解析一类SIS和SIR传染病模型的稳定性

借助微分方程建立传染病SIS模型和SIR模型,进一步研究了一类SIS和SIR传染病模型,得出了决定SIS传染病是否发生的阈值;解析了SIR模型无病平衡点和地方平衡点的稳定性.     

几个具有隔离项的传染病模型的局部稳定性和全局稳定性

首先建立了一类具常恢复率,有效接触率依赖于总人数的SIQS传染病模型,并得到了阈值参数σ的表达式.如果σ≤1,则疾病消除平衡点全局稳定;如果σ>1,则存在唯一的传染病平衡点且是局部渐近稳定的。对于带有双线性传染率和标准传染率的两个相应模型,我们进一步证明了当σ>1时传染病平衡点的全局稳定性。其次对于带隔离项修正的传染率的相应模型,我们同样证明了传染病平衡点只要存在唯一就一定全局稳定的结论。上述结果均推广和改进了Hethcote et al.(2002)的相应工作。     

一类具有非单调感染率的时滞传染病模型的全局稳定性(英文)

通过构造Lyapunov函数研究了一类具有非单调感染率的时滞传染病模型,并证明了该模型的无病平衡点和地方性平衡点的全局稳定性.     

时滞传染病模型的指数稳定性

利用分析技巧研究了一类SEIRS传染病模型的动力学行为.结论表明如果再生数小于1,则带变时滞的传染病模型的无病平衡点是全局指数渐近稳定的,如果再生数大于1,得到传染病平衡点局部指数稳定的充分条件,同时给出了例子说明结论的有效性.     

一类具有阶段结构和隔离的种群-传染病模型

该文讨论了一类具有阶段结构和隔离的种群-传染病模型.在该模型中,假设染病者没有生育能力.通过分析讨论,得到了地方病平衡点存在的阈值条件,以及无病平衡点和地方病平衡点局部渐近稳定和全局渐近稳定的充分条件.     

一类具有常数迁入且总入口在变化的SIRI传染病模型的稳定性

讨论一类具有常数迁入率,染病类有病死且有效接触率依赖于总人数的SIRI传染病模型.给出了基本再生数σ的表达式.如果σ≤1,则疾病消除平衡点是全局稳定的;如果σ＞1,则存在唯一的传染病平衡点且是局部渐近稳定的.对具有双线性传染率和标准传染率的相应模型,进一步证明了当σ＞1时传染病平衡点的全局稳定性.     

一类具有隔离干预的非线性传染率的传染病模型的全局稳定性分析(英文)

研究一类具有隔离干预的非线性传染率的SIQR传染病模型的全局稳定性,得到了阈值R及无病平衡点和地方病平衡点的存在的条件,并利用构造李雅普诺夫函数证明无病平衡点和地方病平衡点的全局稳定性.     

一类带有隔离和分布时滞的SIQRS的传染病模型的稳定性分析

提出一类含有分布时滞和隔离的传染病模型,利用构造李亚普诺夫泛函的方法,得到无病平衡点和地方平衡点全局渐近稳定性的结论,并讨论了永久免疫时,系统无病平衡点的指数稳定性.     

一类具有标准发生率的SIS型传染病模型的全局稳定性

研究一类具有标准发生率的SIS传染病模型,讨论了各类平衡点存在的条件;运用微分方程的定性理论,得到了无病平衡点E_1和地方病平衡点E_2的全局渐近稳定的充分条件.     

一类具有隔离干预和可分离广义传染率的SIQRS传染病模型的全局稳定性分析(英文)

研究一类具有隔离干预和可分离广义传染率的SIQRS传染病模型的全局稳定性,得到了阈值R及无病平衡点和地方病平衡点的存在的条件,并利用构造李雅普诺夫函数证明无病平衡点和地方病平衡点的全局稳定性.     

