https://wiki.swarma.org/api.php?action=feedcontributions&feedformat=atom&user=Potatozh 2026-11-03T19:26:41Z 用户贡献 MediaWiki 1.35.0 https://wiki.swarma.org/index.php?title=%E7%BD%91%E7%BB%9C%E7%A7%91%E5%AD%A6%E4%B8%93%E9%A2%98&diff=17092 2020-10-27T04:34:04Z

<p>Potatozh：</p> <hr /> <div><br /> == 张江——演化网络 ==<br /> {{div col|colwidth=15em}}<br /> * [[玻色-爱因斯坦凝聚]]<br /> * [[双曲空间]]<br /> * [[复杂网络双曲生长模型]]<br /> * [[克洛内克积（Kronecker Product）]]<br /> * [[克罗内克图]]<br /> * [[网络的密集化现象（Densification）]]<br /> * [[加速生长（Accelerating Growth）]]<br /> * [[匹配生长模型]]<br /> {{div col end}}<br /> == 陈清华——无标度性质 ==<br /> {{div col|colwidth=15em}}<br /> * [[无标度网络]]<br /> * [[无标度性]]<br /> * [[度分布]]<br /> * [[幂律分布]]<br /> * [[80/20原则]]<br /> * [[Pareto分布]]<br /> * [[Zipf律]]<br /> * [[分形]]<br /> * [[自组织临界]]<br /> * [[沙堆模型]]<br /> * [[无标度网络的鲁棒性与脆弱性——阿喀琉斯之踵]]<br /> * [[蒙特卡洛模拟]]<br /> * [[KS检验]]<br /> {{div col end}}<br /> <br /> == 史定华——BA模型 ==<br /> {{div col|colwidth=15em}}<br /> * [[随机变量]]<br /> * [[随机过程]]<br /> * [[概率分布]]<br /> * [[概率密度函数]]<br /> * [[分布函数]]<br /> * [[补分布函数]]<br /> * [[幂律分布]]<br /> * [[重尾分布]]<br /> * [[幂律哀减尾部]]<br /> {{div col end}}<br /> <br /> == 许小可——随机网络 ==<br /> <br /> {{div col|colwidth=15em}}<br /> * [[二项分布]]<br /> * [[泊松分布]]<br /> * [[幂率分布]]<br /> * [[六度分离]]<br /> * [[小世界]]<br /> * [[相变]]<br /> * [[临界点]]<br /> * [[聚集系数]]<br /> * [[巨连通分支]]<br /> * [[度分布]]<br /> * [[格子网络]]<br /> {{div col end}}<br /> <br /> == 陈关荣——网络鲁棒性 ==<br /> <br /> [1] Percolation theory [仅需&quot;阈值&quot; (threshold) 的基本概念] <br /> https://zh.wikipedia.org/wiki/%E6%B8%97%E6%B5%81%E7%90%86%E8%AE%BA <br /> <br /> [2] Cascading failures [级联崩溃] <br /> https://en.wikipedia.org/wiki/Cascading_failure <br /> <br /> [3] Attacks [攻击] <br /> Node-removal and edge-removal attacks [对节点和连边的攻击] <br /> Random attack and malicious attack [随机攻击和恶意攻击] <br /> Attack threshold [攻击凑效的阈值] <br /> <br /> [4] Robustness (and Fragility) [鲁棒性（与 脆弱性）] <br /> https://baike.baidu.com/item/%E9%B2%81%E6%A3%92%E6%80%A7 <br /> https://en.wikipedia.org/wiki/Robustness_of_complex_networks <br /> Achilles heel: https://baike.baidu.com/item/%E9%98%BF%E5%96%80%E7%90%89%E6%96%AF%E4%B9%8B%E8%B8%B5/340132?fromtitle=Achilles%20heel&amp;fromid=8705928<br /> <br /> == 刘宗华——传播现象 ==<br /> <br /> {{div col|colwidth=15em}}<br /> * [[感染率]]<br /> * [[传染病的数学模型]]<br /> * [[阈值模型]]<br /> * [[理论分析方法]]<br /> * [[反应扩散模型和亚种群模型]]<br /> * [[基本再生数（Basic Reproduction Number）]]<br /> * [[非马尔科夫疾病传播（Non-Markovian Epidemic Spreading）]]<br /> * [[免疫策略（Immunization Strategy）]]<br /> {{div col end}}<br /> <br /> === 理论 ===<br /> ==== 已有词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[图论]]<br /> * [[WS小世界模型]]<br /> * [[社区结构]]<br /> * [[复杂网络]]<br /> * [[网络效应]]<br /> {{div col end}}<br /> <br /> ==== 待翻译词条 ==== <br /> {{div col|colwidth=15em}}<br /> * [[网络可控性]]<br /> * [[组合优化]]<br /> * [[互惠性]]<br /> * [[三元闭包]]<br /> * [[同质性]]<br /> * [[传递关系]]<br /> * [[平衡理论]]<br /> * [[社会影响]]<br /> * [[复杂传染病]]<br /> {{div col end}}<br /> ==== 待审校词条 ==== <br /> {{div col|colwidth=15em}}<br /> * [[链路分析]]<br /> * [[图的绘制]]<br /> * [[演化网络]]<br /> * [[相变]]<br /> <br /> * [[优先链接]]<br /> {{div col end}}<br /> <br /> === 图的特征 ===<br /> <br /> ==== 已翻译词条 ====<br /> *[[超图]]<br /> <br /> ==== 待翻译词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[团（图论）]] <br /> * [[切]]<br /> *[[图论常用概念表]]<br /> {{div col end}}<br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[环]]<br /> *[[邻接矩阵]]<br /> *[[关联矩阵]]<br /> *[[顶点]] <br /> *[[路径]]<br /> *[[二分图]]<br /> *[[随机图]]<br /> *[[图距离]]<br /> *[[图（抽象数据类型）]]<br /> <br /> {{div col end}}<br /> <br /> <br /> === 网络特性 ===<br /> ==== 已建立词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[集聚系数]]<br /> * [[幂律分布]]<br /> * [[节点中心性]]<br /> * [[HITS算法]]<br /> * [[六度分隔]]<br /> * [[PageRank算法]]<br /> * [[模块度]]<br /> * [[链路预测]]<br /> * [[随机网络模型]]<br /> * [[渗流理论]]（待完善）<br /> * [[无标度网络]]<br /> <br /> {{div col end}}<br /> <br /> <br /> ==== 待建立词条 ====<br /> {{div col|colwidth=15em}}<br /> <br /> * [[布尔网络]] <br /> *[[网络连通性]]<br /> *[[LFR算法]]<br /> {{div col end}}<br /> <br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[度]]<br /> *[[度分布]] <br /> *[[网络中心性]] <br /> *[[介数中心性]]<br /> {{div col end}}<br /> === 其他词条===<br /> <br /> <br /> ==== 待建立词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[埃尔德什数理论]] <br /> * [[巨连通分支]]<br /> * [[鲁棒性]]<br /> * [[攻击耐受性]]<br /> * [[级联故障]]<br /> * [[NP-难问题]] <br /> {{div col end}}<br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[泊松分布]] <br /> * [[枢纽节点]]<br /> * [[社交网络]]<br /> * [[层次聚类]]<br /> * [[生物网络]]<br /> {{div col end}}</div>

Potatozh https://wiki.swarma.org/index.php?title=%E5%8F%8D%E5%BA%94%E6%89%A9%E6%95%A3%E6%A8%A1%E5%9E%8B%E5%92%8C%E4%BA%9A%E7%A7%8D%E7%BE%A4%E6%A8%A1%E5%9E%8B&diff=16646 2020-10-23T14:49:23Z

<p>Potatozh：</p> <hr /> <div>此词条暂由Potatozh初译，未经审校，带来阅读不便，请见谅。<br /> <br /> <br /> == 反应扩散过程 ==<br /> <br /> [[File:Snipaste_2020-10-23_22-40-33.png|thumb|right|(a) Schematic illustration of the simplified modeling framework based on the particle-network scheme. At the macroscopic level the system is composed of a heterogeneous network of subpopulations. The contagion process in one subpopulation can spread to other subpopulations because of particles diffusing across subpopulations. (b) At the microscopic level, each subpopulation contains a population of individuals. The dynamical process, for instance, a contagion phenomenon, is described by a simple compartmentalization (compartments are indicated by different colored dots in the picture). Within each subpopulation, individuals can mix homogeneously or according to a subnetwork and can diffuse with probability p from one subpopulation to another following the edges of the network. (c) A critical value $p_c$ of the individuals or particles diffusion identifies a phase transition between a regime in which the contagion affects a large fraction of the system and one in which only a small fraction is affected.]] <br /> <br /> More in general models of social behavior and human mobility are often framed as reaction-diffusion processes where each node $i$ is allowed to host any nonnegative integer number of particles $\mathcal{N}(i)$, so that the total particle population of the system is $\mathcal{N}=\sum_i\mathcal{N}(i)$. This particle-network framework considers that each particle diffuses along the edges connecting nodes with a diffusion coefficient that depends on the node degree and/or other node attributes. Within each node particles may react according to different schemes characterizing the interaction dynamics of the system. A simple sketch of the particle-network framework is represented in the Figure.<br /> <br /> 一般来说，社会行为和人类流动性模型经常被构建为反应扩散过程，其中每个节点$i$可以容纳任何非负整数$\mathcal{N}(i)$个粒子，因此系统的总粒子数为$\mathcal{N}=\sum_i\mathcal{N}(i)$。 该粒子——网络框架中的节点内的每个粒子只能沿着连接节点的连边扩散，扩散系数取决于节点度或其他节点属性等。不同系统里的节点内的粒子的反应规则不同。粒子——网络的框架简单示意图如图所示。<br /> <br /> <br /> In order to have an analytic description of reaction-diffusion systems in networks one has to allow the possibility of heterogeneous connectivity patterns among nodes. A first analytical approach to these systems considers the extension of the degree-based mean-field approach to reaction-diffusion systems in networks with arbitrary degree distribution. For simplicity, we first consider the DBMF approach to the case of a simple system in which noninteracting particles (individuals) diffuse on a network with arbitrary topology. A convenient representation of the system is therefore provided by quantities defined in terms of the degree k:<br /> <br /> 为了对网络中的反应扩散系统进行理论上的分析描述，我们允许节点之间具有异质的连接模式。对这些系统的第一种分析方法是考虑将基于度的平均场理论方法扩展到具有任意度分布的网络中的反应扩散系统。简单起见，首先考虑一个任意网络结构的简单系统，其中粒子间没有相互作用或反应，粒子只会在网络上扩散的的情况。因此，通过以度$k$来分类的话，可以将系统简单表示为： <br /> \begin{equation} <br /> \mathcal{N}_k=\frac{1}{N_k}\sum_{i\in\mathcal{V}(k)}\mathcal{N}(i) <br /> \end{equation} <br /> where $N_k=NP(k)$ is the number of nodes with degree $k$ and the sum runs over the set of nodes $\mathcal{V}(k)$ having degree equal to k. The degree block variable $\mathcal{N}_k$ represents the average number of particles in nodes with degree k. The use of the DBMF approach amounts to the assumption that nodes with degree k, and thus the particles in those nodes, are statistically equivalent. In this approximation the dynamics of particles randomly diffusing on the network is given by a mean-field dynamical equation expressing the variation in time of the particle subpopulation N kðtÞ in each degree block k. This can be easily written as<br /> <br /> 其中$N_k=NP(k)$表示度为k的节点数量，$\mathcal{V}(k)$表示度为$k$的节点集合，$\mathcal{N}_k$表示度为$k$的节点的平均粒子数。基于度的平均场理论假设度为$k$的节点，同理节点中的粒子，在统计上是等效的。在这种近似的假设下，粒子随机扩散的动力学可以由如下平均场理论方程表示： <br /> \begin{equation} <br /> \frac{d\mathcal{N}_k}{dt}=-d_k\mathcal{N}_k(t)+k\sum_{k'}P(k'|k)d_{k'k}\mathcal{N}_{k'}(t) <br /> \end{equation} <br /> The first term of the equation considers that only a fraction $d_k$ of particles moves out of the node per unit time. The second term instead accounts for the particles diffusing from the neighbors into the node of degree k. This term is proportional to the number of links k times the average number of particles coming from each neighbor. This is equal to the average over all possible degrees $k'$ of the fraction of particles moving on that edge, $d_{k'k}$, according to the conditional probability $P(k'|k)$ that an edge belonging to a node of degree k is pointing to a node of degree $k'$. Here the term $d_{k'k}$ is the diffusion rate along the edges connecting nodes of degree k and $k'$. In the simplest case of homogeneous diffusion each particle diffuses with rate $r$ from the node in which it is and thus the diffusion per link $d_{k'k}=r/k'$. On uncorrelated networks $P(k'|k)=k'P(k')/\left&lt;k\right&gt;$ and hence one easily gets in the stationary state $d\mathcal{N}/dt=0$ the solution &lt;ref&gt;Noh J D, Rieger H. Random walks on complex networks[J]. Physical review letters, 2004, 92(11): 118701.&lt;/ref&gt;&lt;ref&gt;Colizza V, Pastor-Satorras R, Vespignani A. Reaction–diffusion processes and metapopulation models in heterogeneous networks[J]. Nature Physics, 2007, 3(4): 276-282.&lt;/ref&gt;<br /> <br /> 方程中的第一项表示每单位时间内有$d_k$部分的粒子从度为$k$的节点中离开，第二项表示粒子从邻居节点扩散到度为$k$的节点中，该项与连边数$k$成正比。条件概率$P(k'|k)$表示的是一条边的一端连接度为$k$的节点时，其另一端指向度为$k'$的概率。$d_{k'k}$表示的是两端连接了度为$k$和$k'$节点的连边的扩散率。在均匀扩散的最简单情况下，每个粒子从其所在的节点以速率$r$扩散，因此沿着每条连边的扩散率为$d_{k'k}=r/k'$。在无关联的网络上，$P(k'|k)=k'P(k')/\left&lt;k\right&gt;$，因此在稳态$d\mathcal{N}/dt=0$时，很容易得到解（Colizza等，2007b； Noh和Rieger，2004）： <br /> \begin{equation} <br /> \mathcal{N}_k=\frac{k}{\left&lt;k\right&gt;}\frac{\mathcal{N}}{N} <br /> \end{equation} <br /> The above equation explicitly brings the diffusion of particles in the description of the system and points out the importance of network topology in reaction-diffusion processes. This expression indicates that the larger the degree of a node, the larger the probability to be visited by the diffusing particles.<br /> <br /> 上式显式地给出了系统中粒子扩散的描述，并指出网络拓扑结构在反应扩散过程中的重要性。从式中可以看到，一个节点的度越大，则其被扩散中的粒子访问的可能性就越大。<br /> <br /> == 亚种群模型 ==<br /> <br /> The above approach can be generalized to reacting particles with different states by adding a reaction term to the above equations &lt;ref&gt;Colizza V, Pastor-Satorras R, Vespignani A. Reaction–diffusion processes and metapopulation models in heterogeneous networks[J]. Nature Physics, 2007, 3(4): 276-282.&lt;/ref&gt;. We now describe a generalization to this setting of the standard SIS model in discrete time, with probability per unit time $\beta$ of infection and probability $\mu$ of recovery. We consider $\mathcal{N}$ individuals diffusing in a heterogeneous network with $N$ nodes and degree distribution $P(k)$.<br /> Each node i of the network has a number $I(i)$ of infectious and $S(i)$ of susceptible individuals. The occupation numbers $I(i)$ and $S(i)$ can have any integer value, including $I(i)=S(i)=0$, that is, void nodes with no individuals.<br /> This modeling scheme describes spatially structured interacting subpopulations, such as city locations, urban areas, or defined geographical regions &lt;ref&gt;Grenfell B, Harwood J. (Meta) population dynamics of infectious diseases[J]. Trends in ecology &amp; evolution, 1997, 12(10): 395-399.&lt;/ref&gt;&lt;ref&gt;Ecology, genetics and evolution of metapopulations[M]. Academic Press, 2004.&lt;/ref&gt; and is usually referred to as the metapopulation approach. Each node of the network represents a subpopulation and the compartment dynamics accounts for the possibility that individuals in the same location may get into contact and change their state according to the infection dynamics. The interaction among subpopulations is the result of the movement of individuals from one subpopulation to the other. We have thus to associate with each individual’s class a diffusion probability $p_I$ and $p_S$ that indicates the probability for any individual to leave its node and move to a neighboring node of the network. In general the diffusion probabilities are heterogeneous and can be node dependent; however, for simplicity we assume that individuals diffuse with probability $p_I=p_S=1$ along any of the links departing from the node in which they are. This implies that at each time step an individual sitting on a node with degree k will diffuse into one of its nearest neighbors with probability $1/k$. In order to write the dynamical equations of the system we define the following quantities:<br /> <br /> 通过将反应项添加到上述方程中，可以将上述方法推广到具有不同状态的粒子发生反应的情形（Colizza等，2007b）。现在，我们描述在该情形下的SIS模型情况，其中单位时间的感染概率为$\beta$，恢复概率为$\mu$。我们考虑在具有$N$个节点和度分布为$P(k)$的异质网络中扩散的$\mathcal{N}$个个体。网络的每个节点$i$分别具有$I(i)$个感染态个体和$S(i)$个易感态个体（取值为非负整数值）。这种建模体系描述了空间结构相互作用的亚种群，例如城市位置、城市区域或确定的地理区域（Gren fall和Harwood，1997； Hanski和Gaggiotti，2004），并且通常被称为亚种群方法。网络中的每个节点代表一个亚种群，仓室动力学表示了处于相同位置的个体可能会相互接触并根据系统对应的感染动力学改变自身的状态的可能性。亚群之间的相互作用是个体从一个亚种群迁移到另一个亚种群的结果。为简单起见，我们可以假设个体从他们所在的节点离开沿着任意一条连边扩散的概率为$p_I=p_S=1$。这意味着每个时间步，度为$k$的节点上的一个个体将以概率$1/k$扩散到其最近的邻居之一。为了得到系统的动力学方程，我们定义如下的量： <br /> \begin{equation} <br /> I_k=\frac{1}{N_k}\sum_{i\in\mathcal{V}(k)}I(i) <br /> \end{equation} <br /> \begin{equation} <br /> S_k=\frac{1}{N_k}\sum_{i\in\mathcal{V}(k)}S(i) <br /> \end{equation} <br /> <br /> where the sums $\sum_{i\in\mathcal{V}(k)}$ are performed over nodes of degree $k$. These two quantities express the average number of susceptible and infectious individuals in nodes with degree $k$. Clearly, $\mathcal{N}_k=I_k+S_k$ is the average number of individuals in nodes with degree $k$. These quantities allow one to write the discrete-time equation describing the time evolution of $I_k(t)$ for each class of degree k as<br /> <br /> 其中$\mathcal{V}(k)$表示度为$k$的节点集合。上面的两式表示的是度为$k$的节点的平均感染态和易感态个体数，显然有$\mathcal{N}_k=I_k+S_k$。因此，系统的动力学方程可以表示为 <br /> \begin{equation} <br /> I_k(t+1)=k\sum_{k'}P(k|k')\frac{1}{k'}[(1-\mu)I_{k'}(t)+\beta\Gamma_{k'}(t)] <br /> \end{equation} <br /> where $\Gamma_{k'}(t)$ is an interaction kernel, a function of $I_{k'}$ and $S_{k'}$. The above equation is obtained by considering that at each time step the particles present on a node of degree $k$, first react, and then diffuse away from the node with probability 1. The value of $I_k(t+1)$ is obtained by summing the contribution of all particles diffusing to nodes of degree k from their neighbors of any degree $k'$, including the new particles generated by the reaction term $\Gamma_{k'}$. In the case of uncorrelated networks, the above equation reduces to<br /> <br /> 其中$\Gamma_{k'}(t)$为相互作用的核，是关于$I_{k'}$和$S_{k'}$的函数。该方程是通过考虑在每个时间步，度为$k$的节点上的粒子先发生反应，然后以1的概率从节点扩散出去得到的。在无关联网络的情况下，上式可以写为 <br /> \begin{equation} <br /> I_k(t+1)=\frac{k}{\left&lt;k\right&gt;}[(1-\mu)\bar{I}(t)+\beta\Gamma] <br /> \end{equation} <br /> where $\bar{I}(t)=\sum_k P(k)I_k$ is the average number of infected individuals per node in the network and $\Gamma=\sum_k P(k)\Gamma_k$. Analogously the equation describing the dynamics of susceptible individuals is<br /> <br /> 其中$\bar{I}(t)=\sum_k P(k)I_k$表示网络中每个节点的平均感染态个体的数量。$\Gamma=\sum_k P(k)\Gamma_k$。类似地，易感态个体的动力学方程可以表示为 <br /> \begin{equation} <br /> S_k(t+1)=\frac{k}{\left&lt;k\right&gt;}[\bar{S}(t)+\mu\bar{I}(t)-\beta\Gamma] <br /> \end{equation} <br /> where $\bar{S}(t)=\sum_k P(k)S_k$.<br /> <br /> 其中$\bar{S}(t)=\sum_k P(k)S_k$。<br /> <br /> == 参考文献 References == <br /> {{reflist}} <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;small&gt;此页参考来源： Pastor-Satorras R, Castellano C, Van Mieghem P, et al. Epidemic processes in complex networks[J]. Reviews of modern physics, 2015, 87(3): 925.</div>

