非线性组合动态传播率模型与我国COVID-19疫情分析和预测

针对传统的流行性传染病学中基本传染数R0难以准确估计以及单一模型预测精度低的缺陷,利用组合动态传播率替换基本传染数R0,提出基于支持向量回归的非线性时变传播率模型并对我国COVID-19疫情进行分析和预测.首先,计算动态传播率的离散值;其次,使用多项式函数、指数函数、双曲函数和幂函数分别对动态传播率的离散值进行拟合并基...     

Regional Prediction of COVID-19 in the United States Based on the Difference Equation Model

The novel coronavirus pneumonia 2019 (COVID-19) has swept the globe in just a few months with negative social and psychological consequences for public health. So far, the United States has been one of the countries most affected by the epidemic. In this study, 51 states in the United States are divided into 10 state clusters according to relevant factors, and a difference equation model with spatio-temporal dynamic characteristics is established to predict the transmission dynamics of COVID-19 in the 10 state clusters and obtain data on regional aggregation levels (the United States). The study showed that the Pearson Correlation Coefficient between the actual data and the predicted data in the 10 state clusters is between 0.6 and 0.96 (mean R$^{2}$=0.8448), and the mean absolute error (MAE) of the newly confirmed cases in each cluster is between 300 and 1650 (mean MAE=878) and the average forecasting error rate (AFER) of the total confirmed cases in each cluster is between 0.9$\%$ and 3$\%$ (mean AFER=1.57$\%$). These results show that the difference equation model can well predict the changes in the recent confirmed cases of infectious diseases such as COVID-19.     

SIR类型新型冠状病毒肺炎多阶段最优控制模型

新型冠状病毒肺炎(COVID-19)疫情在全球范围传播,给人们的健康带来了严重的威胁。面对疫情发展预期数据,我们需要在有限医疗资源的情况下确定疫情传播参数,以指导主要防疫措施的实施力度。本文采用SIR类型的模型描述新冠肺炎疫情发展,并建立多阶段最优控制模型确定疫情传播参数。为了高效确定参数取值,我们建立多项式时间可计算的半定规划近似模型。基于世界卫生组织发布的数据,我们求解近似模型,得到描述给定时段内美国新冠肺炎疫情发展态势的疫情传播参数,并分析疫情防控策略。     

基于EMD-GRU的高速公路行程时间组合预测模型

考虑到高速公路行程时间影响因素繁多且行程时间序列非线性、非平稳特征显著,设计了基于经验模态分解和GRU神经网络的高速公路行程时间组合预测模型.首先,利用高速公路收费数据中车辆进出高速公路的时间信息获取路段行程时间序列;然后,利用经验模态分解算法,将复杂的行程时间序列分解为若干时间尺度不同、相对平稳的本征模态函数分量和残差分量;接着,使用GRU神经网络对各本征模态函数分量和残差分量进行预测与集成操作.实例分析表明:经验模态分解可有效提高LSTM、GRU神经网络的预测精度;在相同参数设置的情况下,GRU神经网络的预测精度优于LSTM神经网络.     

一类潜伏期有传染性的传染病模型动力学分析

建立了一类潜伏期具备传染性的传染病传播模型,根据疾病传播规律求解了疾病消失和持续生存的阈值——基本再生数.对系统的稳定性进行了讨论,得到了系统稳定性条件.最后,以COVID-19为例,解释了各种举措在疾病控制中的作用,并对疫情传播扩散做了探讨和预测.     

一类具有接种和时滞的非自治捕食-食饵的SIR传染病模型(英文)

A non-autonomous SIR epidemic model of prey-predator with vaccination and time delay is investigated in this paper. And an infectious disease is only considered to spread among the prey population. By using comparison principle and Lyapunov functional methods, we obtain the sufficient criteria for the permanence, extinction of infectious disease and the global attractively of the model. Furthermore, some numerical simulations illustrate that the vaccination has a better effect for disease controlling of infective prey.     

