A simple in-host model for Mycobacterium tuberculosis that captures all infection outcomes

ABSTRACT

Tuberculosis infection can result in clearance, latent infection, or active disease, with slow or fast progression. A four-dimensional model of in-host tuberculosis infection includes macrophages, T lymphocytes, tuberculosis bacteria, and their interactions. Changes in the infection rate, cell-mediated immunity rate, macrophage loss rate, and bacteria killing rate most affect disease outcomes. Simulations show that a periodic solution can occur. When the infected macrophage killing rate is constant, a backward bifurcation exists and the system is globally stable.     

Assessing an Adult Mental Health Clinic for Age-Related Issues

A community mental health clinic in an urban setting is the subject of an exploratory assessment to determine whether its 55 + patients are being treated according to the special diagnostic and treatment issues that tend to affect older people. Depression is found to be high on the list of diagnoses in this age group. As many as 40% of the group are considered to be at risk for suicide. The interrelationship of medical and psychological factors is emphasized. Sixty-two percent of those studied are determined to be socially isolated. Treatment techniques recommended in the literature for older patients are summarized, and it is determined that, while therapists at the clinic.have not been trained in geriatric psychology, many are instinctively utilizing these techniques. Recommendations are made for increased use of groupwork with older patients, inservice education for staff, attention to medical complications, improved outreach, and involvement of family.     

Optimal Designing of Variables Sampling Plan for Resubmitted Lots

This article proposes a variables sampling plan that can be applied for sampling inspection of resubmitted lots when the quality characteristic of interest follows the normal distribution. Resubmission of lots for inspection is allowed in some situations where the original inspection results are suspected or when the supplier or producer is allowed to opt for resampling as per the provisions of the contract, etc. The advantages of this proposed variables sampling plan over the existing single sampling variables plan are discussed. Tables are also constructed for the selection of optimal parameters of known and unknown standard deviation variables resampling scheme for specified two points on the operating characteristic curve, namely, the acceptable quality level and the limiting quality level along with the producer and consumer's risks. The optimization problem is formulated as a nonlinear programming where the objective function to be minimized is the average sample number and the constraints are related to lot acceptance probabilities at acceptable quality level and limiting quality level under the operating characteristic curve.     

Sampling from a Discrete Distribution While Preserving Monotonicity

This article describes a cutpoint sampling method for efficiently sampling from an n-point discrete distribution that preserves the monotone relationship between a uniform deviate and the random variate it generates. This property is useful for developing a sampling plan to reduce variance in a Monte Carlo or simulation study. The expected number of comparisons with this method is derived and shown to be bounded above by (m + n ?1)/n, where m denotes the number of cut-points. The alias sampling method, which is regarded as the most efficient table sampling technique, generally lacks the monotone property and requires 2n storage locations, whereas the proposed cutpoint sampling method requires m + n storage locations. The article describes two modifications for cases in which n is large and possibly infinite. It is shown that circumstances arise in which the cutpoint method requires fewer comparisons on average than the alias method does for exactly the same space requirement. The article also describes an algorithm to implement the proposed method.     

Stabilized Sieve Sampling: A Point-Estimator Analysis

A modification of sieve sampling is proposed that returns a constant sample size. It is a scheme that selects line items with probability proportional to size (PPS) and nearly without replacement. An unbiased estimator of the total error amount is presented and its variance derived. Conditions under which the scheme is more efficient than sieve sampling and than PPS with replacement sampling are given.     

Quota fulfilment and errors in stratification

It is desired to draw a random sample containing specified numbers of individuals from each stratum of a population. First a random sample of size N is chosen from the whole populations and the stratum of each individual ascertained; then any shortfall is made up by selecting individuals with known stratum affiliation. Optimal values of N are sought allowing for cost structure and also the possibility of error in ascertaining the strata to which individuals in the first sample belong.     

Selection of multiple deferred (dependent) state sampling plans

This paper presents procedures and tables for the selection of minimum sample size multiple deferred (dependent) state sampling plan of type MDS and MDS - 1 for various entry conditions such as (AQL, LQL), (AQL, AOQL) etc.     

Modified tables for the selection of double sampling attribute plan indexed by aql and lql

This paper presents modified tables for the selection of double sampling plans for given Acceptable Qualtiy Level (AQL), producer’s risk (α) Limiting Quality Level (LQL), and consumer’s risk (ß) giving the minimum sum of Average Sample Numbers (ASN) at AQL and LQL under the conditions of the Poisson model for the Operating Characteristic (OC) curve.     

有序响应变量的贝叶斯模型选择及其在COPD疾病防治中的应用

慢性阻塞性肺病(COPD)是一种发病率、死亡率都非常高的疾病,且COPD的诊断和严重程度分级依赖于肺功能的检查,但是由于肺功能检查仪器价格昂贵,使得这项检查在很多经济欠发达地区尤其是农村基层医院并没有普及。本文基于有序响应变量模型致力于研究一种便于基层和社区使用的可以初步判别COPD病情的模型,以期提高我国基层和社区的COPD 防治水平。利用贝叶斯变量选择方法和数据增强的潜变量策略得到了易于实施的Gibbs后验抽样算法。数值模拟分析进一步说明了本文提出的有序响应变量贝叶斯模型选择方法的有效性,实例分析得到了易于判别COPD严重程度的稀疏模型。     

大规模数据下基于充分降维的Leverage重要性抽样方法

随着信息技术的飞速发展,大规模数据在短时间内搜集并储存下来,为分析决策提供了巨大的信息量,也给统计建模带来了一定难度。对于样本容量大、变量个数少的数据,Leverage重要性抽样是一个简便可行的方法。本文发现,该方法中度量样本重要性的Leverage分数与因变量无关,而且在维度较大的情形下对样本没有区分程度,使得估计结果较差。为了同时考虑因变量和维度的影响,本文提出了基于充分降维的Leverage重要性抽样方法。该方法以不损失信息为前提,在充分降维的空间内重新计算Leverage分数,使得抽样更具有代表性。模拟数据分析显示,在样本容量较大的复杂数据中,相比于原始的Leverage重要性抽样方法,本文提出的方法可以降低估计的均方误差。三个实际数据也证实了该方法的可行性和有效性。