Potatozh https://wiki.swarma.org/index.php?title=%E5%8F%8D%E5%BA%94%E6%89%A9%E6%95%A3%E6%A8%A1%E5%9E%8B%E5%92%8C%E4%BA%9A%E7%A7%8D%E7%BE%A4%E6%A8%A1%E5%9E%8B&diff=16639 2020-10-23T14:43:21Z

<p>Potatozh：</p> <hr /> <div>此词条暂由Potatozh初译，未经审校，带来阅读不便，请见谅。<br /> <br /> <br /> == 反应扩散过程 ==<br /> <br /> [[File:Snipaste_2020-10-23_22-40-33.png|thumb|right|(a) Schematic illustration of the simplified modeling framework based on the particle-network scheme. At the macroscopic level the system is composed of a heterogeneous network of subpopulations. The contagion process in one subpopulation can spread to other subpopulations because of particles diffusing across subpopulations. (b) At the microscopic level, each subpopulation contains a population of individuals. The dynamical process, for instance, a contagion phenomenon, is described by a simple compartmentalization (compartments are indicated by different colored dots in the picture). Within each subpopulation, individuals can mix homogeneously or according to a subnetwork and can diffuse with probability p from one subpopulation to another following the edges of the network. (c) A critical value $p_c$ of the individuals or particles diffusion identifies a phase transition between a regime in which the contagion affects a large fraction of the system and one in which only a small fraction is affected.]] <br /> <br /> More in general models of social behavior and human mobility are often framed as reaction-diffusion processes where each node $i$ is allowed to host any nonnegative integer number of particles $\mathcal{N}(i)$, so that the total particle population of the system is $\mathcal{N}=\sum_i\mathcal{N}(i)$. This particle-network framework considers that each particle diffuses along the edges connecting nodes with a diffusion coefficient that depends on the node degree and/or other node attributes. Within each node particles may react according to different schemes characterizing the interaction dynamics of the system. A simple sketch of the particle-network framework is represented in the Figure.<br /> <br /> 一般来说，社会行为和人类流动性模型经常被构建为反应扩散过程，其中每个节点$i$可以容纳任何非负整数$\mathcal{N}(i)$个粒子，因此系统的总粒子数为$\mathcal{N}=\sum_i\mathcal{N}(i)$。 该粒子——网络框架中的节点内的每个粒子只能沿着连接节点的连边扩散，扩散系数取决于节点度或其他节点属性等。不同系统里的节点内的粒子的反应规则不同。粒子——网络的框架简单示意图如图所示。<br /> <br /> <br /> In order to have an analytic description of reaction-diffusion systems in networks one has to allow the possibility of heterogeneous connectivity patterns among nodes. A first analytical approach to these systems considers the extension of the degree-based mean-field approach to reaction-diffusion systems in networks with arbitrary degree distribution. For simplicity, we first consider the DBMF approach to the case of a simple system in which noninteracting particles (individuals) diffuse on a network with arbitrary topology. A convenient representation of the system is therefore provided by quantities defined in terms of the degree k:<br /> <br /> 为了对网络中的反应扩散系统进行理论上的分析描述，我们允许节点之间具有异质的连接模式。对这些系统的第一种分析方法是考虑将基于度的平均场理论方法扩展到具有任意度分布的网络中的反应扩散系统。简单起见，首先考虑一个任意网络结构的简单系统，其中粒子间没有相互作用或反应，粒子只会在网络上扩散的的情况。因此，通过以度$k$来分类的话，可以将系统简单表示为： <br /> \begin{equation} <br /> \mathcal{N}_k=\frac{1}{N_k}\sum_{i\in\mathcal{V}(k)}\mathcal{N}(i) <br /> \end{equation} <br /> where $N_k=NP(k)$ is the number of nodes with degree $k$ and the sum runs over the set of nodes $\mathcal{V}(k)$ having degree equal to k. The degree block variable $\mathcal{N}_k$ represents the average number of particles in nodes with degree k. The use of the DBMF approach amounts to the assumption that nodes with degree k, and thus the particles in those nodes, are statistically equivalent. In this approximation the dynamics of particles randomly diffusing on the network is given by a mean-field dynamical equation expressing the variation in time of the particle subpopulation N kðtÞ in each degree block k. This can be easily written as<br /> <br /> 其中$N_k=NP(k)$表示度为k的节点数量，$\mathcal{V}(k)$表示度为$k$的节点集合，$\mathcal{N}_k$表示度为$k$的节点的平均粒子数。基于度的平均场理论假设度为$k$的节点，同理节点中的粒子，在统计上是等效的。在这种近似的假设下，粒子随机扩散的动力学可以由如下平均场理论方程表示： <br /> \begin{equation} <br /> \frac{d\mathcal{N}_k}{dt}=-d_k\mathcal{N}_k(t)+k\sum_{k'}P(k'|k)d_{k'k}\mathcal{N}_{k'}(t) <br /> \end{equation} <br /> The first term of the equation considers that only a fraction $d_k$ of particles moves out of the node per unit time. The second term instead accounts for the particles diffusing from the neighbors into the node of degree k. This term is proportional to the number of links k times the average number of particles coming from each neighbor. This is equal to the average over all possible degrees $k'$ of the fraction of particles moving on that edge, $d_{k'k}$, according to the conditional probability $P(k'|k)$ that an edge belonging to a node of degree k is pointing to a node of degree $k'$. Here the term $d_{k'k}$ is the diffusion rate along the edges connecting nodes of degree k and $k'$. In the simplest case of homogeneous diffusion each particle diffuses with rate $r$ from the node in which it is and thus the diffusion per link $d_{k'k}=r/k'$. On uncorrelated networks $P(k'|k)=k'P(k')/\left&lt;k\right&gt;$ and hence one easily gets in the stationary state $d\mathcal{N}/dt=0$ the solution (Noh and Rieger, 2004; Colizza, Pastor-Satorras, and Vespignani, 2007)<br /> <br /> 方程中的第一项表示每单位时间内有$d_k$部分的粒子从度为$k$的节点中离开，第二项表示粒子从邻居节点扩散到度为$k$的节点中，该项与连边数$k$成正比。条件概率$P(k'|k)$表示的是一条边的一端连接度为$k$的节点时，其另一端指向度为$k'$的概率。$d_{k'k}$表示的是两端连接了度为$k$和$k'$节点的连边的扩散率。在均匀扩散的最简单情况下，每个粒子从其所在的节点以速率$r$扩散，因此沿着每条连边的扩散率为$d_{k'k}=r/k'$。在无关联的网络上，$P(k'|k)=k'P(k')/\left&lt;k\right&gt;$，因此在稳态$d\mathcal{N}/dt=0$时，很容易得到解（Colizza等，2007b； Noh和Rieger，2004）： <br /> \begin{equation} <br /> \mathcal{N}_k=\frac{k}{\left&lt;k\right&gt;}\frac{\mathcal{N}}{N} <br /> \end{equation} <br /> The above equation explicitly brings the diffusion of particles in the description of the system and points out the importance of network topology in reaction-diffusion processes. This expression indicates that the larger the degree of a node, the larger the probability to be visited by the diffusing particles.<br /> <br /> 上式显式地给出了系统中粒子扩散的描述，并指出网络拓扑结构在反应扩散过程中的重要性。从式中可以看到，一个节点的度越大，则其被扩散中的粒子访问的可能性就越大。<br /> <br /> == 亚种群模型 ==<br /> <br /> The above approach can be generalized to reacting particles with different states by adding a reaction term to the above equations (Colizza, Pastor-Satorras, and Vespignani, 2007). We now describe a generalization to this setting of the standard SIS model in discrete time, with probability per unit time $\beta$ of infection and probability $\mu$ of recovery. We consider $\mathcal{N}$ individuals diffusing in a heterogeneous network with $N$ nodes and degree distribution $P(k)$.<br /> Each node i of the network has a number $I(i)$ of infectious and $S(i)$ of susceptible individuals. The occupation numbers $I(i)$ and $S(i)$ can have any integer value, including $I(i)=S(i)=0$, that is, void nodes with no individuals.<br /> This modeling scheme describes spatially structured interacting subpopulations, such as city locations, urban areas, or defined geographical regions (Grenfell and Harwood, 1997; Hanski and Gaggiotti, 2004) and is usually referred to as the metapopulation approach. Each node of the network represents a subpopulation and the compartment dynamics accounts for the possibility that individuals in the same location may get into contact and change their state according to the infection dynamics. The interaction among subpopulations is the result of the movement of individuals from one subpopulation to the other. We have thus to associate with each individual’s class a diffusion probability $p_I$ and $p_S$ that indicates the probability for any individual to leave its node and move to a neighboring node of the network. In general the diffusion probabilities are heterogeneous and can be node dependent; however, for simplicity we assume that individuals diffuse with probability $p_I=p_S=1$ along any of the links departing from the node in which they are. This implies that at each time step an individual sitting on a node with degree k will diffuse into one of its nearest neighbors with probability $1/k$. In order to write the dynamical equations of the system we define the following quantities:<br /> <br /> 通过将反应项添加到上述方程中，可以将上述方法推广到具有不同状态的粒子发生反应的情形（Colizza等，2007b）。现在，我们描述在该情形下的SIS模型情况，其中单位时间的感染概率为$\beta$，恢复概率为$\mu$。我们考虑在具有$N$个节点和度分布为$P(k)$的异质网络中扩散的$\mathcal{N}$个个体。网络的每个节点$i$分别具有$I(i)$个感染态个体和$S(i)$个易感态个体（取值为非负整数值）。这种建模体系描述了空间结构相互作用的亚种群，例如城市位置、城市区域或确定的地理区域（Gren fall和Harwood，1997； Hanski和Gaggiotti，2004），并且通常被称为亚种群方法。网络中的每个节点代表一个亚种群，仓室动力学表示了处于相同位置的个体可能会相互接触并根据系统对应的感染动力学改变自身的状态的可能性。亚群之间的相互作用是个体从一个亚种群迁移到另一个亚种群的结果。为简单起见，我们可以假设个体从他们所在的节点离开沿着任意一条连边扩散的概率为$p_I=p_S=1$。这意味着每个时间步，度为$k$的节点上的一个个体将以概率$1/k$扩散到其最近的邻居之一。为了得到系统的动力学方程，我们定义如下的量： <br /> \begin{equation} <br /> I_k=\frac{1}{N_k}\sum_{i\in\mathcal{V}(k)}I(i) <br /> \end{equation} <br /> \begin{equation} <br /> S_k=\frac{1}{N_k}\sum_{i\in\mathcal{V}(k)}S(i) <br /> \end{equation} <br /> <br /> where the sums $\sum_{i\in\mathcal{V}(k)}$ are performed over nodes of degree $k$. These two quantities express the average number of susceptible and infectious individuals in nodes with degree $k$. Clearly, $\mathcal{N}_k=I_k+S_k$ is the average number of individuals in nodes with degree $k$. These quantities allow one to write the discrete-time equation describing the time evolution of $I_k(t)$ for each class of degree k as<br /> <br /> 其中$\mathcal{V}(k)$表示度为$k$的节点集合。上面的两式表示的是度为$k$的节点的平均感染态和易感态个体数，显然有$\mathcal{N}_k=I_k+S_k$。因此，系统的动力学方程可以表示为 <br /> \begin{equation} <br /> I_k(t+1)=k\sum_{k'}P(k|k')\frac{1}{k'}[(1-\mu)I_{k'}(t)+\beta\Gamma_{k'}(t)] <br /> \end{equation} <br /> where $\Gamma_{k'}(t)$ is an interaction kernel, a function of $I_{k'}$ and $S_{k'}$. The above equation is obtained by considering that at each time step the particles present on a node of degree $k$, first react, and then diffuse away from the node with probability 1. The value of $I_k(t+1)$ is obtained by summing the contribution of all particles diffusing to nodes of degree k from their neighbors of any degree $k'$, including the new particles generated by the reaction term $\Gamma_{k'}$. In the case of uncorrelated networks, the above equation reduces to<br /> <br /> 其中$\Gamma_{k'}(t)$为相互作用的核，是关于$I_{k'}$和$S_{k'}$的函数。该方程是通过考虑在每个时间步，度为$k$的节点上的粒子先发生反应，然后以1的概率从节点扩散出去得到的。在无关联网络的情况下，上式可以写为 <br /> \begin{equation} <br /> I_k(t+1)=\frac{k}{\left&lt;k\right&gt;}[(1-\mu)\bar{I}(t)+\beta\Gamma] <br /> \end{equation} <br /> where $\bar{I}(t)=\sum_k P(k)I_k$ is the average number of infected individuals per node in the network and $\Gamma=\sum_k P(k)\Gamma_k$. Analogously the equation describing the dynamics of susceptible individuals is<br /> <br /> 其中$\bar{I}(t)=\sum_k P(k)I_k$表示网络中每个节点的平均感染态个体的数量。$\Gamma=\sum_k P(k)\Gamma_k$。类似地，易感态个体的动力学方程可以表示为 <br /> \begin{equation} <br /> S_k(t+1)=\frac{k}{\left&lt;k\right&gt;}[\bar{S}(t)+\mu\bar{I}(t)-\beta\Gamma] <br /> \end{equation} <br /> where $\bar{S}(t)=\sum_k P(k)S_k$.<br /> <br /> 其中$\bar{S}(t)=\sum_k P(k)S_k$。<br /> <br /> == 参考文献 References == <br /> {{reflist}} <br /> <br /> <br /> <br /> <br /> <br /> <br /> &lt;small&gt;此页参考来源： Pastor-Satorras R, Castellano C, Van Mieghem P, et al. Epidemic processes in complex networks[J]. Reviews of modern physics, 2015, 87(3): 925.</div>

