基于时滞动力学模型对钻石公主号邮轮疫情的分析

2019年末以来,新型冠状病毒肺炎迅速蔓延的疫情引发了全球关注.文献[5-6]提出了一类时滞动力学系统的新冠肺炎传播模型用以描述疫情的发展趋势.文献[7]在此基础上,结合CCDC统计数据,提出了一类基于CCDC统计数据的随机时滞动力学模型.本文将使用以上两类模型研究分析"钻石公主号"邮轮的疫情发展.基于日本厚生劳动省公布的数据,本文准确反演出模型参数,进而有效模拟当前疫情的发展,并预测疫情未来的趋势,发现在疫情爆发初期基本再生数R0(t)较大,而后随着防控措施加强而逐渐减小;约在2月下旬,累计确诊人数增长速度放缓,在3月上旬,累计确诊人数趋于稳定,即无新增确诊人数,疫情得到有效控制;最终累计确诊人数对隔离率变化敏感,隔离率升高,最终累计确诊人数将有显著下降.针对传染率较高、隔离率较低的问题,本文建议日本政府进一步加强防控措施,抑制疫情的大规模爆发.     

基于一类时滞动力学系统对新型冠状病毒肺炎疫情的建模和预测

2019年12月,新型冠状病毒肺炎(novel coronavirus pneumonia, NCP)疫情从武汉开始暴发,几天内迅速传播到全国乃至海外.科学有效地掌控疫情发展对疫情防控至关重要.本文基于全国各级卫生健康委员会每日公布的累计确诊数和治愈数,提出一类基于时滞动力学系统的传染病动力学模型.在模型中引入时滞过程,用来描述病毒潜伏期和治疗周期.通过公布的疫情数据,首先准确反演模型的参数;其次有效地模拟目前疫情的发展,并预测疫情未来的趋势;最后分析各级政府防控措施手段的有效程度,并发现在现有的高效防控措施下,疫情将在近期好转.     

互联网双时滞TCP-RED模型Hopf分岔和Fold分岔的数值分析

利用时滞动力学分析软件DDE-BIFTOOL研究了时滞互联网TCP-RED拥塞控制模型的动力学.传输时滞τ_1可以使得该系统发生Hopf分岔、Fold分岔,使得平均队列长度和到达速率出现近似恒速运动或周期波动,揭示了时滞对其动力学的重要影响.     

一类具时滞和脉冲接种的SEIR疾病传播模型

首先针对具时滞和脉冲接种的SEIR传染病模型,分析了模型无病周期解的全局吸引性.然后,基于2013年宁夏流行性腮腺炎的疫情数据,估计脉冲接种周期,并对系统进行了数值模拟,模拟结果与理论结果一致.     

一类时滞病毒动力学模型的稳定性分析

建立并分析了一类带有两个时滞的病毒动力学模型.通过讨论,获得了有时滞情况下无病平衡点以及正平衡点的稳定性态.     

随机时滞教学系统的最优习得策略

认识非线性随机时滞现象的内在机理、运演性态和掌握其内在规律己成为当今非线性随机动力学理论分析与数值分析的重要主题.基于教学论、自组织理论和非线性随机时滞动力学等理论及其有关研究成果,构建教学系统的非线性随机时滞Logistic模型,寻求教学系统的演化规律,并在此基础上研究教学系统的最优习得策略和提出相应教学建议,期望能促进教学系统有序发展;同时,也期望能促使教学论与非线性随机动力学等理论"联姻",拓展与丰富教学论的研究领域,为教学论研究提供一个新的学科视角的支持与论证.     

考虑媒体播报效应的双时滞传染病模型

在疾病控制过程中, 媒体的重要性举足轻重.该文旨在建立并分析一个含有媒体效应的多时滞传染病模型, 研究模型的稳定性, 并通过分析相应特征方程根, 分别研究在时滞不同的5种情况下, 系统的稳定性发生变化, 以及产生Hopf分支的条件.再利用持续性理论, 证明模型的持续生存性.最后将时滞模型研究结果应用于苏格兰小儿肺炎中, 验证媒体效应对疫情控制起到的重要作用以及时滞大小对模型稳定性的影响.     

一类带全局反应项SIR传染病模型的异宿轨和行波解（英文）

本文研究一类SIR类型传染病模型的正异宿轨的存在性问题,该类模型通常被视为带全局反应项和非单调型的时滞微分方程组.利用Fraia和黄等发展的Freedholm算子分解及非线性扰动理论,我们研究反应扩散系统的行波解和对应时滞微分方程异宿轨解之间的关联性,并据此证明该系统行波解的存在性和动力学性质.     