Pulse and constant control schemes for epidemic models with seasonality

Control schemes for infectious disease models with time-varying contact rate are analyzed. First, time-constant control schemes are introduced and studied. Specifically, a constant treatment scheme for the infected is applied to a SIR model with time-varying contact rate, which is modelled by a switching parameter. Two variations of this model are considered: one with waning immunity and one with progressive immunity. Easily verifiable conditions on the basic reproduction number of the infectious disease are established which ensure disease eradication under these constant control strategies. Pulse control schemes for epidemic models with time-varying contact rates are also studied in detail. Both pulse vaccination and pulse treatment models are applied to a SIR model with time-varying contact rate. Further, a vaccine failure model as well as a model with a reduced infective class are considered with pulse control schemes. Again, easily verifiable conditions on the basic reproduction number are developed which guarantee disease eradication. Some simulations are given to illustrate the threshold theorems developed.     

社会防控与信息公开：重大传染病疫情的演化机理与预测模型

世界各国对新冠肺炎疫情的抗疫模式产生了严重的分歧。本文根据中国新冠肺炎疫情防控的体制动员成功经验,将社会公共卫生防控措施的博弈因素与传染病模型相结合,构建了重大传染病疫情演化机理与情境预测的演化博弈模型。通过将传染病传播模型中感染系数参数加以内生化,解释了社会动员体制在疫情初期防控中的关键作用。最后使用感染系数内生化的SI模型分别对美国、意大利和中国三种抗疫模式进行Logistic方程拟合和峰值点分析,并将结果进行比对。本文研究表明,在缺少有效疫苗的情况下,采取隔离和政府疫情信息公开的中国疫情防控模式,在新型重大传染病疫情防控过程中发挥着关键性作用。     

基于Kolmogorov前向方程评估甲型H1N1流感疫情的动态变化北大核心CSCD

基于个体水平的传染病模型可以揭示随机性在传染病疫情防控中的重要作用.研究此类模型的普遍方法是通过事件驱动的、大量重复的随机模拟来确定预测变量的范围.而基于Kolmogorov前向方程(KFE)研究个体水平的传染病模型,不仅不需要大量的重复模拟来确定预测变量的范围,而且可以同时考虑每种状态发生的概率.因此,基于2009年西安市第八医院甲型H1N1流感数据,建立了基于社交网络的个体决策心理模型,以确定行为改变率;进一步地,为得到传染病传播过程中各状态的概率分布,基于改进的个体SIR模型,通过Markov过程推导出KFE.结果表明:通过数值求解KFE可以得到整个爆发过程中每种状态发生的概率分布、最严重的时间段及相应的概率,从而能更快、更准确地了解甲型H1N1疫情的传播过程,因此有助于高效地进行甲型H1N1疫情防控.     

一类具有阶段结构和接种的SIR传染病模型

根据不同程度的感染者有不同的传染率,建立了一个具有阶段结构和双线性传染率的S IR流行病模型,得到了模型的阈值参数R0,证明了模型平衡点的全局性态完全由R0的值确定.并进行了数值模拟.     

Analysis of two discrete forms of the classic continuous SIR epidemiological model

ABSTRACT

From continuous standard SIR model, which is configured from two sequenced flows (a) susceptible – infectious and (b) infectious – removed, we obtain two impulsive SIR models assuming different time scales for (a) respect to (b) (one more quickly than the other and inversely). By associating respective stroboscopic maps to this impulsive systems, two discretizations are defined. The dynamics of these maps are analysed in order to get thresholds conditions for predicting (or to control) epidemic outbreaks. As it is traditional for SIR systems, we also find conditions for the final size of the susceptible group.     

一类带有一般接触率和常数输入的流行病模型的全局分析

借助极限系统理论和构造适当的Liapunov函数,对带有一般接触率和常数输入的SIR型和SIRS型传染病模型进行讨论．当无染病者输入时,地方病平衡点存在的阈值被找到A·D2对相应的SIR模型,关于无病平衡点和地方病平衡点的全局渐近稳定性均得到充要条件;对相应的SIRS模型,得到无病平衡点和地方病平衡点全局渐近稳定的充分条件．当有染病者输入时,模型不存在无病平衡点．对相应的SIR模型,地方病平衡点是全局渐近稳定的;对相应的SIRS模型,得到地方病平衡点全局渐近稳定的充分条件．     

SIR epidemics with stochastic infectious periods

We consider an SIR model for the spread of an epidemic in a closed and homogeneously mixing population, where the infectious periods are represented by an arbitrary absorbing Markov process. A version of this process starts whenever an infection occurs, and the contamination rate of the newly infected individual is a function of its state. When his process is absorbed, the individual becomes a removed case. We use a martingale approach to derive the distribution of the final epidemic size and severity for this class of models. Next, we examine some special cases. In particular, we focus on situations where the infection processes are Brownian motions and where they are Markov-modulated fluid flows. In the latter case, we use matrix-analytic methods to provide more explicit results. We conclude with some numerical illustrations.     