Potatozh https://wiki.swarma.org/index.php?title=%E6%96%87%E4%BB%B6:Snipaste_2020-10-23_22-40-33.png&diff=16637 2020-10-23T14:41:31Z

<p>Potatozh：基于MsUpload的文件上传</p> <hr /> <div>基于MsUpload的文件上传</div>

Potatozh https://wiki.swarma.org/index.php?title=%E5%8F%8D%E5%BA%94%E6%89%A9%E6%95%A3%E6%A8%A1%E5%9E%8B%E5%92%8C%E4%BA%9A%E7%A7%8D%E7%BE%A4%E6%A8%A1%E5%9E%8B&diff=16632 2020-10-23T14:38:07Z

<p>Potatozh：</p> <hr /> <div>此词条暂由Potatozh初译，未经审校，带来阅读不便，请见谅。<br /> <br /> <br /> == 反应扩散过程 ==<br /> More in general models of social behavior and human mobility are often framed as reaction-diffusion processes where each node $i$ is allowed to host any nonnegative integer number of particles $\mathcal{N}(i)$, so that the total particle population of the system is $\mathcal{N}=\sum_i\mathcal{N}(i)$. This particle-network framework considers that each particle diffuses along the edges connecting nodes with a diffusion coefficient that depends on the node degree and/or other node attributes. Within each node particles may react according to different schemes characterizing the interaction dynamics of the system. A simple sketch of the particle-network framework is represented in the Figure.<br /> <br /> 一般来说，社会行为和人类流动性模型经常被构建为反应扩散过程，其中每个节点$i$可以容纳任何非负整数$\mathcal{N}(i)$个粒子，因此系统的总粒子数为$\mathcal{N}=\sum_i\mathcal{N}(i)$。 该粒子——网络框架中的节点内的每个粒子只能沿着连接节点的连边扩散，扩散系数取决于节点度或其他节点属性等。不同系统里的节点内的粒子的反应规则不同。粒子——网络的框架简单示意图如图所示。<br /> <br /> <br /> In order to have an analytic description of reaction-diffusion systems in networks one has to allow the possibility of heterogeneous connectivity patterns among nodes. A first analytical approach to these systems considers the extension of the degree-based mean-field approach to reaction-diffusion systems in networks with arbitrary degree distribution. For simplicity, we first consider the DBMF approach to the case of a simple system in which noninteracting particles (individuals) diffuse on a network with arbitrary topology. A convenient representation of the system is therefore provided by quantities defined in terms of the degree k:<br /> <br /> 为了对网络中的反应扩散系统进行理论上的分析描述，我们允许节点之间具有异质的连接模式。对这些系统的第一种分析方法是考虑将基于度的平均场理论方法扩展到具有任意度分布的网络中的反应扩散系统。简单起见，首先考虑一个任意网络结构的简单系统，其中粒子间没有相互作用或反应，粒子只会在网络上扩散的的情况。因此，通过以度$k$来分类的话，可以将系统简单表示为： <br /> \begin{equation} <br /> \mathcal{N}_k=\frac{1}{N_k}\sum_{i\in\mathcal{V}(k)}\mathcal{N}(i) <br /> \end{equation} <br /> where $N_k=NP(k)$ is the number of nodes with degree $k$ and the sum runs over the set of nodes $\mathcal{V}(k)$ having degree equal to k. The degree block variable $\mathcal{N}_k$ represents the average number of particles in nodes with degree k. The use of the DBMF approach amounts to the assumption that nodes with degree k, and thus the particles in those nodes, are statistically equivalent. In this approximation the dynamics of particles randomly diffusing on the network is given by a mean-field dynamical equation expressing the variation in time of the particle subpopulation N kðtÞ in each degree block k. This can be easily written as<br /> <br /> 其中$N_k=NP(k)$表示度为k的节点数量，$\mathcal{V}(k)$表示度为$k$的节点集合，$\mathcal{N}_k$表示度为$k$的节点的平均粒子数。基于度的平均场理论假设度为$k$的节点，同理节点中的粒子，在统计上是等效的。在这种近似的假设下，粒子随机扩散的动力学可以由如下平均场理论方程表示： <br /> \begin{equation} <br /> \frac{d\mathcal{N}_k}{dt}=-d_k\mathcal{N}_k(t)+k\sum_{k'}P(k'|k)d_{k'k}\mathcal{N}_{k'}(t) <br /> \end{equation} <br /> The first term of the equation considers that only a fraction $d_k$ of particles moves out of the node per unit time. The second term instead accounts for the particles diffusing from the neighbors into the node of degree k. This term is proportional to the number of links k times the average number of particles coming from each neighbor. This is equal to the average over all possible degrees $k'$ of the fraction of particles moving on that edge, $d_{k'k}$, according to the conditional probability $P(k'|k)$ that an edge belonging to a node of degree k is pointing to a node of degree $k'$. Here the term $d_{k'k}$ is the diffusion rate along the edges connecting nodes of degree k and $k'$. In the simplest case of homogeneous diffusion each particle diffuses with rate $r$ from the node in which it is and thus the diffusion per link $d_{k'k}=r/k'$. On uncorrelated networks $P(k'|k)=k'P(k')/\left&lt;k\right&gt;$ and hence one easily gets in the stationary state $d\mathcal{N}/dt=0$ the solution (Noh and Rieger, 2004; Colizza, Pastor-Satorras, and Vespignani, 2007)<br /> <br /> 方程中的第一项表示每单位时间内有$d_k$部分的粒子从度为$k$的节点中离开，第二项表示粒子从邻居节点扩散到度为$k$的节点中，该项与连边数$k$成正比。条件概率$P(k'|k)$表示的是一条边的一端连接度为$k$的节点时，其另一端指向度为$k'$的概率。$d_{k'k}$表示的是两端连接了度为$k$和$k'$节点的连边的扩散率。在均匀扩散的最简单情况下，每个粒子从其所在的节点以速率$r$扩散，因此沿着每条连边的扩散率为$d_{k'k}=r/k'$。在无关联的网络上，$P(k'|k)=k'P(k')/\left&lt;k\right&gt;$，因此在稳态$d\mathcal{N}/dt=0$时，很容易得到解（Colizza等，2007b； Noh和Rieger，2004）： <br /> \begin{equation} <br /> \mathcal{N}_k=\frac{k}{\left&lt;k\right&gt;}\frac{\mathcal{N}}{N} <br /> \end{equation} <br /> The above equation explicitly brings the diffusion of particles in the description of the system and points out the importance of network topology in reaction-diffusion processes. This expression indicates that the larger the degree of a node, the larger the probability to be visited by the diffusing particles.<br /> <br /> 上式显式地给出了系统中粒子扩散的描述，并指出网络拓扑结构在反应扩散过程中的重要性。从式中可以看到，一个节点的度越大，则其被扩散中的粒子访问的可能性就越大。<br /> <br /> == 亚种群模型 ==<br /> <br /> The above approach can be generalized to reacting particles with different states by adding a reaction term to the above equations (Colizza, Pastor-Satorras, and Vespignani, 2007). We now describe a generalization to this setting of the standard SIS model in discrete time, with probability per unit time $\beta$ of infection and probability $\mu$ of recovery. We consider $\mathcal{N}$ individuals diffusing in a heterogeneous network with $N$ nodes and degree distribution $P(k)$.<br /> Each node i of the network has a number $I(i)$ of infectious and $S(i)$ of susceptible individuals. The occupation numbers $I(i)$ and $S(i)$ can have any integer value, including $I(i)=S(i)=0$, that is, void nodes with no individuals.<br /> This modeling scheme describes spatially structured interacting subpopulations, such as city locations, urban areas, or defined geographical regions (Grenfell and Harwood, 1997; Hanski and Gaggiotti, 2004) and is usually referred to as the metapopulation approach. Each node of the network represents a subpopulation and the compartment dynamics accounts for the possibility that individuals in the same location may get into contact and change their state according to the infection dynamics. The interaction among subpopulations is the result of the movement of individuals from one subpopulation to the other. We have thus to associate with each individual’s class a diffusion probability $p_I$ and $p_S$ that indicates the probability for any individual to leave its node and move to a neighboring node of the network. In general the diffusion probabilities are heterogeneous and can be node dependent; however, for simplicity we assume that individuals diffuse with probability $p_I=p_S=1$ along any of the links departing from the node in which they are. This implies that at each time step an individual sitting on a node with degree k will diffuse into one of its nearest neighbors with probability $1/k$. In order to write the dynamical equations of the system we define the following quantities:<br /> <br /> 通过将反应项添加到上述方程中，可以将上述方法推广到具有不同状态的粒子发生反应的情形（Colizza等，2007b）。现在，我们描述在该情形下的SIS模型情况，其中单位时间的感染概率为$\beta$，恢复概率为$\mu$。我们考虑在具有$N$个节点和度分布为$P(k)$的异质网络中扩散的$\mathcal{N}$个个体。网络的每个节点$i$分别具有$I(i)$个感染态个体和$S(i)$个易感态个体（取值为非负整数值）。这种建模体系描述了空间结构相互作用的亚种群，例如城市位置、城市区域或确定的地理区域（Gren fall和Harwood，1997； Hanski和Gaggiotti，2004），并且通常被称为亚种群方法。网络中的每个节点代表一个亚种群，仓室动力学表示了处于相同位置的个体可能会相互接触并根据系统对应的感染动力学改变自身的状态的可能性。亚群之间的相互作用是个体从一个亚种群迁移到另一个亚种群的结果。为简单起见，我们可以假设个体从他们所在的节点离开沿着任意一条连边扩散的概率为$p_I=p_S=1$。这意味着每个时间步，度为$k$的节点上的一个个体将以概率$1/k$扩散到其最近的邻居之一。为了得到系统的动力学方程，我们定义如下的量： <br /> \begin{equation} <br /> I_k=\frac{1}{N_k}\sum_{i\in\mathcal{V}(k)}I(i) <br /> \end{equation} <br /> \begin{equation} <br /> S_k=\frac{1}{N_k}\sum_{i\in\mathcal{V}(k)}S(i) <br /> \end{equation} <br /> <br /> where the sums $\sum_{i\in\mathcal{V}(k)}$ are performed over nodes of degree $k$. These two quantities express the average number of susceptible and infectious individuals in nodes with degree $k$. Clearly, $\mathcal{N}_k=I_k+S_k$ is the average number of individuals in nodes with degree $k$. These quantities allow one to write the discrete-time equation describing the time evolution of $I_k(t)$ for each class of degree k as<br /> <br /> 其中$\mathcal{V}(k)$表示度为$k$的节点集合。上面的两式表示的是度为$k$的节点的平均感染态和易感态个体数，显然有$\mathcal{N}_k=I_k+S_k$。因此，系统的动力学方程可以表示为 <br /> \begin{equation} <br /> I_k(t+1)=k\sum_{k'}P(k|k')\frac{1}{k'}[(1-\mu)I_{k'}(t)+\beta\Gamma_{k'}(t)] <br /> \end{equation} <br /> where $\Gamma_{k'}(t)$ is an interaction kernel, a function of $I_{k'}$ and $S_{k'}$. The above equation is obtained by considering that at each time step the particles present on a node of degree $k$, first react, and then diffuse away from the node with probability 1. The value of $I_k(t+1)$ is obtained by summing the contribution of all particles diffusing to nodes of degree k from their neighbors of any degree $k'$, including the new particles generated by the reaction term $\Gamma_{k'}$. In the case of uncorrelated networks, the above equation reduces to<br /> <br /> 其中$\Gamma_{k'}(t)$为相互作用的核，是关于$I_{k'}$和$S_{k'}$的函数。该方程是通过考虑在每个时间步，度为$k$的节点上的粒子先发生反应，然后以1的概率从节点扩散出去得到的。在无关联网络的情况下，上式可以写为 <br /> \begin{equation} <br /> I_k(t+1)=\frac{k}{\left&lt;k\right&gt;}[(1-\mu)\bar{I}(t)+\beta\Gamma] <br /> \end{equation} <br /> where $\bar{I}(t)=\sum_k P(k)I_k$ is the average number of infected individuals per node in the network and $\Gamma=\sum_k P(k)\Gamma_k$. Analogously the equation describing the dynamics of susceptible individuals is<br /> <br /> 其中$\bar{I}(t)=\sum_k P(k)I_k$表示网络中每个节点的平均感染态个体的数量。$\Gamma=\sum_k P(k)\Gamma_k$。类似地，易感态个体的动力学方程可以表示为 <br /> \begin{equation} <br /> S_k(t+1)=\frac{k}{\left&lt;k\right&gt;}[\bar{S}(t)+\mu\bar{I}(t)-\beta\Gamma] <br /> \end{equation} <br /> where $\bar{S}(t)=\sum_k P(k)S_k$.<br /> <br /> 其中$\bar{S}(t)=\sum_k P(k)S_k$。</div>

Potatozh https://wiki.swarma.org/index.php?title=%E5%9F%BA%E6%9C%AC%E5%86%8D%E7%94%9F%E6%95%B0&diff=16629 2020-10-23T14:04:06Z

<p>Potatozh：</p> <hr /> <div>此词条暂由Potatozh初译，未经审校，带来阅读不便，请见谅。<br /> <br /> <br /> 基本再生数（basic reproduction number）是传染病研究中一个重要的量，记为$R_0$，下面给出它的定义。考虑疾病传播的最初阶段，此时只有少数的疾病案例，其他人都是易感个体，传染病学术语中通常称为幼稚人口（native population），假如其中一个易感个体在疾病爆发以后被感染，那么基本再生数指的就是这个被传染的人在他恢复之前将疾病平均传染给了多少个人。例如，如果每个被感染者将疾病平均又传染给了其他两个人，则$R_0=2$。如果有一半的人将疾病分别又传染给另一个人，而剩余的一半人没有传染给其他任何人，那么$R_0=1/2$，依次类推。<br /> <br /> 如果$R_0=2$，那么每个得病的人就会平均传染两个人，而这两个人中的每一个又会再传染另外两个人，如此下去，每一轮新的疾病的案例都会加倍，从而出现指数增长规律。相反，如果$R_0=1/2$，那么疾病就会以指数形式衰减。$R_0=1$是增长和减少的分割点，因此它也标示着传染病阈值（epidemic threshold），偏离此值时，疾病要么倍增要么消亡。<br /> <br /> 以SIR模型为例来说，其$R_0$值可以通过如下方式计算得到:<br /> <br /> 由于SIR模型中，任意时间间隔$\delta\tau$内，个体恢复的概率为$\gamma\delta\tau$，不能恢复的概率为$1-\gamma\delta\tau$。因此，在总时间$\tau$后，个体仍然处于感染态的概率为<br /> \begin{equation}<br /> \lim_{\delta\tau\to0}(1-\gamma\delta\tau)^{\tau/\delta\tau}=e^{-\gamma\tau},<br /> \end{equation}<br /> 同时，个体保持感染态，然后在$\tau$到$\tau+d\tau$区间恢复的概率$p(\tau)d\tau$可以表示为上述概率与$\gamma d\tau$的乘积，即<br /> \begin{equation}<br /> p(\tau)d\tau=\gamma e^{-\gamma\tau}d\tau,<br /> \end{equation}<br /> 通过上式我们得到了感染个体在恢复以前处于感染态的时间长度$\tau$的分布。由于基本再生数$R_0$指的就是这个被传染的人在他恢复之前将疾病平均传染给了多少个人。所以对于SIR模型的$R_0$值，如果一个人在时间$\tau$内仍然具有传染性，则在这段时间内，与之接触的人的期望值为$\beta\tau$。$R_0$的定义是针对幼稚人口给出的，而在幼稚人口中接触到的所有人都是易感者，因此接触到的所有人都是易感者，因此$\beta\tau$也就是感染个体将传染的总人数。对$\beta\tau$中的$\tau$的分布取平均，就可以得到$R_0$的平均值：<br /> \begin{equation}<br /> R_0=\beta\gamma\int^\infty_0 \tau e^{-\gamma\tau}d\tau=\frac{\beta}{\gamma}.<br /> \end{equation}<br /> <br /> <br /> <br /> <br /> &lt;small&gt;此页参考来源： 网络科学引论. M. E. J. Newman著 郭世泽 陈哲 译 电子工业出版社</div>

Potatozh https://wiki.swarma.org/index.php?title=%E6%96%87%E4%BB%B6:Snipaste_2020-10-23_21-11-43.png&diff=16615 2020-10-23T13:14:24Z

<p>Potatozh：基于MsUpload的文件上传</p> <hr /> <div>基于MsUpload的文件上传</div>

Potatozh https://wiki.swarma.org/index.php?title=%E6%96%87%E4%BB%B6:Snipaste1.png&diff=16613 2020-10-23T13:00:54Z

<p>Potatozh：基于MsUpload的文件上传</p> <hr /> <div>基于MsUpload的文件上传</div>

Potatozh https://wiki.swarma.org/index.php?title=%E6%96%87%E4%BB%B6:Snipaste_2020-10-23_20-56-39.png&diff=16612 2020-10-23T12:59:46Z

<p>Potatozh：基于MsUpload的文件上传</p> <hr /> <div>基于MsUpload的文件上传</div>

Potatozh https://wiki.swarma.org/index.php?title=%E6%84%9F%E6%9F%93%E7%8E%87&diff=16607 2020-10-23T12:32:54Z