一类具有垂直传染与脉冲免疫的SEIR传染病模型的全局分析

考虑了一个具有垂直传染与积分时滞的SEIR传染病动力学模型.分析了该模型在脉冲免疫接种条件下的动力学行为,获得了传染病灭绝的充分条件,进而运用脉冲时滞泛函微分方程理论,获得了含有时滞的系统持久性的充分条件,并且证明了积分时滞与脉冲免疫能对模型的动力学行为产生显著的影响.     

具有分布时滞的病毒感染模型动力学性质研究

本文研究具有分布时滞的病毒感染模型的动力学性质.构建该模型,基于Lyapunov泛函分析方法,研究该系统平衡点的全局稳定性.分别得到该系统无病平衡点和感染平衡点全局稳定的充分条件.     

Dynamic models for Coronavirus Disease 2019 and data analysis

In this letter, two time delay dynamic models, a Time Delay Dynamical–Novel Coronavirus Pneumonia (TDD-NCP) model and Fudan-Chinese Center for Disease Control and Prevention (CCDC) model, are introduced to track the data of Coronavirus Disease 2019 (COVID-19). The TDD-NCP model was developed recently by Chengąŕs group in Fudan and Shanghai University of Finance and Economics (SUFE). The TDD-NCP model introduced the time delay process into the differential equations to describe the latent period of the epidemic. The Fudan-CDCC model was established when Wenbin Chen suggested to determine the kernel functions in the TDD-NCP model by the public data from CDCC. By the public data of the cumulative confirmed cases in different regions in China and different countries, these models can clearly illustrate that the containment of the epidemic highly depends on early and effective isolations.     

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$R_0$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$R_0$,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于最佳滑窗期$k=3$构建相应的预测模型;接着,基于拟合优度等评价指标选择最佳的三种单一模型并对其预测结果进行非线性组合;最后,利用非线性组合动态传播率模型对湖北、全国除湖北和全国COVID-19疫情进行分析和预测。实证结果表明提出的非线性组合动态传播率模型对不同地区COVID-19疫情数据的预测误差均相对较小;对重点省市COVID-19疫情的拐点预测切实合理;湖北、全国除湖北与全国自2020年2月27日起后20天疫情预测曲线的拟合优度分别为98.53%、98.06%和97.98%。     

动态传播率模型及其在疫情分析中的应用

应用数据驱动的动态传播率来代替基本传染数$R_0$,在全国和省市两个层面上研究COVID-19疫情发展的特点和趋势。首先,基于动态增长率建立传染病常微分方程,推导得出动态传播率模型。其次,选择幂函数作为动态传播率的拟合函数,以3天作为最优滑窗期,对各地拐点进行了估计。最后,通过动态模型对各地不同程度尾声开始的起点进行了预测,并在13个省市间进行9个疫情相关指标的对比分析。结果显示,各地动态传播率在经过短暂的波动后均稳步下降,疫情得到有效控制;估计的拐点主要集中在2月中旬,而预测的尾声都将在3月底之前到来;同时,各地疫情发展特点和趋势、防控措施力度和效果存在一定差异。     

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.     

A practical exercise in simulation model validation

The validation of continuous system simulation models is a matter of great practical importance. Unfortunately, however, it often receives very little attention in university-level courses on modelling and simulation. The paper considers some possible reasons for the lack of emphasis given to model validation issues in education and describes an exercise which has been designed to introduce students to some practical aspects of internal verification and external validation of nonlinear dynamic models. The work involves a laboratory-scale system based on two inter-connected tanks of liquid and also demonstrates some important limitations of a widely-used nonlinear model. Features which make the chosen system particularly suitable for this teaching application include the relatively simple physical nature of the system and the fact that all the key variables of the model are accessible for measurement. Students are exposed to a range of practical issues, such as the selection of the sampling rate for data collection and the design of experiments to provide data sets appropriate for external validation purposes. Dealing successfully with such questions and carrying out validation tests exposes students to concepts of model credibility and the whole process of model development and application. They are also challenged in terms of their practical abilities to use computer simulation techniques and in their understanding of the mathematical model and the physics of the real system. It is argued that an exercise of this type can have very important educational benefits.     

