, non-integer values of infected hosts, and the number of hosts in the model can be less than one, but more than zero, thereby allowing the pathogen in the model to propagate. The reliability of compartmental models is limited to compartmental applications.

To be more precise, these models are only valid in the thermodynamic limit, where the population is effectively infinite. In stochastic models, the long-time endemic equilibrium derived above, does not hold, as there is a finite probability that the number of infected individuals drops below one in a system. In a true system then, the pathogen may not propagate, as no host will be infected. But, in deterministic mean-field models, the number of infected can take on real, namely, non-integer values of infected hosts, and the number of hosts in the model can be less than one, but more than zero, thereby allowing the pathogen in the model to propagate. The reliability of compartmental models is limited to compartmental applications.

更准确地说，这些模型仅在热力学极限中有效，在该热力学极限中，总体数量是无限大的。在随机模型中，由上述推导出的长期地方病平衡并不成立，因为在一个系统中，感染个体的数量下降到1以下的概率是有限的。在一个真正的系统中，病原体可能不会传播，因为没有宿主会被感染。但是，在确定性平均场模型中，被感染的病原体数量可以采用实数值，即被感染宿主的非整数值，模型中的宿主数量可以小于1，但大于零，从而使模型中的病原体得以繁殖。仓室模型的可靠性仅限于区分人群类别的应用。

One of the possible extensions of mean-field models considers the spreading of epidemics on a network based on percolation theory concepts.

One of the possible extensions of mean-field models considers the spreading of epidemics on a network based on percolation theory concepts.

平均场模型的一个可能的扩展是基于渗流理论的概念来考虑流行病在网络上的传播。

See also

参见

• Mathematical modelling in epidemiology

• Modifiable areal unit problem

• Next-generation matrix

• Risk assessment

• Attack rate

References

参考文献

Further reading

深入阅读

• May, Robert M.; Anderson, Roy M. (1991). Infectious diseases of humans: dynamics and control. Oxford: Oxford University Press. ISBN 0-19-854040-X. 

• Vynnycky, E.; White, R. G., eds. (2010). An Introduction to Infectious Disease Modelling. Oxford: Oxford University Press. ISBN 978-0-19-856576-5. 

External links

外部链接

• SIR model: Online experiments with JSXGraph

• "Simulating an epidemic". 3Blue1Brown. March 27, 2020 – via YouTube.

编辑推荐

[1]

美国新冠病毒肺炎流行病如何引发种族仇恨 | 网络科学论文速度36篇

[2]

用Links建模网络动力学

模板:Computer modeling

Category:Epidemiology

类别: 流行病学

Category:Scientific modeling

类别: 科学建模

This page was moved from wikipedia:en:Compartmental models in epidemiology. Its edit history can be viewed at 复杂传染病/edithistory