<p>Potatozh：</p> <hr /> <div>此词条暂由Potatozh初译，未经审校，带来阅读不便，请见谅。<br /> <br /> <br /> An infection rate (or incident rate) is the probability or risk of an infection in a population. It is used to measure the frequency of occurrence of new instances of infection within a population during a specific time period.<br /> <br /> 感染率（或发病率）是人群中感染的可能性或风险。 它用于衡量特定时间段内某个人群中新感染病例的发生频率。<br /> <br /> [[File:1.png|400px]]<br /> <br /> The number of infections equals the cases identified in the study or observed. An example might by HIV infection during a specific time period in the defined population. The population at risk are the cases appearing in the population during the same time period. An example would be all the people in a city during a specific time period. The constant, or K is assigned a value of 100 to represent a percentage. An example would be to find the percentage of people in a city who are infected with HIV: 6,000 cases in March divided by the population of a city (one million) multiplied by the constant (K) would give an infection rate of 0.6%. Calculating the infection rate is used to analyze trends for the purpose of infection and disease control.&lt;ref&gt;{{cite web | title = Calculation of Infection Rates|url = http://health.utah.gov/epi/diseases/HAI/resources/Cal_Inf_Rates.pdf |publisher = Utah Department of Health|date= 2017| accessdate = 2017-01-09 }}&lt;/ref&gt;<br /> <br /> 感染数量等于研究中确诊或观察到的病例。 一个例子可能是在特定时期内确诊人群中的HIV感染。处于风险中的人口数目是同一时期内人口数目。一个示例是特定时间段内城市中的所有人。常数K被赋值等于100，以表示百分比。例如，要找到一个城市中感染艾滋病毒的人口百分比：3月的时候有6,000例病例除以一个城市人口（一百万）乘以常数（K）得出的感染率为0.6％。计算感染率通常是被用于分析趋势，以达到控制感染和疾病的目的。<br /> <br /> An online infection rate calculator has been developed by the Centers for Disease Control and Prevention that allows the determination of the Streptococcal A infection rate in a population.&lt;ref&gt;{{cite web | title = Group A Streptococcus Calculator | publisher=Centers for Disease Control and Prevention |date= 4 July 2008 |url = https://www2a.cdc.gov/ncidod/dbmd/abcs/calc/calc_new/calculator.html | accessdate = 2017-01-09 }}&lt;/ref&gt;<br /> <br /> 疾病控制与预防中心已经开发了一个在线感染率计算器，可以确定人群中链球菌A的感染率。<br /> <br /> == 临床应用 Clinical applications ==<br /> <br /> Health care facilities routinely track their infection rates according to the guidelines issued by the Joint Commission.&lt;ref&gt;{{cite web | title= Basic Statistical Data Used in Acute Care Facilities | publisher = JB Learning | url = http://www.jblearning.com/samples/0763750344/45561_CH01.pdf | accessdate = 2017-01-09 }}&lt;/ref&gt; The healthcare-associated infection (HAI) rates measure infection of patients in a particular hospital. This allows rates to compared with other hospitals. These infections can often be prevented when healthcare facilities follow guidelines for safe care. To get payment from Medicare, hospitals are required to report data about some infections to the Centers for Disease Control and Prevention's (CDC's) National Healthcare Safety Network (NHSN). Hospitals currently submit information on central line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), surgical site infections (SSIs), MRSA Bacteremia, and C. difficile laboratory-identified events. The public reporting of these data is an effort by the Department of Health and Human Services.&lt;ref&gt;{{cite web | title = Healthcare-Associated Infections (HAI) data | publisher = Medicare.gov| url = https://www.medicare.gov/hospitalcompare/Data/Healthcare-Associated-Infections.html | accessdate = 2017-01-09 }}&lt;/ref&gt;<br /> <br /> 卫生保健机构根据联合委员会发布的准则，定期跟踪其感染率。医疗保健相关感染（HAI）率用于衡量特定医院中患者的感染情况。 这样可以将感染率与其他医院相比。 当医疗机构遵循安全护理指南时，通常可以预防这些感染。 为了从Medicare获得付款，医院必须向疾病控制与预防中心（CDC）的国家医疗安全网络（NHSN）报告有关某些感染的数据。 医院目前提交有关中心线相关的血液感染（CLABSI），导管相关的尿路感染（CAUTI），手术部位感染（SSI），MRSA细菌血症和艰难梭菌实验室鉴定事件的信息。 这些数据的公开报告是卫生与公共服务部的一项工作。<br /> <br /> For meaningful comparisons of infection rates, populations must be very similar between the two or more assessments. However, a problem with mean rates is that they cannot reflect differences in risk between populations.&lt;ref&gt;{{cite web | title= NHSN Standardized Infection Ratio |url = https://www.cdc.gov/nhsn/pdfs/ps-analysis-resources/nhsn-sir-guide.pdf | accessdate = 2017-01-09 | date= December 2016 }}&lt;/ref&gt;<br /> <br /> 为了有意义地比较感染率，两次或更多次评估之间的人群必须非常相似。 但是，平均感染率存在的问题是它们无法反映人群之间的风险差异。<br /> <br /> == 参考文献 References ==<br /> <br /> {{reflist}}<br /> <br /> == 外部链接 External links ==<br /> <br /> <br /> * [http://shea-online.org The Society for Healthcare Epidemiology of America] epidemiologists or physicians in infection control.<br /> * [http://www.apic.org Association for Professionals in Infection Control and Epidemiology] infection prevention and control professionals.<br /> * [http://www.cbic.org The Certification Board of Infection Control and Epidemiology, Inc.]<br /> <br /> <br /> <br /> <br /> &lt;small&gt;此页参考来源：维基百科：Infection rate</div>

Potatozh https://wiki.swarma.org/index.php?title=%E6%84%9F%E6%9F%93%E7%8E%87&diff=16606 2020-10-23T12:32:27Z

<p>Potatozh：</p> <hr /> <div>此词条暂由Potatozh初译，未经审校，带来阅读不便，请见谅。<br /> <br /> <br /> An infection rate (or incident rate) is the probability or risk of an infection in a population. It is used to measure the frequency of occurrence of new instances of infection within a population during a specific time period.<br /> <br /> 感染率（或发病率）是人群中感染的可能性或风险。 它用于衡量特定时间段内某个人群中新感染病例的发生频率。<br /> <br /> [[File:1.png|400px]]<br /> <br /> The number of infections equals the cases identified in the study or observed. An example might by HIV infection during a specific time period in the defined population. The population at risk are the cases appearing in the population during the same time period. An example would be all the people in a city during a specific time period. The constant, or K is assigned a value of 100 to represent a percentage. An example would be to find the percentage of people in a city who are infected with HIV: 6,000 cases in March divided by the population of a city (one million) multiplied by the constant (K) would give an infection rate of 0.6%. Calculating the infection rate is used to analyze trends for the purpose of infection and disease control.&lt;ref&gt;{{cite web | title = Calculation of Infection Rates|url = http://health.utah.gov/epi/diseases/HAI/resources/Cal_Inf_Rates.pdf |publisher = Utah Department of Health|date= 2017| accessdate = 2017-01-09 }}&lt;/ref&gt;<br /> <br /> 感染数量等于研究中确诊或观察到的病例。 一个例子可能是在特定时期内确诊人群中的HIV感染。处于风险中的人口数目是同一时期内人口数目。一个示例是特定时间段内城市中的所有人。常数K被赋值等于100，以表示百分比。例如，要找到一个城市中感染艾滋病毒的人口百分比：3月的时候有6,000例病例除以一个城市人口（一百万）乘以常数（K）得出的感染率为0.6％。计算感染率通常是被用于分析趋势，以达到控制感染和疾病的目的。<br /> <br /> An online infection rate calculator has been developed by the Centers for Disease Control and Prevention that allows the determination of the Streptococcal A infection rate in a population.&lt;ref&gt;{{cite web | title = Group A Streptococcus Calculator | publisher=Centers for Disease Control and Prevention |date= 4 July 2008 |url = https://www2a.cdc.gov/ncidod/dbmd/abcs/calc/calc_new/calculator.html | accessdate = 2017-01-09 }}&lt;/ref&gt;<br /> <br /> 疾病控制与预防中心已经开发了一个在线感染率计算器，可以确定人群中链球菌A的感染率。<br /> <br /> == 临床应用 Clinical applications ==<br /> <br /> Health care facilities routinely track their infection rates according to the guidelines issued by the Joint Commission.&lt;ref&gt;{{cite web | title= Basic Statistical Data Used in Acute Care Facilities | publisher = JB Learning | url = http://www.jblearning.com/samples/0763750344/45561_CH01.pdf | accessdate = 2017-01-09 }}&lt;/ref&gt; The healthcare-associated infection (HAI) rates measure infection of patients in a particular hospital. This allows rates to compared with other hospitals. These infections can often be prevented when healthcare facilities follow guidelines for safe care. To get payment from Medicare, hospitals are required to report data about some infections to the Centers for Disease Control and Prevention's (CDC's) National Healthcare Safety Network (NHSN). Hospitals currently submit information on central line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), surgical site infections (SSIs), MRSA Bacteremia, and C. difficile laboratory-identified events. The public reporting of these data is an effort by the Department of Health and Human Services.&lt;ref&gt;{{cite web | title = Healthcare-Associated Infections (HAI) data | publisher = Medicare.gov| url = https://www.medicare.gov/hospitalcompare/Data/Healthcare-Associated-Infections.html | accessdate = 2017-01-09 }}&lt;/ref&gt;<br /> <br /> 卫生保健机构根据联合委员会发布的准则，定期跟踪其感染率。医疗保健相关感染（HAI）率用于衡量特定医院中患者的感染情况。 这样可以将感染率与其他医院相比。 当医疗机构遵循安全护理指南时，通常可以预防这些感染。 为了从Medicare获得付款，医院必须向疾病控制与预防中心（CDC）的国家医疗安全网络（NHSN）报告有关某些感染的数据。 医院目前提交有关中心线相关的血液感染（CLABSI），导管相关的尿路感染（CAUTI），手术部位感染（SSI），MRSA细菌血症和艰难梭菌实验室鉴定事件的信息。 这些数据的公开报告是卫生与公共服务部的一项工作。<br /> <br /> For meaningful comparisons of infection rates, populations must be very similar between the two or more assessments. However, a problem with mean rates is that they cannot reflect differences in risk between populations.&lt;ref&gt;{{cite web | title= NHSN Standardized Infection Ratio |url = https://www.cdc.gov/nhsn/pdfs/ps-analysis-resources/nhsn-sir-guide.pdf | accessdate = 2017-01-09 | date= December 2016 }}&lt;/ref&gt;<br /> <br /> 为了有意义地比较感染率，两次或更多次评估之间的人群必须非常相似。 但是，平均感染率存在的问题是它们无法反映人群之间的风险差异。<br /> <br /> == 参考文献 References ==<br /> <br /> {{reflist}}<br /> <br /> == 外部链接 External links ==<br /> <br /> <br /> * [http://shea-online.org The Society for Healthcare Epidemiology of America] epidemiologists or physicians in infection control.<br /> * [http://www.apic.org Association for Professionals in Infection Control and Epidemiology] infection prevention and control professionals.<br /> * [http://www.cbic.org The Certification Board of Infection Control and Epidemiology, Inc.]<br /> <br /> &lt;small&gt;此页参考来源：维基百科：Infection rate</div>

Potatozh https://wiki.swarma.org/index.php?title=%E6%96%87%E4%BB%B6:SIR_model_anim.gif&diff=16603 2020-10-23T12:22:44Z

<p>Potatozh：基于MsUpload的文件上传</p> <hr /> <div>基于MsUpload的文件上传</div>

Potatozh https://wiki.swarma.org/index.php?title=%E6%96%87%E4%BB%B6:1920px-SIR-Modell.svg.png&diff=16602 2020-10-23T12:22:37Z

<p>Potatozh：基于MsUpload的文件上传</p> <hr /> <div>基于MsUpload的文件上传</div>

Potatozh https://wiki.swarma.org/index.php?title=%E5%9F%BA%E6%9C%AC%E5%86%8D%E7%94%9F%E6%95%B0&diff=16598 2020-10-23T08:16:37Z

<p>Potatozh：创建页面，内容为“基本再生数（basic reproduction number）是传染病研究中一个重要的量，记为$R_0$，下面给出它的定义。考虑疾病传播的最初阶段，…”</p> <hr /> <div>基本再生数（basic reproduction number）是传染病研究中一个重要的量，记为$R_0$，下面给出它的定义。考虑疾病传播的最初阶段，此时只有少数的疾病案例，其他人都是易感个体，传染病学术语中通常称为幼稚人口（native population），假如其中一个易感个体在疾病爆发以后被感染，那么基本再生数指的就是这个被传染的人在他恢复之前将疾病平均传染给了多少个人。例如，如果每个被感染者将疾病平均又传染给了其他两个人，则$R_0=2$。如果有一半的人将疾病分别又传染给另一个人，而剩余的一半人没有传染给其他任何人，那么$R_0=1/2$，依次类推。<br /> <br /> 如果$R_0=2$，那么每个得病的人就会平均传染两个人，而这两个人中的每一个又会再传染另外两个人，如此下去，每一轮新的疾病的案例都会加倍，从而出现指数增长规律。相反，如果$R_0=1/2$，那么疾病就会以指数形式衰减。$R_0=1$是增长和减少的分割点，因此它也标示着传染病阈值（epidemic threshold），偏离此值时，疾病要么倍增要么消亡。<br /> <br /> 以SIR模型为例来说，其$R_0$值可以通过如下方式计算得到:<br /> <br /> 由于SIR模型中，任意时间间隔$\delta\tau$内，个体恢复的概率为$\gamma\delta\tau$，不能恢复的概率为$1-\gamma\delta\tau$。因此，在总时间$\tau$后，个体仍然处于感染态的概率为<br /> \begin{equation}<br /> \lim_{\delta\tau\to0}(1-\gamma\delta\tau)^{\tau/\delta\tau}=e^{-\gamma\tau},<br /> \end{equation}<br /> 同时，个体保持感染态，然后在$\tau$到$\tau+d\tau$区间恢复的概率$p(\tau)d\tau$可以表示为上述概率与$\gamma d\tau$的乘积，即<br /> \begin{equation}<br /> p(\tau)d\tau=\gamma e^{-\gamma\tau}d\tau,<br /> \end{equation}<br /> 通过上式我们得到了感染个体在恢复以前处于感染态的时间长度$\tau$的分布。由于基本再生数$R_0$指的就是这个被传染的人在他恢复之前将疾病平均传染给了多少个人。所以对于SIR模型的$R_0$值，如果一个人在时间$\tau$内仍然具有传染性，则在这段时间内，与之接触的人的期望值为$\beta\tau$。$R_0$的定义是针对幼稚人口给出的，而在幼稚人口中接触到的所有人都是易感者，因此接触到的所有人都是易感者，因此$\beta\tau$也就是感染个体将传染的总人数。对$\beta\tau$中的$\tau$的分布取平均，就可以得到$R_0$的平均值：<br /> \begin{equation}<br /> R_0=\beta\gamma\int^\infty_0 \tau e^{-\gamma\tau}d\tau=\frac{\beta}{\gamma}.<br /> \end{equation}</div>

Potatozh https://wiki.swarma.org/index.php?title=%E5%8F%8D%E5%BA%94%E6%89%A9%E6%95%A3%E6%A8%A1%E5%9E%8B%E5%92%8C%E4%BA%9A%E7%A7%8D%E7%BE%A4%E6%A8%A1%E5%9E%8B&diff=16597 2020-10-23T08:15:16Z