Continuum secondary structure captures protein flexibility

The DSSP program assigns protein secondary structure to one of eight states. This discrete assignment cannot describe the continuum of thermal fluctuations. Hence, a continuous assignment is proposed. Technically, the continuum results from averaging over ten discrete DSSP assignments with different hydrogen bond thresholds. The final continuous assignment for a single NMR model successfully reflected the structural variations observed between all NMR models in the ensemble. The structural variations between NMR models were verified to correlate with thermal motion; these variations were captured by the continuous assignments. Because the continuous assignment reproduces the structural variation between many NMR models from one single model, functionally important variation can be extracted from a single X-ray structure. Thus, continuous assignments of secondary structure may affect future protein structure analysis, comparison, and prediction.     

On the Relationship of Steady States of Continuous and Discrete Models Arising from Biology

For many biological systems that have been modeled using continuous and discrete models, it has been shown that such models have similar dynamical properties. In this paper, we prove that this happens in more general cases. We show that under some conditions there is a bijection between the steady states of continuous and discrete models arising from biological systems. Our results also provide a novel method to analyze certain classes of nonlinear models using discrete mathematics.     

Parametric and Parametrically Smoothed Distribution-Free Proportional Hazard Models with Discrete Data

This paper discusses discrete time proportional hazard models and suggests a new class of flexible hazard functions. Explicitly modeling the discreteness of data is important since standard continuous models are biased; allowing for flexibility in the hazard estimation is desirable since strong parametric restrictions are likely to be similarly misleading. Simulation compare continuous and discrete models when data are generated by grouping and demonstrate that simple approximations recover underlying hazards well and outperform nonparametric maximum likelihood estimates in term of mean squared error.     

Current flow and potential in a three-dimensional syncytium

Analytical solutions are obtained describing the spread of potential in a continuous syncytium which is the three-dimensional analogue of the “cable” and “thin sheet” models. Two discrete (finite-element) models are also developed and their behaviour shown to agree with that of the continuous model. All three models are able to account for the peculiarities of passive responses to intracellular current injection in tissues such as cardiac and smooth muscle. The time course and spread of junction potentials in electrically coupled tissues is discussed.     

Accurate Path Integration in Continuous Attractor Network Models of Grid Cells

Grid cells in the rat entorhinal cortex display strikingly regular firing responses to the animal''s position in 2-D space and have been hypothesized to form the neural substrate for dead-reckoning. However, errors accumulate rapidly when velocity inputs are integrated in existing models of grid cell activity. To produce grid-cell-like responses, these models would require frequent resets triggered by external sensory cues. Such inadequacies, shared by various models, cast doubt on the dead-reckoning potential of the grid cell system. Here we focus on the question of accurate path integration, specifically in continuous attractor models of grid cell activity. We show, in contrast to previous models, that continuous attractor models can generate regular triangular grid responses, based on inputs that encode only the rat''s velocity and heading direction. We consider the role of the network boundary in the integration performance of the network and show that both periodic and aperiodic networks are capable of accurate path integration, despite important differences in their attractor manifolds. We quantify the rate at which errors in the velocity integration accumulate as a function of network size and intrinsic noise within the network. With a plausible range of parameters and the inclusion of spike variability, our model networks can accurately integrate velocity inputs over a maximum of ∼10–100 meters and ∼1–10 minutes. These findings form a proof-of-concept that continuous attractor dynamics may underlie velocity integration in the dorsolateral medial entorhinal cortex. The simulations also generate pertinent upper bounds on the accuracy of integration that may be achieved by continuous attractor dynamics in the grid cell network. We suggest experiments to test the continuous attractor model and differentiate it from models in which single cells establish their responses independently of each other.     

Feedback identification of continuous microbial growth systems

The fundamental problem of dynamic modeling of continuous culture systems for process control and optimization is addressed. Forcing a system to bifurcation via feedback control is a very promising method for model discrimination and identification. Dynamic information is obtained by using this technique, the dynamic behavior of the chemostat as predicted by unstructured models, the model with delay, and a structured model has been analyzed. The method exposes significant differences in the nonlinear dynamic structure of the various models and can be implemented to discriminate between various possible models for a continuous culture system.     