COVID-19 Epidemic Prediction Based on Deep Learning

In this paper, a multi-layer gated recurrent unit neural network (multi-head GRU) model is proposed to predict the confirmed cases of the new crown epidemic (COVID-19). We extract the time series relationship in the data, and the rolling prediction method is adopted to ensure the simple structure of the model and achieve higher precision and interpretability. The prediction results of this model are compared with the LSTM model, the Transformer model and the infectious disease model (SIR). The results show that the proposed model has higher prediction accuracy. The mean absolute error (MAE) of epidemic prediction in most countries (the United States, Brazil, India, the United Kingdom and Russia) is respectively 197.52, 68.02, 200.67, 24.78 and 123.50, which is much smaller than the prediction error of the SIR model, LSTM model and Transformer model. For the spread of the COVID-19 epidemic, traditional infectious disease models and machine learning models cannot achieve more accurate predictions. In this paper, we use a GRU model to predict the real-time spread of COVID-19, which has fewer parameters and reduces the risk of overfitting to train faster. Meanwhile, it can make up for the shortcoming of the transformer model to capture local features.     

Stochastic modeling of carcinogenesis by state space models: a new approach

In this paper, we have developed some state space models for carcinogenesis involving multievent models and multiple pathways models. In these state space models, the stochastic system models are stochastic models of carcinogenesis expressed in terms of stochastic differential equations, whereas the observation models are statistical models based on the observed number of detectable preneoplastic lesions per individual over time and the observed number of detectable cancer tumors per individual over time. In this paper, we have applied some of the theories to some animal papillomas data from some initiation-promotion experiments on skin cancer in mice to estimate some unknown parameters. For this data set we have obtained excellent fit by a model with three piece-wise intervals.     

MODELING FOREST PRODUCTIVITY ACROSS LARGE AREAS AND LONG PERIODS

ABSTRACT. There are three classes of forest model used to simulate forest productivity across large areas and over long periods: growth and yield models, based on statistical relationships derived from measurements on trees; the so-called gap models, concerned with species succession and dynamics, and carbon balance or biomass models. The characteristics of each type are discussed and illustrated by reference to some of the more important of the models in current use. The emphasis in this paper is on the carbon balance models, particularly on a new model (3-PG), developed in a deliberate attempt to bridge the gap between growth and yield and carbon balance models, and the companion model (3-PGS) derived from 3-PG to utilize satellite data as inputs to constrain the simulation calculations and improve estimates of growth over time. 3-PG/3-PGS run on monthly time steps, driven by weather data, and avoid the problems of over-parameterization and the requirements for a great deal of input data that limit the practical value of most carbon balance models. We present test results from 3-PG against experimental data, and against forest plot (mensuration) data from large areas; also test results from 3-PGS against estimates of average forest growth over large areas, and in plantations with different planting times, using AVHRR and Landsat MSS data to constrain the model outputs. The paper discusses the problems of the variability of natural forests and the difficulties this causes in validating models intended for use over large areas. The value of remote sensing as means of overcoming this problem is considered.     

A dynamic wind farm aggregate model for the simulation of power fluctuations due to wind turbulence

An important aspect related to wind energy integration into the electrical power system is the fluctuation of the generated power due to the stochastic variations of the wind speed across the area where wind turbines are installed. Simulation models are useful tools to evaluate the impact of the wind power on the power system stability and on the power quality. Aggregate models reduce the simulation time required by detailed dynamic models of multiturbine systems.In this paper, a new behavioral model representing the aggregate contribution of several variable-speed-pitch-controlled wind turbines is introduced. It is particularly suitable for the simulation of short term power fluctuations due to wind turbulence, where steady-state models are not applicable.The model relies on the output rescaling of a single turbine dynamic model. The single turbine output is divided into its steady state and dynamic components, which are then multiplied by different scaling factors. The smoothing effect due to wind incoherence at different locations inside a wind farm is taken into account by filtering the steady state power curve by means of a Gaussian filter as well as applying a proper damping on the dynamic part.The model has been developed to be one of the building-blocks of a model of a large electrical system, therefore a significant reduction of simulation time has been pursued. Comparison against a full model obtained by repeating a detailed single turbine model, shows that a proper trade-off between accuracy and computational speed has been achieved.     

The Verification of Dynamic Simulation Models

This paper is concerned with the use of ‘loop analysis’ in the evaluation and validation of dynamic simulation models. The part that loop analysis plays in the overall validation process is described, and it is shown, by reference to the analysis of a complex model, how the model's behaviour may be investigated. The analysis proceeds by identifying some of the elements of system behaviour such as damping, phase-shifts and gain/delay factors, and, in the process, highlights those feedback loops that are pseudo-positive and/or unconformable. In addition to the validation aspect of the use of loop analysis, there is a valuable contribution towards the knowledge and insight of the model-builder, enabling him better to identify areas of the system which may benefit from re-design.