<p>Potatozh：创建页面，内容为“一般来说，社会行为和人类流动性模型经常被构建为反应扩散过程，其中每个节点$i$可以容纳任何非负整数$\mathcal{N}(i)$个粒…”</p> <hr /> <div>一般来说，社会行为和人类流动性模型经常被构建为反应扩散过程，其中每个节点$i$可以容纳任何非负整数$\mathcal{N}(i)$个粒子，因此系统的总粒子数为$\mathcal{N}=\sum_i\mathcal{N}(i)$。 该粒子——网络框架中的节点内的每个粒子只能沿着连接节点的连边扩散，扩散系数取决于节点度或其他节点属性等。不同系统里的节点内的粒子的反应规则不同。粒子——网络的框架简单示意图如图？？？？？？所示。<br /> <br /> 为了对网络中的反应扩散系统进行理论上的分析描述，我们允许节点之间具有异质的连接模式。对这些系统的第一种分析方法是考虑将基于度的平均场理论方法扩展到具有任意度分布的网络中的反应扩散系统。简单起见，首先考虑一个任意网络结构的简单系统，其中粒子间没有相互作用或反应，粒子只会在网络上扩散的的情况。因此，通过以度$k$来分类的话，可以将系统简单表示为： <br /> \begin{equation} <br /> \mathcal{N}_k=\frac{1}{N_k}\sum_{i\in\mathcal{V}(k)}\mathcal{N}(i) <br /> \end{equation} <br /> 其中$N_k=NP(k)$表示度为k的节点数量，$\mathcal{V}(k)$表示度为$k$的节点集合，$\mathcal{N}_k$表示度为$k$的节点的平均粒子数。基于度的平均场理论假设度为$k$的节点，同理节点中的粒子，在统计上是等效的。在这种近似的假设下，粒子随机扩散的动力学可以由如下平均场理论方程表示： <br /> \begin{equation} <br /> \frac{d\mathcal{N}_k}{dt}=-d_k\mathcal{N}_k(t)+k\sum_{k'}P(k'|k)d_{k'k}\mathcal{N}_{k'}(t) <br /> \end{equation} <br /> 方程中的第一项表示每单位时间内有$d_k$部分的粒子从度为$k$的节点中离开，第二项表示粒子从邻居节点扩散到度为$k$的节点中，该项与连边数$k$成正比。条件概率$P(k'|k)$表示的是一条边的一端连接度为$k$的节点时，其另一端指向度为$k'$的概率。$d_{k'k}$表示的是两端连接了度为$k$和$k'$节点的连边的扩散率。在均匀扩散的最简单情况下，每个粒子从其所在的节点以速率$r$扩散，因此沿着每条连边的扩散率为$d_{k'k}=r/k'$。在无关联的网络上，$P(k'|k)=k'P(k')/\left&lt;k\right&gt;$，因此在稳态$d\mathcal{N}/dt=0$时，很容易得到解（Colizza等，2007b； Noh和Rieger，2004）： <br /> \begin{equation} <br /> \mathcal{N}_k=\frac{k}{\left&lt;k\right&gt;}\frac{\mathcal{N}}{N} <br /> \end{equation} <br /> 上式显式地给出了系统中粒子扩散的描述，并指出网络拓扑结构在反应扩散过程中的重要性。从式中可以看到，一个节点的度越大，则其被扩散中的粒子访问的可能性就越大。<br /> <br /> 通过将反应项添加到上述方程中，可以将上述方法推广到具有不同状态的粒子发生反应的情形（Colizza等，2007b）。现在，我们描述在该情形下的SIS模型情况，其中单位时间的感染概率为$\beta$，恢复概率为$\mu$。我们考虑在具有$N$个节点和度分布为$P(k)$的异质网络中扩散的$\mathcal{N}$个个体。网络的每个节点$i$分别具有$I(i)$个感染态个体和$S(i)$个易感态个体（取值为非负整数值）。这种建模体系描述了空间结构相互作用的亚种群，例如城市位置、城市区域或确定的地理区域（Gren fall和Harwood，1997； Hanski和Gaggiotti，2004），并且通常被称为亚种群方法。网络中的每个节点代表一个亚种群，仓室动力学表示了处于相同位置的个体可能会相互接触并根据系统对应的感染动力学改变自身的状态的可能性。亚群之间的相互作用是个体从一个亚种群迁移到另一个亚种群的结果。为简单起见，我们可以假设个体从他们所在的节点离开沿着任意一条连边扩散的概率为$p_I=p_S=1$。这意味着每个时间步，度为$k$的节点上的一个个体将以概率$1/k$扩散到其最近的邻居之一。为了得到系统的动力学方程，我们定义如下的量： <br /> \begin{equation} <br /> I_k=\frac{1}{N_k}\sum_{i\in\mathcal{V}(k)}I(i) <br /> \end{equation} <br /> \begin{equation} <br /> S_k=\frac{1}{N_k}\sum_{i\in\mathcal{V}(k)}S(i) <br /> \end{equation} <br /> 其中$\mathcal{V}(k)$表示度为$k$的节点集合。上面的两式表示的是度为$k$的节点的平均感染态和易感态个体数，显然有$\mathcal{N}_k=I_k+S_k$。因此，系统的动力学方程可以表示为 <br /> \begin{equation} <br /> I_k(t+1)=k\sum_{k'}P(k|k')\frac{1}{k'}[(1-\mu)I_{k'}(t)+\beta\Gamma_{k'}(t)] <br /> \end{equation} <br /> 其中$\Gamma_{k'}(t)$为相互作用的核，是关于$I_{k'}$和$S_{k'}$的函数。该方程是通过考虑在每个时间步，度为$k$的节点上的粒子先发生反应，然后以1的概率从节点扩散出去得到的。在无关联网络的情况下，上式可以写为 <br /> \begin{equation} <br /> I_k(t+1)=\frac{k}{\left&lt;k\right&gt;}[(1-\mu)\bar{I}(t)+\beta\Gamma] <br /> \end{equation} <br /> 其中$\bar{I}(t)=\sum_k P(k)I_k$表示网络中每个节点的平均感染态个体的数量。$\Gamma=\sum_k P(k)\Gamma_k$。类似地，易感态个体的动力学方程可以表示为 <br /> \begin{equation} <br /> S_k(t+1)=\frac{k}{\left&lt;k\right&gt;}[\bar{S}(t)+\mu\bar{I}(t)-\beta\Gamma] <br /> \end{equation} <br /> 其中$\bar{S}(t)=\sum_k P(k)S_k$。</div>

Potatozh https://wiki.swarma.org/index.php?title=%E7%BD%91%E7%BB%9C%E7%A7%91%E5%AD%A6%E4%B8%93%E9%A2%98&diff=16596 2020-10-23T08:12:51Z

<p>Potatozh：</p> <hr /> <div><br /> == 张江——演化网络 ==<br /> {{div col|colwidth=15em}}<br /> * [[玻色-爱因斯坦凝聚]]<br /> * [[双曲空间]]<br /> * [[复杂网络双曲生长模型]]<br /> * [[克洛内克积（Kronecker Product）]]<br /> * [[克罗内克图]]<br /> * [[网络的密集化现象（Densification）]]<br /> * [[加速生长（Accelerating Growth）]]<br /> * [[匹配生长模型]]<br /> {{div col end}}<br /> == 陈清华——无标度性质 ==<br /> {{div col|colwidth=15em}}<br /> * [[无标度网络]]<br /> * [[无标度性]]<br /> * [[度分布]]<br /> * [[幂律分布]]<br /> * [[80/20原则]]<br /> * [[Pareto分布]]<br /> * [[Zipf律]]<br /> * [[分形]]<br /> * [[自组织临界]]<br /> * [[沙堆模型]]<br /> * [[无标度网络的鲁棒性与脆弱性——阿喀琉斯之踵]]<br /> * [[蒙特卡洛模拟]]<br /> * [[KS检验]]<br /> {{div col end}}<br /> <br /> == 史定华——BA模型 ==<br /> {{div col|colwidth=15em}}<br /> * [[随机变量]]<br /> * [[随机过程]]<br /> * [[概率分布]]<br /> * [[概率密度函数]]<br /> * [[分布函数]]<br /> * [[补分布函数]]<br /> * [[幂律分布]]<br /> * [[重尾分布]]<br /> * [[幂律哀减尾部]]<br /> {{div col end}}<br /> <br /> == 许小可——随机网络 ==<br /> <br /> {{div col|colwidth=15em}}<br /> * [[二项分布]]<br /> * [[泊松分布]]<br /> * [[幂率分布]]<br /> * [[六度分离]]<br /> * [[小世界]]<br /> * [[相变]]<br /> * [[临界点]]<br /> * [[聚集系数]]<br /> * [[巨连通分支]]<br /> * [[度分布]]<br /> * [[格子网络]]<br /> {{div col end}}<br /> <br /> == 陈关荣——网络鲁棒性 ==<br /> <br /> [1] Percolation theory [仅需&quot;阈值&quot; (threshold) 的基本概念] <br /> https://zh.wikipedia.org/wiki/%E6%B8%97%E6%B5%81%E7%90%86%E8%AE%BA <br /> <br /> [2] Cascading failures [级联崩溃] <br /> https://en.wikipedia.org/wiki/Cascading_failure <br /> <br /> [3] Attacks [攻击] <br /> Node-removal and edge-removal attacks [对节点和连边的攻击] <br /> Random attack and malicious attack [随机攻击和恶意攻击] <br /> Attack threshold [攻击凑效的阈值] <br /> <br /> [4] Robustness (and Fragility) [鲁棒性（与 脆弱性）] <br /> https://baike.baidu.com/item/%E9%B2%81%E6%A3%92%E6%80%A7 <br /> https://en.wikipedia.org/wiki/Robustness_of_complex_networks <br /> Achilles heel: https://baike.baidu.com/item/%E9%98%BF%E5%96%80%E7%90%89%E6%96%AF%E4%B9%8B%E8%B8%B5/340132?fromtitle=Achilles%20heel&amp;fromid=8705928<br /> <br /> == 传播 ==<br /> <br /> {{div col|colwidth=15em}}<br /> * [[感染率]]<br /> * [[传染病的数学模型]]<br /> * [[阈值模型]]<br /> * [[理论分析方法]]<br /> * [[反应扩散模型和亚种群模型]]<br /> * [[基本再生数（Basic Reproduction Number）]]<br /> * [[非马尔科夫疾病传播（Non-Markovian Epidemic Spreading）]]<br /> * [[免疫策略（Immunization Strategy）]]<br /> {{div col end}}<br /> <br /> === 理论 ===<br /> ==== 已有词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[图论]]<br /> * [[WS小世界模型]]<br /> * [[社区结构]]<br /> * [[复杂网络]]<br /> * [[网络效应]]<br /> {{div col end}}<br /> <br /> ==== 待翻译词条 ==== <br /> {{div col|colwidth=15em}}<br /> * [[网络可控性]]<br /> * [[组合优化]]<br /> * [[互惠性]]<br /> * [[三元闭包]]<br /> * [[同质性]]<br /> * [[传递关系]]<br /> * [[平衡理论]]<br /> * [[社会影响]]<br /> * [[复杂传染病]]<br /> {{div col end}}<br /> ==== 待审校词条 ==== <br /> {{div col|colwidth=15em}}<br /> * [[链路分析]]<br /> * [[图的绘制]]<br /> * [[演化网络]]<br /> * [[相变]]<br /> <br /> * [[优先链接]]<br /> {{div col end}}<br /> <br /> === 图的特征 ===<br /> <br /> ==== 已翻译词条 ====<br /> *[[超图]]<br /> <br /> ==== 待翻译词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[团（图论）]] <br /> * [[切]]<br /> *[[图论常用概念表]]<br /> {{div col end}}<br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[环]]<br /> *[[邻接矩阵]]<br /> *[[关联矩阵]]<br /> *[[顶点]] <br /> *[[路径]]<br /> *[[二分图]]<br /> *[[随机图]]<br /> *[[图距离]]<br /> *[[图（抽象数据类型）]]<br /> <br /> {{div col end}}<br /> <br /> <br /> === 网络特性 ===<br /> ==== 已建立词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[集聚系数]]<br /> * [[幂律分布]]<br /> * [[节点中心性]]<br /> * [[HITS算法]]<br /> * [[六度分隔]]<br /> * [[PageRank算法]]<br /> * [[模块度]]<br /> * [[链路预测]]<br /> * [[随机网络模型]]<br /> * [[渗流理论]]（待完善）<br /> * [[无标度网络]]<br /> <br /> {{div col end}}<br /> <br /> <br /> ==== 待建立词条 ====<br /> {{div col|colwidth=15em}}<br /> <br /> * [[布尔网络]] <br /> *[[网络连通性]]<br /> *[[LFR算法]]<br /> {{div col end}}<br /> <br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[度]]<br /> *[[度分布]] <br /> *[[网络中心性]] <br /> *[[介数中心性]]<br /> {{div col end}}<br /> === 其他词条===<br /> <br /> <br /> ==== 待建立词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[埃尔德什数理论]] <br /> * [[巨连通分支]]<br /> * [[鲁棒性]]<br /> * [[攻击耐受性]]<br /> * [[级联故障]]<br /> * [[NP-难问题]] <br /> {{div col end}}<br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[泊松分布]] <br /> * [[枢纽节点]]<br /> * [[社交网络]]<br /> * [[层次聚类]]<br /> * [[生物网络]]<br /> {{div col end}}</div>

Potatozh https://wiki.swarma.org/index.php?title=%E7%BD%91%E7%BB%9C%E7%A7%91%E5%AD%A6%E4%B8%93%E9%A2%98&diff=16595 2020-10-23T08:12:09Z

<p>Potatozh：</p> <hr /> <div><br /> == 张江——演化网络 ==<br /> {{div col|colwidth=15em}}<br /> * [[玻色-爱因斯坦凝聚]]<br /> * [[双曲空间]]<br /> * [[复杂网络双曲生长模型]]<br /> * [[克洛内克积（Kronecker Product）]]<br /> * [[克罗内克图]]<br /> * [[网络的密集化现象（Densification）]]<br /> * [[加速生长（Accelerating Growth）]]<br /> * [[匹配生长模型]]<br /> {{div col end}}<br /> == 陈清华——无标度性质 ==<br /> {{div col|colwidth=15em}}<br /> * [[无标度网络]]<br /> * [[无标度性]]<br /> * [[度分布]]<br /> * [[幂律分布]]<br /> * [[80/20原则]]<br /> * [[Pareto分布]]<br /> * [[Zipf律]]<br /> * [[分形]]<br /> * [[自组织临界]]<br /> * [[沙堆模型]]<br /> * [[无标度网络的鲁棒性与脆弱性——阿喀琉斯之踵]]<br /> * [[蒙特卡洛模拟]]<br /> * [[KS检验]]<br /> {{div col end}}<br /> <br /> == 史定华——BA模型 ==<br /> {{div col|colwidth=15em}}<br /> * [[随机变量]]<br /> * [[随机过程]]<br /> * [[概率分布]]<br /> * [[概率密度函数]]<br /> * [[分布函数]]<br /> * [[补分布函数]]<br /> * [[幂律分布]]<br /> * [[重尾分布]]<br /> * [[幂律哀减尾部]]<br /> {{div col end}}<br /> <br /> == 许小可——随机网络 ==<br /> <br /> {{div col|colwidth=15em}}<br /> * [[二项分布]]<br /> * [[泊松分布]]<br /> * [[幂率分布]]<br /> * [[六度分离]]<br /> * [[小世界]]<br /> * [[相变]]<br /> * [[临界点]]<br /> * [[聚集系数]]<br /> * [[巨连通分支]]<br /> * [[度分布]]<br /> * [[格子网络]]<br /> {{div col end}}<br /> <br /> == 陈关荣——网络鲁棒性 ==<br /> <br /> [1] Percolation theory [仅需&quot;阈值&quot; (threshold) 的基本概念] <br /> https://zh.wikipedia.org/wiki/%E6%B8%97%E6%B5%81%E7%90%86%E8%AE%BA <br /> <br /> [2] Cascading failures [级联崩溃] <br /> https://en.wikipedia.org/wiki/Cascading_failure <br /> <br /> [3] Attacks [攻击] <br /> Node-removal and edge-removal attacks [对节点和连边的攻击] <br /> Random attack and malicious attack [随机攻击和恶意攻击] <br /> Attack threshold [攻击凑效的阈值] <br /> <br /> [4] Robustness (and Fragility) [鲁棒性（与 脆弱性）] <br /> https://baike.baidu.com/item/%E9%B2%81%E6%A3%92%E6%80%A7 <br /> https://en.wikipedia.org/wiki/Robustness_of_complex_networks <br /> Achilles heel: https://baike.baidu.com/item/%E9%98%BF%E5%96%80%E7%90%89%E6%96%AF%E4%B9%8B%E8%B8%B5/340132?fromtitle=Achilles%20heel&amp;fromid=8705928<br /> <br /> == 传播 ==<br /> <br /> {{div col|colwidth=15em}}<br /> * [[感染率]]<br /> * [[传染病的数学模型]]<br /> * [[阈值模型]]<br /> * [[理论分析方法]]<br /> * [[反应扩散模型和亚种群模型（Reaction-diffusion Processes and Metapopulation Models）]]<br /> * [[基本再生数（Basic Reproduction Number）]]<br /> * [[非马尔科夫疾病传播（Non-Markovian Epidemic Spreading）]]<br /> * [[免疫策略（Immunization Strategy）]]<br /> {{div col end}}<br /> <br /> === 理论 ===<br /> ==== 已有词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[图论]]<br /> * [[WS小世界模型]]<br /> * [[社区结构]]<br /> * [[复杂网络]]<br /> * [[网络效应]]<br /> {{div col end}}<br /> <br /> ==== 待翻译词条 ==== <br /> {{div col|colwidth=15em}}<br /> * [[网络可控性]]<br /> * [[组合优化]]<br /> * [[互惠性]]<br /> * [[三元闭包]]<br /> * [[同质性]]<br /> * [[传递关系]]<br /> * [[平衡理论]]<br /> * [[社会影响]]<br /> * [[复杂传染病]]<br /> {{div col end}}<br /> ==== 待审校词条 ==== <br /> {{div col|colwidth=15em}}<br /> * [[链路分析]]<br /> * [[图的绘制]]<br /> * [[演化网络]]<br /> * [[相变]]<br /> <br /> * [[优先链接]]<br /> {{div col end}}<br /> <br /> === 图的特征 ===<br /> <br /> ==== 已翻译词条 ====<br /> *[[超图]]<br /> <br /> ==== 待翻译词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[团（图论）]] <br /> * [[切]]<br /> *[[图论常用概念表]]<br /> {{div col end}}<br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[环]]<br /> *[[邻接矩阵]]<br /> *[[关联矩阵]]<br /> *[[顶点]] <br /> *[[路径]]<br /> *[[二分图]]<br /> *[[随机图]]<br /> *[[图距离]]<br /> *[[图（抽象数据类型）]]<br /> <br /> {{div col end}}<br /> <br /> <br /> === 网络特性 ===<br /> ==== 已建立词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[集聚系数]]<br /> * [[幂律分布]]<br /> * [[节点中心性]]<br /> * [[HITS算法]]<br /> * [[六度分隔]]<br /> * [[PageRank算法]]<br /> * [[模块度]]<br /> * [[链路预测]]<br /> * [[随机网络模型]]<br /> * [[渗流理论]]（待完善）<br /> * [[无标度网络]]<br /> <br /> {{div col end}}<br /> <br /> <br /> ==== 待建立词条 ====<br /> {{div col|colwidth=15em}}<br /> <br /> * [[布尔网络]] <br /> *[[网络连通性]]<br /> *[[LFR算法]]<br /> {{div col end}}<br /> <br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[度]]<br /> *[[度分布]] <br /> *[[网络中心性]] <br /> *[[介数中心性]]<br /> {{div col end}}<br /> === 其他词条===<br /> <br /> <br /> ==== 待建立词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[埃尔德什数理论]] <br /> * [[巨连通分支]]<br /> * [[鲁棒性]]<br /> * [[攻击耐受性]]<br /> * [[级联故障]]<br /> * [[NP-难问题]] <br /> {{div col end}}<br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[泊松分布]] <br /> * [[枢纽节点]]<br /> * [[社交网络]]<br /> * [[层次聚类]]<br /> * [[生物网络]]<br /> {{div col end}}</div>

