Total Economic Consequences of an Influenza Outbreak in the United States

Pandemic influenza represents a serious threat not only to the population of the United States, but also to its economy. In this study, we analyze the total economic consequences of potential influenza outbreaks in the United States for four cases based on the distinctions between disease severity and the presence/absence of vaccinations. The analysis is based on data and parameters on influenza obtained from the Centers for Disease Control and the general literature. A state‐of‐the‐art economic impact modeling approach, computable general equilibrium, is applied to analyze a wide range of potential impacts stemming from the outbreaks. This study examines the economic impacts from changes in medical expenditures and workforce participation, and also takes into consideration different types of avoidance behavior and resilience actions not previously fully studied. Our results indicate that, in the absence of avoidance and resilience effects, a pandemic influenza outbreak could result in a loss in U.S. GDP of $25.4 billion, but that vaccination could reduce the losses to $19.9 billion. When behavioral and resilience factors are taken into account, a pandemic influenza outbreak could result in GDP losses of $45.3 billion without vaccination and $34.4 billion with vaccination. These results indicate the importance of including a broader set of causal factors to achieve more accurate estimates of the total economic impacts of not just pandemic influenza but biothreats in general. The results also highlight a number of actionable items that government policymakers and public health officials can use to help reduce potential economic losses from the outbreaks.     

基于提前期的多阶通用件库存模型

针对单个企业内部应用通用件的环境,在假定一个多阶生产系统的基础上,对应用通用件的库存量水平及其成本进行了分析,建立了多阶通用件库存模型,提出通过对采用通用件和不采用通用件的成本差异的比较,来决策多阶生产系统中在哪一阶工序采用通用件,并求解出每一阶工序优化的基本库存水平。     

二维变系数线性微分系统的求解定理

给出二维变系数线性微分系统在已知某解的情形下求通解的公式,并直接应用此公式,导出几类二维变系数线性微分系统的通解公式.对理论和实际应用都是有益的.     

论孟子仁学的内涵和实质

本文以“爱人”为核心,从亲亲、爱人、爱物三个方面,比较全面地分析和探讨了孟子仁学思想的基本内涵,认为为仁需经过亲亲而爱人,由爱人而爱物的步骤来完成,揭示了一个比较完整的仁学系统,从而把儒家学说核心范畴和最高道德理想的仁,以一种平实简易的样态呈现于世人,人们只要扩充“仁之端”,拓展“亲亲”之情,“盈科而后进”,则可以成为仁人。     

基于"互联网+"的普通高校大学英语教学模式探究

随着"互联网+"普及力度的加大，"互联网+"背景下的大学英语教学已成为国内外语言界的热议话题，是一种新形态、新方向、新领域的教学模式，其本质就在于依托互联网的资源优势、秉承现代教育理论、以现代信息技术为载体来引导学生开展自主学习，有机地融合线上与线下教学，满足当前大学生的英语学习需要。分析了"互联网+"给普通高校大学英语教学带来的转变，剖析了"互联网+"背景下深化大学英语教学模式的具体路径。     

《高等有机化学》研究性教学的实践与思考

研究性教学是以培养提高学生创新思维能力为主要目的的一种教学方式。针对《高等有机化学》教学中存在的问题,提出了研究性教学的理念和改革教学方法的思路。在教改实践中,积极倡导教师在教学中的主导作用和研究生的主体地位,以参与式、研讨式以及研究报告会等多种形式展开教学,引导研究生进行探究性的学习,在研究性教学实践中实施创新教育。     

Asymptotic approximations to the distribution of Kendall's sample τ

This paper provides a saddlepoint approximation to the distribution of the sample version of Kendall's τ, which is a measure of association between two samples. The saddlepoint approximation is compared with the Edgeworth and the normal approximations, and with the bootstrap resampling distribution. A numerical study shows that with small sample sizes the saddlepoint approximation outperforms both the normal and the Edgeworth approximations. This paper gives also an analytical comparison between approximated and exact cumulants of the sample Kendall's τ when the two samples are independent.     

Computational comparison of the general weighted moments estimators of the scale parameter of a Pareto distribution with a known shape parameter with other estimators based on a multiply type II-censored sample

Wu et al. [Computational comparison for weighted moments estimators and BLUE of the scale parameter of a Pareto distribution with known shape parameter under type II multiply censored sample, Appl. Math. Comput. 181 (2006), pp. 1462–1470] proposed the weighted moments estimators (WMEs) of the scale parameter of a Pareto distribution with known shape parameter on a multiply type II-censored sample. They claimed that some WMEs are better than the best linear unbiased estimator (BLUE) based on the exact mean-squared error (MSE). In this paper, the general WME (GWME) is proposed and the computational comparison of the proposed estimator with the WMEs and BLUE is done on the basis of the exact MSE for given sample sizes and different censoring schemes. As a result, the GWME is performing better than the best estimator among 12 WMEs and BLUE for all cases. Therefore, GWME is recommended for use. At last, one example is given to demonstrate the proposed GWME.     

Gaussian copula distributions for mixed data,with application in discrimination

The construction of a joint model for mixed discrete and continuous random variables that accounts for their associations is an important statistical problem in many practical applications. In this paper, we use copulas to construct a class of joint distributions of mixed discrete and continuous random variables. In particular, we employ the Gaussian copula to generate joint distributions for mixed variables. Examples include the robit-normal and probit-normal-exponential distributions, the first for modelling the distribution of mixed binary-continuous data and the second for a mixture of continuous, binary and trichotomous variables. The new class of joint distributions is general enough to include many mixed-data models currently available. We study properties of the distributions and outline likelihood estimation; a small simulation study is used to investigate the finite-sample properties of estimates obtained by full and pairwise likelihood methods. Finally, we present an application to discriminant analysis of multiple correlated binary and continuous data from a study involving advanced breast cancer patients.     

Approximation of power in multivariate analysis

We consider the calculation of power functions in classical multivariate analysis. In this context, power can be expressed in terms of tail probabilities of certain noncentral distributions. The necessary noncentral distribution theory was developed between the 1940s and 1970s by a number of authors. However, tractable methods for calculating the relevant probabilities have been lacking. In this paper we present simple yet extremely accurate saddlepoint approximations to power functions associated with the following classical test statistics: the likelihood ratio statistic for testing the general linear hypothesis in MANOVA; the likelihood ratio statistic for testing block independence; and Bartlett's modified likelihood ratio statistic for testing equality of covariance matrices.