加权无标度网络中的疾病传播

提出具有加权传播率和非线性传染能力的SIR模型和SIS模型,通过平均场方法证明了这两个模型在加权无标度网络中可以存在非零的传播阈值,从而传播率需要跨越更大的传播阈值才能流行.并且得到的结果在特殊情况下可退化为已有的一些经典结论.     

基于神经网络的股票预测模型

针对股票市场的特征提取困难、预测精度较低等问题,本文基于深度学习算法,构建了一系列用于股票市场预测的神经网络模型,包括基于多层感知机(MLP)、卷积神经网络(CNN)、递归神经网络(RNN)、长短期记忆网络(LSTM)和门控神经单元(GRU)的模型。 针对RNN、LSTM和GRU无法充分利用所参考的时间维度的信息,引入注意力机制(Attention Mechanism) 给各时间维度的信息赋予不同权重,区分不同信息对预测的重要程度,从而提升递归网络模型的性能。上述模型均基于股票数据进行了优化,基于上证指数对各类模型进行了充分的对比实验,探索了模型中重要变量对性能的影响,旨在为基于神经网络的股票预测模型给出具体的优化方向。     

封闭空间中新型冠状病毒肺炎传播模型:以日本“钻石公主号”邮轮为例

该文以新型冠状病毒(SARS-Cov-2)在日本钻石公主号邮轮上传播为例,通过建立简单的易感者-感染者传染病模型,研究在封闭空间中新冠病毒肺炎(COVID-19)的传播机制.动力学分析和数值拟合预测了疾病传播过程和最终结果,讨论了不同隔离措施对疾病传播进程的影响,并给出防控策略建议.     

Estimation of the number of failures in the Weibull model using the ordinary differential equation

In estimating the number of failures using right truncated grouped data, we often encounter cases that the estimate is smaller than the true one when we use the likelihood principle to conditional probability. In infectious disease spread predictions, the SIR model described by simultaneous ordinary differential equations is commonly used, and it can predict reasonably well the number of infected patients even when the size of observed data is small. We have investigated whether the ordinary differential equation model can estimate the number of failures more accurately than does the likelihood principle under the condition of right truncated grouped data. The positive results are obtained in the Weibull model, similarly to the cases of the SARS, A(H1N1), and FMD.     

Global properties of a two-scale network stochastic delayed human epidemic dynamic model

Complex population structure and the large-scale inter-patch connection human transportation underlie the recent rapid spread of infectious diseases of humans. Furthermore, the fluctuations in the endemicity of the diseases within patch dwelling populations are closely related with the hereditary features of the infectious agent. We present an SIR delayed stochastic dynamic epidemic process in a two-scale dynamic structured population. The disease confers temporary natural or infection-acquired immunity to recovered individuals. The time delay accounts for the time-lag during which naturally immune individuals become susceptible. We investigate the stochastic asymptotic stability of the disease free equilibrium of the scale structured mobile population, under environmental fluctuations and the impact on the emergence, propagation and resurgence of the disease. The presented results are demonstrated by numerical simulation results.     

Mathematical model for the novel coronavirus (2019-nCOV) with clinical data using fractional operator

Coronavirus infection (COVID-19) is a considerably dangerous disease with a high demise rate around the world. There is no known vaccination or medicine until our time because the unknown aspects of the virus are more significant than our theoretical and experimental knowledge. One of the most effective strategies for comprehending and controlling the spread of this epidemic is to model it using a powerful mathematical model. However, mathematical modeling with a fractional operator can provide explanations for the disease's possibility and severity. Accordingly, basic information will be provided to identify the kind of measure and intrusion that will be required to control the disease's progress. In this study, we propose using a fractional-order SEIARPQ model with the Caputo sense to model the coronavirus (COVID-19) pandemic, which has never been done before in the literature. The stability analysis, existence, uniqueness theorems, and numerical solutions of such a model are displayed. All results were numerically simulated using MATLAB programming. The current study supports the applicability and influence of fractional operators on real-world problems.