Potatozh https://wiki.swarma.org/index.php?title=%E7%BD%91%E7%BB%9C%E7%A7%91%E5%AD%A6%E4%B8%93%E9%A2%98&diff=16594 2020-10-23T08:08:23Z

<p>Potatozh：</p> <hr /> <div><br /> == 张江——演化网络 ==<br /> {{div col|colwidth=15em}}<br /> * [[玻色-爱因斯坦凝聚]]<br /> * [[双曲空间]]<br /> * [[复杂网络双曲生长模型]]<br /> * [[克洛内克积（Kronecker Product）]]<br /> * [[克罗内克图]]<br /> * [[网络的密集化现象（Densification）]]<br /> * [[加速生长（Accelerating Growth）]]<br /> * [[匹配生长模型]]<br /> {{div col end}}<br /> == 陈清华——无标度性质 ==<br /> {{div col|colwidth=15em}}<br /> * [[无标度网络]]<br /> * [[无标度性]]<br /> * [[度分布]]<br /> * [[幂律分布]]<br /> * [[80/20原则]]<br /> * [[Pareto分布]]<br /> * [[Zipf律]]<br /> * [[分形]]<br /> * [[自组织临界]]<br /> * [[沙堆模型]]<br /> * [[无标度网络的鲁棒性与脆弱性——阿喀琉斯之踵]]<br /> * [[蒙特卡洛模拟]]<br /> * [[KS检验]]<br /> {{div col end}}<br /> <br /> == 史定华——BA模型 ==<br /> {{div col|colwidth=15em}}<br /> * [[随机变量]]<br /> * [[随机过程]]<br /> * [[概率分布]]<br /> * [[概率密度函数]]<br /> * [[分布函数]]<br /> * [[补分布函数]]<br /> * [[幂律分布]]<br /> * [[重尾分布]]<br /> * [[幂律哀减尾部]]<br /> {{div col end}}<br /> <br /> == 许小可——随机网络 ==<br /> <br /> {{div col|colwidth=15em}}<br /> * [[二项分布]]<br /> * [[泊松分布]]<br /> * [[幂率分布]]<br /> * [[六度分离]]<br /> * [[小世界]]<br /> * [[相变]]<br /> * [[临界点]]<br /> * [[聚集系数]]<br /> * [[巨连通分支]]<br /> * [[度分布]]<br /> * [[格子网络]]<br /> {{div col end}}<br /> <br /> == 陈关荣——网络鲁棒性 ==<br /> <br /> [1] Percolation theory [仅需&quot;阈值&quot; (threshold) 的基本概念] <br /> https://zh.wikipedia.org/wiki/%E6%B8%97%E6%B5%81%E7%90%86%E8%AE%BA <br /> <br /> [2] Cascading failures [级联崩溃] <br /> https://en.wikipedia.org/wiki/Cascading_failure <br /> <br /> [3] Attacks [攻击] <br /> Node-removal and edge-removal attacks [对节点和连边的攻击] <br /> Random attack and malicious attack [随机攻击和恶意攻击] <br /> Attack threshold [攻击凑效的阈值] <br /> <br /> [4] Robustness (and Fragility) [鲁棒性（与 脆弱性）] <br /> https://baike.baidu.com/item/%E9%B2%81%E6%A3%92%E6%80%A7 <br /> https://en.wikipedia.org/wiki/Robustness_of_complex_networks <br /> Achilles heel: https://baike.baidu.com/item/%E9%98%BF%E5%96%80%E7%90%89%E6%96%AF%E4%B9%8B%E8%B8%B5/340132?fromtitle=Achilles%20heel&amp;fromid=8705928<br /> <br /> == 传播 ==<br /> <br /> {{div col|colwidth=15em}}<br /> * [[感染率]]<br /> * [[传染病的数学模型]]<br /> * [[阈值模型]]<br /> * [[理论分析方法]]<br /> * [[反应扩散模型和亚种群模型]]<br /> * [[基本再生数（Basic reproduction number）]]<br /> * [[非马尔科夫疾病传播（Non-Markovian epidemic spreading）]]<br /> * [[免疫策略（Immunization strategy）]]<br /> {{div col end}}<br /> <br /> === 理论 ===<br /> ==== 已有词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[图论]]<br /> * [[WS小世界模型]]<br /> * [[社区结构]]<br /> * [[复杂网络]]<br /> * [[网络效应]]<br /> {{div col end}}<br /> <br /> ==== 待翻译词条 ==== <br /> {{div col|colwidth=15em}}<br /> * [[网络可控性]]<br /> * [[组合优化]]<br /> * [[互惠性]]<br /> * [[三元闭包]]<br /> * [[同质性]]<br /> * [[传递关系]]<br /> * [[平衡理论]]<br /> * [[社会影响]]<br /> * [[复杂传染病]]<br /> {{div col end}}<br /> ==== 待审校词条 ==== <br /> {{div col|colwidth=15em}}<br /> * [[链路分析]]<br /> * [[图的绘制]]<br /> * [[演化网络]]<br /> * [[相变]]<br /> <br /> * [[优先链接]]<br /> {{div col end}}<br /> <br /> === 图的特征 ===<br /> <br /> ==== 已翻译词条 ====<br /> *[[超图]]<br /> <br /> ==== 待翻译词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[团（图论）]] <br /> * [[切]]<br /> *[[图论常用概念表]]<br /> {{div col end}}<br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[环]]<br /> *[[邻接矩阵]]<br /> *[[关联矩阵]]<br /> *[[顶点]] <br /> *[[路径]]<br /> *[[二分图]]<br /> *[[随机图]]<br /> *[[图距离]]<br /> *[[图（抽象数据类型）]]<br /> <br /> {{div col end}}<br /> <br /> <br /> === 网络特性 ===<br /> ==== 已建立词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[集聚系数]]<br /> * [[幂律分布]]<br /> * [[节点中心性]]<br /> * [[HITS算法]]<br /> * [[六度分隔]]<br /> * [[PageRank算法]]<br /> * [[模块度]]<br /> * [[链路预测]]<br /> * [[随机网络模型]]<br /> * [[渗流理论]]（待完善）<br /> * [[无标度网络]]<br /> <br /> {{div col end}}<br /> <br /> <br /> ==== 待建立词条 ====<br /> {{div col|colwidth=15em}}<br /> <br /> * [[布尔网络]] <br /> *[[网络连通性]]<br /> *[[LFR算法]]<br /> {{div col end}}<br /> <br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[度]]<br /> *[[度分布]] <br /> *[[网络中心性]] <br /> *[[介数中心性]]<br /> {{div col end}}<br /> === 其他词条===<br /> <br /> <br /> ==== 待建立词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[埃尔德什数理论]] <br /> * [[巨连通分支]]<br /> * [[鲁棒性]]<br /> * [[攻击耐受性]]<br /> * [[级联故障]]<br /> * [[NP-难问题]] <br /> {{div col end}}<br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[泊松分布]] <br /> * [[枢纽节点]]<br /> * [[社交网络]]<br /> * [[层次聚类]]<br /> * [[生物网络]]<br /> {{div col end}}</div>

Potatozh https://wiki.swarma.org/index.php?title=%E7%BD%91%E7%BB%9C%E7%A7%91%E5%AD%A6%E4%B8%93%E9%A2%98&diff=16577 2020-10-22T13:45:58Z

<p>Potatozh：</p> <hr /> <div><br /> == 张江——演化网络 ==<br /> {{div col|colwidth=15em}}<br /> * [[玻色-爱因斯坦凝聚]]<br /> * [[双曲空间]]<br /> * [[复杂网络双曲生长模型]]<br /> * [[克洛内克积（Kronecker Product）]]<br /> * [[克罗内克图]]<br /> * [[网络的密集化现象（Densification）]]<br /> * [[加速生长（Accelerating Growth）]]<br /> * [[匹配生长模型]]<br /> {{div col end}}<br /> == 陈清华——无标度性质 ==<br /> {{div col|colwidth=15em}}<br /> * [[无标度网络]]<br /> * [[无标度性]]<br /> * [[度分布]]<br /> * [[幂律分布]]<br /> * [[80/20原则]]<br /> * [[Pareto分布]]<br /> * [[Zipf律]]<br /> * [[分形]]<br /> * [[自组织临界]]<br /> * [[沙堆模型]]<br /> * [[无标度网络的鲁棒性与脆弱性——阿喀琉斯之踵]]<br /> * [[蒙特卡洛模拟]]<br /> * [[KS检验]]<br /> {{div col end}}<br /> <br /> == 史定华——BA模型 ==<br /> {{div col|colwidth=15em}}<br /> * [[随机变量]]<br /> * [[随机过程]]<br /> * [[概率分布]]<br /> * [[概率密度函数]]<br /> * [[分布函数]]<br /> * [[补分布函数]]<br /> * [[幂律分布]]<br /> * [[重尾分布]]<br /> * [[幂律哀减尾部]]<br /> {{div col end}}<br /> <br /> == 许小可——随机网络 ==<br /> <br /> {{div col|colwidth=15em}}<br /> * [[二项分布]]<br /> * [[泊松分布]]<br /> * [[幂率分布]]<br /> * [[六度分离]]<br /> * [[小世界]]<br /> * [[相变]]<br /> * [[临界点]]<br /> * [[聚集系数]]<br /> * [[巨连通分支]]<br /> * [[度分布]]<br /> * [[格子网络]]<br /> {{div col end}}<br /> <br /> == 陈关荣——网络鲁棒性 ==<br /> <br /> [1] Percolation theory [仅需&quot;阈值&quot; (threshold) 的基本概念] <br /> https://zh.wikipedia.org/wiki/%E6%B8%97%E6%B5%81%E7%90%86%E8%AE%BA <br /> <br /> [2] Cascading failures [级联崩溃] <br /> https://en.wikipedia.org/wiki/Cascading_failure <br /> <br /> [3] Attacks [攻击] <br /> Node-removal and edge-removal attacks [对节点和连边的攻击] <br /> Random attack and malicious attack [随机攻击和恶意攻击] <br /> Attack threshold [攻击凑效的阈值] <br /> <br /> [4] Robustness (and Fragility) [鲁棒性（与 脆弱性）] <br /> https://baike.baidu.com/item/%E9%B2%81%E6%A3%92%E6%80%A7 <br /> https://en.wikipedia.org/wiki/Robustness_of_complex_networks <br /> Achilles heel: https://baike.baidu.com/item/%E9%98%BF%E5%96%80%E7%90%89%E6%96%AF%E4%B9%8B%E8%B8%B5/340132?fromtitle=Achilles%20heel&amp;fromid=8705928<br /> <br /> == 传播 ==<br /> <br /> {{div col|colwidth=15em}}<br /> * [[感染率]]<br /> * [[传染病的数学模型]]<br /> * [[阈值模型]]<br /> * [[理论分析方法]]<br /> * [[反应扩散模型和亚种群模型]]<br /> * [[基本再生数]]<br /> * [[非马尔科夫疾病传播]]<br /> * [[免疫策略]]<br /> {{div col end}}<br /> <br /> === 理论 ===<br /> ==== 已有词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[图论]]<br /> * [[WS小世界模型]]<br /> * [[社区结构]]<br /> * [[复杂网络]]<br /> * [[网络效应]]<br /> {{div col end}}<br /> <br /> ==== 待翻译词条 ==== <br /> {{div col|colwidth=15em}}<br /> * [[网络可控性]]<br /> * [[组合优化]]<br /> * [[互惠性]]<br /> * [[三元闭包]]<br /> * [[同质性]]<br /> * [[传递关系]]<br /> * [[平衡理论]]<br /> * [[社会影响]]<br /> * [[复杂传染病]]<br /> {{div col end}}<br /> ==== 待审校词条 ==== <br /> {{div col|colwidth=15em}}<br /> * [[链路分析]]<br /> * [[图的绘制]]<br /> * [[演化网络]]<br /> * [[相变]]<br /> <br /> * [[优先链接]]<br /> {{div col end}}<br /> <br /> === 图的特征 ===<br /> <br /> ==== 已翻译词条 ====<br /> *[[超图]]<br /> <br /> ==== 待翻译词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[团（图论）]] <br /> * [[切]]<br /> *[[图论常用概念表]]<br /> {{div col end}}<br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[环]]<br /> *[[邻接矩阵]]<br /> *[[关联矩阵]]<br /> *[[顶点]] <br /> *[[路径]]<br /> *[[二分图]]<br /> *[[随机图]]<br /> *[[图距离]]<br /> *[[图（抽象数据类型）]]<br /> <br /> {{div col end}}<br /> <br /> <br /> === 网络特性 ===<br /> ==== 已建立词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[集聚系数]]<br /> * [[幂律分布]]<br /> * [[节点中心性]]<br /> * [[HITS算法]]<br /> * [[六度分隔]]<br /> * [[PageRank算法]]<br /> * [[模块度]]<br /> * [[链路预测]]<br /> * [[随机网络模型]]<br /> * [[渗流理论]]（待完善）<br /> * [[无标度网络]]<br /> <br /> {{div col end}}<br /> <br /> <br /> ==== 待建立词条 ====<br /> {{div col|colwidth=15em}}<br /> <br /> * [[布尔网络]] <br /> *[[网络连通性]]<br /> *[[LFR算法]]<br /> {{div col end}}<br /> <br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[度]]<br /> *[[度分布]] <br /> *[[网络中心性]] <br /> *[[介数中心性]]<br /> {{div col end}}<br /> === 其他词条===<br /> <br /> <br /> ==== 待建立词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[埃尔德什数理论]] <br /> * [[巨连通分支]]<br /> * [[鲁棒性]]<br /> * [[攻击耐受性]]<br /> * [[级联故障]]<br /> * [[NP-难问题]] <br /> {{div col end}}<br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[泊松分布]] <br /> * [[枢纽节点]]<br /> * [[社交网络]]<br /> * [[层次聚类]]<br /> * [[生物网络]]<br /> {{div col end}}</div>

Potatozh https://wiki.swarma.org/index.php?title=%E7%BD%91%E7%BB%9C%E7%A7%91%E5%AD%A6%E4%B8%93%E9%A2%98&diff=16570 2020-10-22T13:00:44Z