Robust Bayesian hierarchical model using normal/independent distributions

The multilevel item response theory (MLIRT) models have been increasingly used in longitudinal clinical studies that collect multiple outcomes. The MLIRT models account for all the information from multiple longitudinal outcomes of mixed types (e.g., continuous, binary, and ordinal) and can provide valid inference for the overall treatment effects. However, the continuous outcomes and the random effects in the MLIRT models are often assumed to be normally distributed. The normality assumption can sometimes be unrealistic and thus may produce misleading results. The normal/independent (NI) distributions have been increasingly used to handle the outlier and heavy tail problems in order to produce robust inference. In this article, we developed a Bayesian approach that implemented the NI distributions on both continuous outcomes and random effects in the MLIRT models and discussed different strategies of implementing the NI distributions. Extensive simulation studies were conducted to demonstrate the advantage of our proposed models, which provided parameter estimates with smaller bias and more reasonable coverage probabilities. Our proposed models were applied to a motivating Parkinson's disease study, the DATATOP study, to investigate the effect of deprenyl in slowing down the disease progression.     

Transforming between discrete and continuous angle distribution models: application to protein χ1 torsions

Two commonly employed angular-mobility models for describing amino-acid side-chain χ(1) torsion conformation, the staggered-rotamer jump and the normal probability density, are discussed and performance differences in applications to scalar-coupling data interpretation highlighted. Both models differ in their distinct statistical concepts, representing discrete and continuous angle distributions, respectively. Circular statistics, introduced for describing torsion-angle distributions by using a universal circular order parameter central to all models, suggest another distribution of the continuous class, here referred to as the elliptic model. Characteristic of the elliptic model is that order parameter and circular variance form complementary moduli. Transformations between the parameter sets that describe the probability density functions underlying the different models are provided. Numerical aspects of parameter optimization are considered. The issues are typified by using a set of χ(1) related (3) J coupling constants available for FK506-binding protein. The discrete staggered-rotamer model is found generally to produce lower order parameters, implying elevated rotatory variability in the amino-acid side chains, whereas continuous models tend to give higher order parameters that suggest comparatively less variation in angle conformations. The differences perceived regarding angular mobility are attributed to conceptually different features inherent to the models.     

A classification model for continuous responses: Identifying risk perception groups on health-related activities

In the current literature on latent variable models, much effort has been put on the development of dichotomous and polytomous cognitive diagnostic models (CDMs) for assessments. Recently, the possibility of using continuous responses in CDMs has been brought to discussion. But no Bayesian approach has been developed yet for the analysis of CDMs when responses are continuous. Our work is the first Bayesian framework for the continuous deterministic inputs, noisy, and gate (DINA) model. We also propose new interpretations for item parameters in this DINA model, which makes the analysis more interpretable than before. In addition, we have conducted several simulations to evaluate the performance of the continuous DINA model through our Bayesian approach. Then, we have applied the proposed DINA model to a real data example of risk perceptions for individuals over a range of health-related activities. The application results exemplify the high potential of the use of the proposed continuous DINA model to classify individuals in the study.     

Discrete and continuous state population models in a noisy world

Simple ecological models operate mostly with population densities using continuous variables. However, in reality densities could not change continuously, since the population itself consists of integer numbers of individuals. At first sight this discrepancy appears to be irrelevant, nevertheless, it can cause large deviations between the actual statistical behaviour of biological populations and that predicted by the corresponding models. We investigate the conditions under which simple models, operating with continuous numbers of individuals can be used to approximate the dynamics of populations consisting of integer numbers of individuals. Based on our definition for the (statistical) distance between the two models we show that the continuous approach is acceptable as long as sufficiently high biological noise is present, or, the dynamical behaviour is regular (non-chaotic). The concepts are illustrated with the Ricker model and tested on the Tribolium castaneum data series. Further, we demonstrate with the help of T. castaneum's model that if time series are not much larger than the possible population states (as in this practical case) the noisy discrete and continuous models can behave temporarily differently, almost independently of the noise level. In this case the noisy, discrete model is more accurate [OR has to be applied].