<p>Potatozh：</p> <hr /> <div><br /> == 张江——演化网络 ==<br /> {{div col|colwidth=15em}}<br /> * [[玻色-爱因斯坦凝聚]]<br /> * [[双曲空间]]<br /> * [[复杂网络双曲生长模型]]<br /> * [[克洛内克积（Kronecker Product）]]<br /> * [[克罗内克图]]<br /> * [[网络的密集化现象（Densification）]]<br /> * [[加速生长（Accelerating Growth）]]<br /> * [[匹配生长模型]]<br /> {{div col end}}<br /> == 陈清华——无标度性质 ==<br /> {{div col|colwidth=15em}}<br /> * [[无标度网络]]<br /> * [[无标度性]]<br /> * [[度分布]]<br /> * [[幂律分布]]<br /> * [[80/20原则]]<br /> * [[Pareto分布]]<br /> * [[Zipf律]]<br /> * [[分形]]<br /> * [[自组织临界]]<br /> * [[沙堆模型]]<br /> * [[无标度网络的鲁棒性与脆弱性——阿喀琉斯之踵]]<br /> * [[蒙特卡洛模拟]]<br /> * [[KS检验]]<br /> {{div col end}}<br /> <br /> == 史定华——BA模型 ==<br /> {{div col|colwidth=15em}}<br /> * [[随机变量]]<br /> * [[随机过程]]<br /> * [[概率分布]]<br /> * [[概率密度函数]]<br /> * [[分布函数]]<br /> * [[补分布函数]]<br /> * [[幂律分布]]<br /> * [[重尾分布]]<br /> * [[幂律哀减尾部]]<br /> {{div col end}}<br /> <br /> == 许小可——随机网络 ==<br /> <br /> {{div col|colwidth=15em}}<br /> * [[二项分布]]<br /> * [[泊松分布]]<br /> * [[幂率分布]]<br /> * [[六度分离]]<br /> * [[小世界]]<br /> * [[相变]]<br /> * [[临界点]]<br /> * [[聚集系数]]<br /> * [[巨连通分支]]<br /> * [[度分布]]<br /> * [[格子网络]]<br /> {{div col end}}<br /> <br /> == 陈关荣——网络鲁棒性 ==<br /> <br /> [1] Percolation theory [仅需&quot;阈值&quot; (threshold) 的基本概念] <br /> https://zh.wikipedia.org/wiki/%E6%B8%97%E6%B5%81%E7%90%86%E8%AE%BA <br /> <br /> [2] Cascading failures [级联崩溃] <br /> https://en.wikipedia.org/wiki/Cascading_failure <br /> <br /> [3] Attacks [攻击] <br /> Node-removal and edge-removal attacks [对节点和连边的攻击] <br /> Random attack and malicious attack [随机攻击和恶意攻击] <br /> Attack threshold [攻击凑效的阈值] <br /> <br /> [4] Robustness (and Fragility) [鲁棒性（与 脆弱性）] <br /> https://baike.baidu.com/item/%E9%B2%81%E6%A3%92%E6%80%A7 <br /> https://en.wikipedia.org/wiki/Robustness_of_complex_networks <br /> Achilles heel: https://baike.baidu.com/item/%E9%98%BF%E5%96%80%E7%90%89%E6%96%AF%E4%B9%8B%E8%B8%B5/340132?fromtitle=Achilles%20heel&amp;fromid=8705928<br /> <br /> == 传播 ==<br /> <br /> {{div col|colwidth=15em}}<br /> * [[感染率]]<br /> * [[传染病的数学模型]]<br /> * [[阈值模型]]<br /> * [[理论分析方法]]<br /> * [[反应扩散模型和亚种群模型]]<br /> * [[基本再生数]]<br /> * [[马尔科夫疾病传播]]<br /> * [[非马尔科夫疾病传播]]<br /> * [[免疫策略]]<br /> {{div col end}}<br /> <br /> === 理论 ===<br /> ==== 已有词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[图论]]<br /> * [[WS小世界模型]]<br /> * [[社区结构]]<br /> * [[复杂网络]]<br /> * [[网络效应]]<br /> {{div col end}}<br /> <br /> ==== 待翻译词条 ==== <br /> {{div col|colwidth=15em}}<br /> * [[网络可控性]]<br /> * [[组合优化]]<br /> * [[互惠性]]<br /> * [[三元闭包]]<br /> * [[同质性]]<br /> * [[传递关系]]<br /> * [[平衡理论]]<br /> * [[社会影响]]<br /> * [[复杂传染病]]<br /> {{div col end}}<br /> ==== 待审校词条 ==== <br /> {{div col|colwidth=15em}}<br /> * [[链路分析]]<br /> * [[图的绘制]]<br /> * [[演化网络]]<br /> * [[相变]]<br /> <br /> * [[优先链接]]<br /> {{div col end}}<br /> <br /> === 图的特征 ===<br /> <br /> ==== 已翻译词条 ====<br /> *[[超图]]<br /> <br /> ==== 待翻译词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[团（图论）]] <br /> * [[切]]<br /> *[[图论常用概念表]]<br /> {{div col end}}<br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[环]]<br /> *[[邻接矩阵]]<br /> *[[关联矩阵]]<br /> *[[顶点]] <br /> *[[路径]]<br /> *[[二分图]]<br /> *[[随机图]]<br /> *[[图距离]]<br /> *[[图（抽象数据类型）]]<br /> <br /> {{div col end}}<br /> <br /> <br /> === 网络特性 ===<br /> ==== 已建立词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[集聚系数]]<br /> * [[幂律分布]]<br /> * [[节点中心性]]<br /> * [[HITS算法]]<br /> * [[六度分隔]]<br /> * [[PageRank算法]]<br /> * [[模块度]]<br /> * [[链路预测]]<br /> * [[随机网络模型]]<br /> * [[渗流理论]]（待完善）<br /> * [[无标度网络]]<br /> <br /> {{div col end}}<br /> <br /> <br /> ==== 待建立词条 ====<br /> {{div col|colwidth=15em}}<br /> <br /> * [[布尔网络]] <br /> *[[网络连通性]]<br /> *[[LFR算法]]<br /> {{div col end}}<br /> <br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[度]]<br /> *[[度分布]] <br /> *[[网络中心性]] <br /> *[[介数中心性]]<br /> {{div col end}}<br /> === 其他词条===<br /> <br /> <br /> ==== 待建立词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[埃尔德什数理论]] <br /> * [[巨连通分支]]<br /> * [[鲁棒性]]<br /> * [[攻击耐受性]]<br /> * [[级联故障]]<br /> * [[NP-难问题]] <br /> {{div col end}}<br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[泊松分布]] <br /> * [[枢纽节点]]<br /> * [[社交网络]]<br /> * [[层次聚类]]<br /> * [[生物网络]]<br /> {{div col end}}</div>

Potatozh https://wiki.swarma.org/index.php?title=%E7%BD%91%E7%BB%9C%E7%A7%91%E5%AD%A6%E4%B8%93%E9%A2%98&diff=16541 2020-10-22T11:11:36Z

<p>Potatozh：</p> <hr /> <div><br /> == 张江——演化网络 ==<br /> {{div col|colwidth=15em}}<br /> * [[玻色-爱因斯坦凝聚]]<br /> * [[双曲空间]]<br /> * [[复杂网络双曲生长模型]]<br /> * [[克洛内克积（Kronecker Product）]]<br /> * [[克罗内克图]]<br /> * [[网络的密集化现象（Densification）]]<br /> * [[加速生长（Accelerating Growth）]]<br /> * [[匹配生长模型]]<br /> {{div col end}}<br /> == 陈清华——无标度性质 ==<br /> {{div col|colwidth=15em}}<br /> * [[无标度网络]]<br /> * [[无标度性]]<br /> * [[度分布]]<br /> * [[幂律分布]]<br /> * [[80/20原则]]<br /> * [[Pareto分布]]<br /> * [[Zipf律]]<br /> * [[分形]]<br /> * [[自组织临界]]<br /> * [[沙堆模型]]<br /> * [[无标度网络的鲁棒性与脆弱性——阿喀琉斯之踵]]<br /> * [[蒙特卡洛模拟]]<br /> * [[KS检验]]<br /> {{div col end}}<br /> <br /> == 史定华——BA模型 ==<br /> {{div col|colwidth=15em}}<br /> * [[随机变量]]<br /> * [[随机过程]]<br /> * [[概率分布]]<br /> * [[概率密度函数]]<br /> * [[分布函数]]<br /> * [[补分布函数]]<br /> * [[幂律分布]]<br /> * [[重尾分布]]<br /> * [[幂律哀减尾部]]<br /> {{div col end}}<br /> <br /> == 许小可——随机网络 ==<br /> <br /> {{div col|colwidth=15em}}<br /> * [[二项分布]]<br /> * [[泊松分布]]<br /> * [[幂率分布]]<br /> * [[六度分离]]<br /> * [[小世界]]<br /> * [[相变]]<br /> * [[临界点]]<br /> * [[聚集系数]]<br /> * [[巨连通分支]]<br /> * [[度分布]]<br /> * [[格子网络]]<br /> {{div col end}}<br /> <br /> == 陈关荣——网络鲁棒性 ==<br /> <br /> [1] Percolation theory [仅需&quot;阈值&quot; (threshold) 的基本概念] <br /> https://zh.wikipedia.org/wiki/%E6%B8%97%E6%B5%81%E7%90%86%E8%AE%BA <br /> <br /> [2] Cascading failures [级联崩溃] <br /> https://en.wikipedia.org/wiki/Cascading_failure <br /> <br /> [3] Attacks [攻击] <br /> Node-removal and edge-removal attacks [对节点和连边的攻击] <br /> Random attack and malicious attack [随机攻击和恶意攻击] <br /> Attack threshold [攻击凑效的阈值] <br /> <br /> [4] Robustness (and Fragility) [鲁棒性（与 脆弱性）] <br /> https://baike.baidu.com/item/%E9%B2%81%E6%A3%92%E6%80%A7 <br /> https://en.wikipedia.org/wiki/Robustness_of_complex_networks <br /> Achilles heel: https://baike.baidu.com/item/%E9%98%BF%E5%96%80%E7%90%89%E6%96%AF%E4%B9%8B%E8%B8%B5/340132?fromtitle=Achilles%20heel&amp;fromid=8705928<br /> <br /> == 传播 ==<br /> <br /> {{div col|colwidth=15em}}<br /> * [[感染率]]<br /> * [[动力学模型]]<br /> * [[理论分析方法]]<br /> * [[反应扩散模型和亚种群模型]]<br /> * [[基本再生数]]<br /> * [[马尔科夫疾病传播]]<br /> * [[非马尔科夫疾病传播]]<br /> * [[免疫策略]]<br /> {{div col end}}<br /> <br /> === 理论 ===<br /> ==== 已有词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[图论]]<br /> * [[WS小世界模型]]<br /> * [[社区结构]]<br /> * [[复杂网络]]<br /> * [[网络效应]]<br /> {{div col end}}<br /> <br /> ==== 待翻译词条 ==== <br /> {{div col|colwidth=15em}}<br /> * [[网络可控性]]<br /> * [[组合优化]]<br /> * [[互惠性]]<br /> * [[三元闭包]]<br /> * [[同质性]]<br /> * [[传递关系]]<br /> * [[平衡理论]]<br /> * [[社会影响]]<br /> * [[复杂传染病]]<br /> {{div col end}}<br /> ==== 待审校词条 ==== <br /> {{div col|colwidth=15em}}<br /> * [[链路分析]]<br /> * [[图的绘制]]<br /> * [[演化网络]]<br /> * [[相变]]<br /> <br /> * [[优先链接]]<br /> {{div col end}}<br /> <br /> === 图的特征 ===<br /> <br /> ==== 已翻译词条 ====<br /> *[[超图]]<br /> <br /> ==== 待翻译词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[团（图论）]] <br /> * [[切]]<br /> *[[图论常用概念表]]<br /> {{div col end}}<br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[环]]<br /> *[[邻接矩阵]]<br /> *[[关联矩阵]]<br /> *[[顶点]] <br /> *[[路径]]<br /> *[[二分图]]<br /> *[[随机图]]<br /> *[[图距离]]<br /> *[[图（抽象数据类型）]]<br /> <br /> {{div col end}}<br /> <br /> <br /> === 网络特性 ===<br /> ==== 已建立词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[集聚系数]]<br /> * [[幂律分布]]<br /> * [[节点中心性]]<br /> * [[HITS算法]]<br /> * [[六度分隔]]<br /> * [[PageRank算法]]<br /> * [[模块度]]<br /> * [[链路预测]]<br /> * [[随机网络模型]]<br /> * [[渗流理论]]（待完善）<br /> * [[无标度网络]]<br /> <br /> {{div col end}}<br /> <br /> <br /> ==== 待建立词条 ====<br /> {{div col|colwidth=15em}}<br /> <br /> * [[布尔网络]] <br /> *[[网络连通性]]<br /> *[[LFR算法]]<br /> {{div col end}}<br /> <br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[度]]<br /> *[[度分布]] <br /> *[[网络中心性]] <br /> *[[介数中心性]]<br /> {{div col end}}<br /> === 其他词条===<br /> <br /> <br /> ==== 待建立词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[埃尔德什数理论]] <br /> * [[巨连通分支]]<br /> * [[鲁棒性]]<br /> * [[攻击耐受性]]<br /> * [[级联故障]]<br /> * [[NP-难问题]] <br /> {{div col end}}<br /> ==== 待审校词条 ====<br /> {{div col|colwidth=15em}}<br /> * [[泊松分布]] <br /> * [[枢纽节点]]<br /> * [[社交网络]]<br /> * [[层次聚类]]<br /> * [[生物网络]]<br /> {{div col end}}</div>

Potatozh https://wiki.swarma.org/index.php?title=%E6%84%9F%E6%9F%93%E7%8E%87&diff=16481 2020-10-22T02:58:52Z

<p>Potatozh：</p> <hr /> <div>An infection rate (or incident rate) is the probability or risk of an infection in a population. It is used to measure the frequency of occurrence of new instances of infection within a population during a specific time period.<br /> <br /> 感染率（或发病率）是人群中感染的可能性或风险。 它用于衡量特定时间段内某个人群中新感染病例的发生频率。<br /> <br /> [[File:1.png|400px]]<br /> <br /> The number of infections equals the cases identified in the study or observed. An example might by HIV infection during a specific time period in the defined population. The population at risk are the cases appearing in the population during the same time period. An example would be all the people in a city during a specific time period. The constant, or K is assigned a value of 100 to represent a percentage. An example would be to find the percentage of people in a city who are infected with HIV: 6,000 cases in March divided by the population of a city (one million) multiplied by the constant (K) would give an infection rate of 0.6%. Calculating the infection rate is used to analyze trends for the purpose of infection and disease control.&lt;ref&gt;{{cite web | title = Calculation of Infection Rates|url = http://health.utah.gov/epi/diseases/HAI/resources/Cal_Inf_Rates.pdf |publisher = Utah Department of Health|date= 2017| accessdate = 2017-01-09 }}&lt;/ref&gt;<br /> <br /> 感染数量等于研究中确诊或观察到的病例。 一个例子可能是在特定时期内确诊人群中的HIV感染。处于风险中的人口数目是同一时期内人口数目。一个示例是特定时间段内城市中的所有人。常数K被赋值等于100，以表示百分比。例如，要找到一个城市中感染艾滋病毒的人口百分比：3月的时候有6,000例病例除以一个城市人口（一百万）乘以常数（K）得出的感染率为0.6％。计算感染率通常是被用于分析趋势，以达到控制感染和疾病的目的。<br /> <br /> An online infection rate calculator has been developed by the Centers for Disease Control and Prevention that allows the determination of the Streptococcal A infection rate in a population.&lt;ref&gt;{{cite web | title = Group A Streptococcus Calculator | publisher=Centers for Disease Control and Prevention |date= 4 July 2008 |url = https://www2a.cdc.gov/ncidod/dbmd/abcs/calc/calc_new/calculator.html | accessdate = 2017-01-09 }}&lt;/ref&gt;<br /> <br /> 疾病控制与预防中心已经开发了一个在线感染率计算器，可以确定人群中链球菌A的感染率。<br /> <br /> == 临床应用 Clinical applications ==<br /> <br /> Health care facilities routinely track their infection rates according to the guidelines issued by the Joint Commission.&lt;ref&gt;{{cite web | title= Basic Statistical Data Used in Acute Care Facilities | publisher = JB Learning | url = http://www.jblearning.com/samples/0763750344/45561_CH01.pdf | accessdate = 2017-01-09 }}&lt;/ref&gt; The healthcare-associated infection (HAI) rates measure infection of patients in a particular hospital. This allows rates to compared with other hospitals. These infections can often be prevented when healthcare facilities follow guidelines for safe care. To get payment from Medicare, hospitals are required to report data about some infections to the Centers for Disease Control and Prevention's (CDC's) National Healthcare Safety Network (NHSN). Hospitals currently submit information on central line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), surgical site infections (SSIs), MRSA Bacteremia, and C. difficile laboratory-identified events. The public reporting of these data is an effort by the Department of Health and Human Services.&lt;ref&gt;{{cite web | title = Healthcare-Associated Infections (HAI) data | publisher = Medicare.gov| url = https://www.medicare.gov/hospitalcompare/Data/Healthcare-Associated-Infections.html | accessdate = 2017-01-09 }}&lt;/ref&gt;<br /> <br /> 卫生保健机构根据联合委员会发布的准则，定期跟踪其感染率。医疗保健相关感染（HAI）率用于衡量特定医院中患者的感染情况。 这样可以将感染率与其他医院相比。 当医疗机构遵循安全护理指南时，通常可以预防这些感染。 为了从Medicare获得付款，医院必须向疾病控制与预防中心（CDC）的国家医疗安全网络（NHSN）报告有关某些感染的数据。 医院目前提交有关中心线相关的血液感染（CLABSI），导管相关的尿路感染（CAUTI），手术部位感染（SSI），MRSA细菌血症和艰难梭菌实验室鉴定事件的信息。 这些数据的公开报告是卫生与公共服务部的一项工作。<br /> <br /> For meaningful comparisons of infection rates, populations must be very similar between the two or more assessments. However, a problem with mean rates is that they cannot reflect differences in risk between populations.&lt;ref&gt;{{cite web | title= NHSN Standardized Infection Ratio |url = https://www.cdc.gov/nhsn/pdfs/ps-analysis-resources/nhsn-sir-guide.pdf | accessdate = 2017-01-09 | date= December 2016 }}&lt;/ref&gt;<br /> <br /> 为了有意义地比较感染率，两次或更多次评估之间的人群必须非常相似。 但是，平均感染率存在的问题是它们无法反映人群之间的风险差异。<br /> <br /> == 参考文献 References ==<br /> <br /> {{reflist}}<br /> <br /> == 外部链接 External links ==<br /> <br /> <br /> * [http://shea-online.org The Society for Healthcare Epidemiology of America] epidemiologists or physicians in infection control.<br /> * [http://www.apic.org Association for Professionals in Infection Control and Epidemiology] infection prevention and control professionals.<br /> * [http://www.cbic.org The Certification Board of Infection Control and Epidemiology, Inc.]</div>

Potatozh https://wiki.swarma.org/index.php?title=%E6%84%9F%E6%9F%93%E7%8E%87&diff=16480 2020-10-22T02:55:50Z

<p>Potatozh：</p> <hr /> <div>An infection rate (or incident rate) is the probability or risk of an infection in a population. It is used to measure the frequency of occurrence of new instances of infection within a population during a specific time period.<br /> <br /> 感染率（或发病率）是人群中感染的可能性或风险。 它用于衡量特定时间段内某个人群中新感染病例的发生频率。<br /> <br /> [[File:1.png|400px]]<br /> <br /> The number of infections equals the cases identified in the study or observed. An example might by HIV infection during a specific time period in the defined population. The population at risk are the cases appearing in the population during the same time period. An example would be all the people in a city during a specific time period. The constant, or K is assigned a value of 100 to represent a percentage. An example would be to find the percentage of people in a city who are infected with HIV: 6,000 cases in March divided by the population of a city (one million) multiplied by the constant (K) would give an infection rate of 0.6%. Calculating the infection rate is used to analyze trends for the purpose of infection and disease control.&lt;ref&gt;{{cite web | title = Calculation of Infection Rates|url = http://health.utah.gov/epi/diseases/HAI/resources/Cal_Inf_Rates.pdf |publisher = Utah Department of Health|date= 2017| accessdate = 2017-01-09 }}&lt;/ref&gt;<br /> <br /> 感染数量等于研究中确诊或观察到的病例。 一个例子可能是在特定时期内确诊人群中的HIV感染。处于风险中的人口数目是同一时期内人口数目。一个示例是特定时间段内城市中的所有人。常数K被赋值等于100，以表示百分比。例如，要找到一个城市中感染艾滋病毒的人口百分比：3月的时候有6,000例病例除以一个城市人口（一百万）乘以常数（K）得出的感染率为0.6％。计算感染率通常是被用于分析趋势，以达到控制感染和疾病的目的。<br /> <br /> An online infection rate calculator has been developed by the Centers for Disease Control and Prevention that allows the determination of the Streptococcal A infection rate in a population.&lt;ref&gt;{{cite web | title = Group A Streptococcus Calculator | publisher=Centers for Disease Control and Prevention |date= 4 July 2008 |url = https://www2a.cdc.gov/ncidod/dbmd/abcs/calc/calc_new/calculator.html | accessdate = 2017-01-09 }}&lt;/ref&gt;<br /> <br /> 疾病控制与预防中心已经开发了一个在线感染率计算器，可以确定人群中链球菌A的感染率。<br /> <br /> == 临床应用 Clinical applications ==<br /> <br /> Health care facilities routinely track their infection rates according to the guidelines issued by the Joint Commission.&lt;ref&gt;{{cite web | title= Basic Statistical Data Used in Acute Care Facilities | publisher = JB Learning | url = http://www.jblearning.com/samples/0763750344/45561_CH01.pdf | accessdate = 2017-01-09 }}&lt;/ref&gt; The healthcare-associated infection (HAI) rates measure infection of patients in a particular hospital. This allows rates to compared with other hospitals. These infections can often be prevented when healthcare facilities follow guidelines for safe care. To get payment from Medicare, hospitals are required to report data about some infections to the Centers for Disease Control and Prevention's (CDC's) National Healthcare Safety Network (NHSN). Hospitals currently submit information on central line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), surgical site infections (SSIs), MRSA Bacteremia, and C. difficile laboratory-identified events. The public reporting of these data is an effort by the Department of Health and Human Services.&lt;ref&gt;{{cite web | title = Healthcare-Associated Infections (HAI) data | publisher = Medicare.gov| url = https://www.medicare.gov/hospitalcompare/Data/Healthcare-Associated-Infections.html | accessdate = 2017-01-09 }}{{PD-notice}}&lt;/ref&gt;<br /> <br /> 卫生保健机构根据联合委员会发布的准则，定期跟踪其感染率。医疗保健相关感染（HAI）率用于衡量特定医院中患者的感染情况。 这样可以将感染率与其他医院相比。 当医疗机构遵循安全护理指南时，通常可以预防这些感染。 为了从Medicare获得付款，医院必须向疾病控制与预防中心（CDC）的国家医疗安全网络（NHSN）报告有关某些感染的数据。 医院目前提交有关中心线相关的血液感染（CLABSI），导管相关的尿路感染（CAUTI），手术部位感染（SSI），MRSA细菌血症和艰难梭菌实验室鉴定事件的信息。 这些数据的公开报告是卫生与公共服务部的一项工作。<br /> <br /> For meaningful comparisons of infection rates, populations must be very similar between the two or more assessments. However, a problem with mean rates is that they cannot reflect differences in risk between populations.&lt;ref&gt;{{cite web | title= NHSN Standardized Infection Ratio |url = https://www.cdc.gov/nhsn/pdfs/ps-analysis-resources/nhsn-sir-guide.pdf | accessdate = 2017-01-09 | date= December 2016 }}{{PD-notice}}&lt;/ref&gt;<br /> <br /> 为了有意义地比较感染率，两次或更多次评估之间的人群必须非常相似。 但是，平均感染率存在的问题是它们无法反映人群之间的风险差异。<br /> <br /> == 参考文献 References ==<br /> <br /> {{reflist}}<br /> <br /> == 外部链接 External links ==<br /> <br /> <br /> * [http://shea-online.org The Society for Healthcare Epidemiology of America] epidemiologists or physicians in infection control.<br /> * [http://www.apic.org Association for Professionals in Infection Control and Epidemiology] infection prevention and control professionals.<br /> * [http://www.cbic.org The Certification Board of Infection Control and Epidemiology, Inc.]</div>

Potatozh https://wiki.swarma.org/index.php?title=%E6%84%9F%E6%9F%93%E7%8E%87&diff=16478 2020-10-22T02:46:46Z

<p>Potatozh：</p> <hr /> <div>An infection rate (or incident rate) is the probability or risk of an infection in a population. It is used to measure the frequency of occurrence of new instances of infection within a population during a specific time period.<br /> <br /> 感染率（或发病率）是人群中感染的可能性或风险。 它用于衡量特定时间段内某个人群中新感染病例的发生频率。<br /> <br /> [[File:1.png|400px]]<br /> <br /> The number of infections equals the cases identified in the study or observed. An example might by HIV infection during a specific time period in the defined population. The population at risk are the cases appearing in the population during the same time period. An example would be all the people in a city during a specific time period. The constant, or K is assigned a value of 100 to represent a percentage. An example would be to find the percentage of people in a city who are infected with HIV: 6,000 cases in March divided by the population of a city (one million) multiplied by the constant (K) would give an infection rate of 0.6%. Calculating the infection rate is used to analyze trends for the purpose of infection and disease control.&lt;ref name=&quot;Calculation of Infection Rates&quot;&gt;&quot;Calculation of Infection Rates&quot; (PDF) [https://pattern.swarma.org/paper?id=b91add02-140f-11eb-bd25-0242ac1a0007 Calculation of Infection Rates]. Utah Department of Health. 2017. Retrieved 2017-01-09.&lt;/ref&gt;<br /> <br /> 感染数量等于研究中确诊或观察到的病例。 一个例子可能是在特定时期内确诊人群中的HIV感染。处于风险中的人口数目是同一时期内人口数目。一个示例是特定时间段内城市中的所有人。常数K被赋值等于100，以表示百分比。例如，要找到一个城市中感染艾滋病毒的人口百分比：3月的时候有6,000例病例除以一个城市人口（一百万）乘以常数（K）得出的感染率为0.6％。计算感染率通常是被用于分析趋势，以达到控制感染和疾病的目的。<br /> <br /> An online infection rate calculator has been developed by the Centers for Disease Control and Prevention that allows the determination of the Streptococcal A infection rate in a population.<br /> <br /> 疾病控制与预防中心已经开发了一个在线感染率计算器，可以确定人群中链球菌A的感染率。<br /> <br /> == 临床应用 Clinical applications ==<br /> <br /> Health care facilities routinely track their infection rates according to the guidelines issued by the Joint Commission.[3] The healthcare-associated infection (HAI) rates measure infection of patients in a particular hospital. This allows rates to compared with other hospitals. These infections can often be prevented when healthcare facilities follow guidelines for safe care. To get payment from Medicare, hospitals are required to report data about some infections to the Centers for Disease Control and Prevention's (CDC's) National Healthcare Safety Network (NHSN). Hospitals currently submit information on central line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), surgical site infections (SSIs), MRSA Bacteremia, and C. difficile laboratory-identified events. The public reporting of these data is an effort by the Department of Health and Human Services.[4]<br /> <br /> 卫生保健机构根据联合委员会发布的准则，定期跟踪其感染率。[3] 医疗保健相关感染（HAI）率用于衡量特定医院中患者的感染情况。 这样可以将感染率与其他医院相比。 当医疗机构遵循安全护理指南时，通常可以预防这些感染。 为了从Medicare获得付款，医院必须向疾病控制与预防中心（CDC）的国家医疗安全网络（NHSN）报告有关某些感染的数据。 医院目前提交有关中心线相关的血液感染（CLABSI），导管相关的尿路感染（CAUTI），手术部位感染（SSI），MRSA细菌血症和艰难梭菌实验室鉴定事件的信息。 这些数据的公开报告是卫生与公共服务部的一项工作。[4]<br /> <br /> For meaningful comparisons of infection rates, populations must be very similar between the two or more assessments. However, a problem with mean rates is that they cannot reflect differences in risk between populations.<br /> <br /> 为了有意义地比较感染率，两次或更多次评估之间的人群必须非常相似。 但是，平均感染率存在的问题是它们无法反映人群之间的风险差异。<br /> <br /> == 参考文献 References ==<br /> <br /> {{reflist}}<br /> <br /> == 外部链接 External links ==<br /> <br /> <br /> * [http://jsxgraph.uni-bayreuth.de/wiki/index.php/Epidemiology:_The_SIR_model SIR model: Online experiments with JSXGraph]<br /> <br /> *{{cite web |title=Simulating an epidemic |work=[[3Blue1Brown]] |date=March 27, 2020 |url=https://www.youtube.com/watch?v=gxAaO2rsdIs |via=[[YouTube]] }}</div>

Potatozh https://wiki.swarma.org/index.php?title=%E6%84%9F%E6%9F%93%E7%8E%87&diff=16434 2020-10-21T13:49:31Z

<p>Potatozh：</p> <hr /> <div>An infection rate (or incident rate) is the probability or risk of an infection in a population. It is used to measure the frequency of occurrence of new instances of infection within a population during a specific time period.<br /> <br /> 感染率（或发病率）是人群中感染的可能性或风险。 它用于衡量特定时间段内某个人群中新感染病例的发生频率。<br /> <br /> [[File:1.png|400px]]<br /> <br /> The number of infections equals the cases identified in the study or observed. An example might by HIV infection during a specific time period in the defined population. The population at risk are the cases appearing in the population during the same time period. An example would be all the people in a city during a specific time period. The constant, or K is assigned a value of 100 to represent a percentage. An example would be to find the percentage of people in a city who are infected with HIV: 6,000 cases in March divided by the population of a city (one million) multiplied by the constant (K) would give an infection rate of 0.6%. Calculating the infection rate is used to analyze trends for the purpose of infection and disease control.<br /> <br /> 感染数量等于研究中确诊或观察到的病例。 一个例子可能是在特定时期内确诊人群中的HIV感染。处于风险中的人口数目是同一时期内人口数目。一个示例是特定时间段内城市中的所有人。常数K被赋值等于100，以表示百分比。例如，要找到一个城市中感染艾滋病毒的人口百分比：3月的时候有6,000例病例除以一个城市人口（一百万）乘以常数（K）得出的感染率为0.6％。计算感染率通常是被用于分析趋势，以达到控制感染和疾病的目的。<br /> <br /> An online infection rate calculator has been developed by the Centers for Disease Control and Prevention that allows the determination of the Streptococcal A infection rate in a population.<br /> <br /> 疾病控制与预防中心已经开发了一个在线感染率计算器，可以确定人群中链球菌A的感染率。<br /> <br /> == 临床应用 Clinical applications ==<br /> <br /> Health care facilities routinely track their infection rates according to the guidelines issued by the Joint Commission.[3] The healthcare-associated infection (HAI) rates measure infection of patients in a particular hospital. This allows rates to compared with other hospitals. These infections can often be prevented when healthcare facilities follow guidelines for safe care. To get payment from Medicare, hospitals are required to report data about some infections to the Centers for Disease Control and Prevention's (CDC's) National Healthcare Safety Network (NHSN). Hospitals currently submit information on central line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), surgical site infections (SSIs), MRSA Bacteremia, and C. difficile laboratory-identified events. The public reporting of these data is an effort by the Department of Health and Human Services.[4]<br /> <br /> 卫生保健机构根据联合委员会发布的准则，定期跟踪其感染率。[3] 医疗保健相关感染（HAI）率用于衡量特定医院中患者的感染情况。 这样可以将感染率与其他医院相比。 当医疗机构遵循安全护理指南时，通常可以预防这些感染。 为了从Medicare获得付款，医院必须向疾病控制与预防中心（CDC）的国家医疗安全网络（NHSN）报告有关某些感染的数据。 医院目前提交有关中心线相关的血液感染（CLABSI），导管相关的尿路感染（CAUTI），手术部位感染（SSI），MRSA细菌血症和艰难梭菌实验室鉴定事件的信息。 这些数据的公开报告是卫生与公共服务部的一项工作。[4]<br /> <br /> For meaningful comparisons of infection rates, populations must be very similar between the two or more assessments. However, a problem with mean rates is that they cannot reflect differences in risk between populations.<br /> <br /> 为了有意义地比较感染率，两次或更多次评估之间的人群必须非常相似。 但是，平均感染率存在的问题是它们无法反映人群之间的风险差异。<br /> <br /> == 参考文献 References ==<br /> <br /> == 外部链接 External links ==</div>

Potatozh https://wiki.swarma.org/index.php?title=%E6%84%9F%E6%9F%93%E7%8E%87&diff=16429 2020-10-21T13:35:58Z

<p>Potatozh：</p> <hr /> <div>An infection rate (or incident rate) is the probability or risk of an infection in a population. It is used to measure the frequency of occurrence of new instances of infection within a population during a specific time period.<br /> <br /> 感染率（或发病率）是人群中感染的可能性或风险。 它用于衡量特定时间段内某个人群中新感染病例的发生频率。<br /> <br /> &lt;center&gt;[[File:1.png|400px]]&lt;\center&gt;<br /> <br /> The number of infections equals the cases identified in the study or observed. An example might by HIV infection during a specific time period in the defined population. The population at risk are the cases appearing in the population during the same time period. An example would be all the people in a city during a specific time period. The constant, or K is assigned a value of 100 to represent a percentage. An example would be to find the percentage of people in a city who are infected with HIV: 6,000 cases in March divided by the population of a city (one million) multiplied by the constant (K) would give an infection rate of 0.6%. Calculating the infection rate is used to analyze trends for the purpose of infection and disease control.<br /> <br /> 感染数量等于研究中确诊或观察到的病例。 一个例子可能是在特定时期内确诊人群中的HIV感染。处于风险中的人口数目是同一时期内人口数目。一个示例是特定时间段内城市中的所有人。常数K被赋值等于100，以表示百分比。例如，要找到一个城市中感染艾滋病毒的人口百分比：3月的时候有6,000例病例除以一个城市人口（一百万）乘以常数（K）得出的感染率为0.6％。计算感染率通常是被用于分析趋势，以达到控制感染和疾病的目的。</div>

Potatozh https://wiki.swarma.org/index.php?title=%E6%84%9F%E6%9F%93%E7%8E%87&diff=16422 2020-10-21T13:27:57Z

<p>Potatozh：</p> <hr /> <div>An infection rate (or incident rate) is the probability or risk of an infection in a population. It is used to measure the frequency of occurrence of new instances of infection within a population during a specific time period.<br /> <br /> 感染率（或发病率）是人群中感染的可能性或风险。 它用于衡量特定时间段内某个人群中新感染病例的发生频率。<br /> <br /> [[File:1.png|400px]]</div>

Potatozh https://wiki.swarma.org/index.php?title=%E6%96%87%E4%BB%B6:1.png&diff=16421 2020-10-21T13:27:43Z

<p>Potatozh：Potatozh上传文件:1.png的新版本</p> <hr /> <div>基于MsUpload的文件上传</div>

Potatozh https://wiki.swarma.org/index.php?title=%E6%84%9F%E6%9F%93%E7%8E%87&diff=16418 2020-10-21T13:24:29Z

<p>Potatozh：</p> <hr /> <div>An infection rate (or incident rate) is the probability or risk of an infection in a population. It is used to measure the frequency of occurrence of new instances of infection within a population during a specific time period.<br /> <br /> 感染率（或发病率）是人群中感染的可能性或风险。 它用于衡量特定时间段内某个人群中新感染病例的发生频率。<br /> <br /> [[文件:9e2369781bb7add691a5ff9397d1dc57f8b4bf5e.svg|居中|infect_rate]]</div>

Potatozh https://wiki.swarma.org/index.php?title=%E6%96%87%E4%BB%B6:9e2369781bb7add691a5ff9397d1dc57f8b4bf5e.svg&diff=16416 2020-10-21T13:23:58Z

<p>Potatozh：</p> <hr /> <div>感染率公式</div>

Potatozh https://wiki.swarma.org/index.php?title=%E6%84%9F%E6%9F%93%E7%8E%87&diff=16415 2020-10-21T13:21:05Z

<p>Potatozh：创建页面，内容为“An infection rate (or incident rate) is the probability or risk of an infection in a population. It is used to measure the frequency of occurrence of new instances o…”</p> <hr /> <div>An infection rate (or incident rate) is the probability or risk of an infection in a population. It is used to measure the frequency of occurrence of new instances of infection within a population during a specific time period.<br /> 感染率（或发病率）是人群中感染的可能性或风险。 它用于衡量特定时间段内某个人群中新感染病例的发生频率。</div>

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