北京市SARS疫情统计分析

本文对严重急性呼吸道综合症 SA RS疾病进行了统计分析 .文章首先以北京市海淀区 SARS确诊病例和疑似病例为研究对象 ,分别按照地区、人口进行了统计分析 ,得出海淀区不同小区疫情发展的显著程度 .然后利用正交试验设计 ,分别就年龄、性别、职业等因素对海淀区 SARS确诊病人和疑似病人影响程度进行了统计分析 ,得出 2 1岁到 50岁的学生和干部得病率最高的统计结果 ,这完全符合海淀区的疫情事实 .这个统计结果已经被海淀区政府“海淀区政府应急管理信息系统 ( H EMIS)”[6]采用 . H EMIS系统作为中国第一个政府应急系统在海淀区 SARS防治中起到了一定作用 [1 ] .文章最后研究了北京市每天新增的确诊病例和每天疑似病例转为确诊病例之间的相关系数 ,从它们的相关性分析可以反映出防治 SARS疫情措施的有效性 .     

关于SARS的预测模型

建立了确诊病例和疑似病例累计人数随时间变化的差分方程模型.通过对其求解和合理的假设讨论了确诊病例数在不同的阶段随时间变化的情况.进一步,用我们的模型来模拟北京疫情(2003.4.20—2003.5.29)的变化情况.说明我们的模型具有一定的实用性.     

SARS的传播模型

建立了SARS传播的Logisitic阻滞增长模型和回归模型.     

SARS流行病传染动力学模型

建立了 SARS流行病的数学模型 ,根据部分国家和地区的 SARS疫情数据 ,计算出其模型参数 ,给出了各地 SARS疫情与模拟结果的比较图 ,模拟结果与实际疫情十分吻合 .分析了 SARS流行特征 ,并对疫情发展进行了预测 .     

一类SARS流行病动力系统的研究

建立了一类描述SARS流行病的常微分方程模型,利用常微分方程动力系统的理论,研究了我们所建立的模型,给出了该系统的奇点及奇点的类型,并作出了相图.我们得到的一个主要结论是:在SARS的控制中,政府行为是最重要的因素.     

一类SARS流行病模型的改进及分析

为进一步研究政府行为在SARS流行病控制中的作用,对我们建立的SARS流行病模型进行了改进.利用常微分方程动力系统的理论,研究了改进的模型,给出了奇点及奇点的类型,并作出了相图.得到的结论进一步表明:在SARS的控制中,政府行为是最重要的因素.     

用常微分方程模型分析预防和隔离措施对SARS发病率的影响

通过建立常微分方程模型 ,分析了预防和隔离措施对 SARS发病率的影响 ,并把计算结果与实际统计数据进行了比较 ,结果表明 ,及时高效的预防和隔离措施能够有效地控制 SARS的传播 .     

关于SARS病毒传播的马尔可夫骨架过程模型

根据SARS病毒传播的特性和侯振挺等人提出的马尔可夫骨架过程理论，建立了SARS病毒传播的马尔可夫骨架模型，并得出结论，在任一时刻的疑似病例数，传染病人数是某非负线性方程组的最小非负解。     

基于元胞自动机的SARS传播模型

根据SARS的局部性、潜伏性特征以及传染病个体传播的特点,人群接触通过近距离邻居发生,利用元胞自动机模型时间、空间上离散,以局部规则为基础,以同步更新为前提讨论整体性质的机制,将人群分为易感者,带菌者,病人,免疫者之后,以易感者对SARS的抵抗能力、病人或带菌者对SARS的传染能力、人群的大小为参数,建立了基于元胞自动机的SARS传播模型.得到的结果可为从传染动力学角度控制SARS的传播提供依据.     

一个新的非典型性肺炎模型及其分析

建立了一个新模型.它能很好的反映SARS病的特点,并且简单易算.在新模型中定义了一个重要的新参数θ,它反映了下一代感染者数量的变化.该模型具有很好的实用价值.应用这个模型建立了SARS病传播的一种预测方法,可以准确的预测若干天后的疾病传播情况;建立了参数的测定方法,并利用北京的数据进行了计算;对于我国早期数据缺失的情况提出了相应的处理方法;分析了毒王的特性并提出了判别标准.     

Modelling the flow of congestive heart failure patients through a hospital system

The number of hospital admissions in England due to heart failure is projected to increase by over 50% during the next 25 years. This will incur greater pressures on hospital managers to allocate resources in an effective manner. A reliable indicator for measuring the quantity of resources consumed by hospital patients is their length of stay (LOS) in care. This paper proposes modelling the length of time heart failure patients spend in hospital using a special type of Markov model, where the flow of patients through hospital can be thought of as consisting of three stages of care—short-, medium- and longer-term care. If it is assumed that new admissions into the ward are replacements for discharges, such a model may be used to investigate the case-mix of patients in hospital and the expected patient turnover during some specified period of time. An example is illustrated by considering hospital admissions to a Belfast hospital in Northern Ireland, between 2000 and 2004.     

Order acceptance and scheduling with machine availability constraints

We consider an order acceptance and scheduling model with machine availability constraints. The manufacturer (machine) is assumed to be available to process orders only within a number of discontinuous time intervals. To capture the real-life behavior of a typical manufacturer who has restrictions of time availability to process orders, our model allows the manufacturer to reject or outsource some of the orders. When an order is rejected or outsourced, an order-dependent cost of penalty will occur. The objective is to minimize the makespan of all accepted orders plus the total penalty of all rejected/outsourced orders. We study the approximability of the model and some of its important special cases.     

基于PCA和DEA方法的北京市可持续发展能力的评价研究

利用主成分分析法,以2000-2009年数据为基础得出了北京市可持续发展系统中的六类成本和效益性综合指数,依此从投入产出角度建立DEA评价模型,并通过模型中指标变量的调整,对北京市近10年可持续发展系统的总体水平进行了评价分析。得出北京具有可持续发展的基础和能力,但北京在可持续性发展中必须注重优化投入产出结构,提高资源与环境的利用率,搞好循环经济。     

Dynamics of a generalized predator‐prey model with delay and impulse via the basic reproduction number

In this paper, we study a generalized predator‐prey model with delay and impulse. The existence of the predator‐free periodic solution is investigated. We employ the approach and techniques coming from epidemiology and calculate the basic reproduction number for the predator. Using the basic reproduction number, we consider the global attraction of the predator‐free periodic solution and permanence of the model. As for application, an example is discussed. Furthermore, some numerical simulations are given to illustrate our results.     

Looking in the wrong place for healthcare improvements: A system dynamics study of an accident and emergency department

Accident and Emergency (A&E) units provide a route for patients requiring urgent admission to acute hospitals. Public concern over long waiting times for admissions motivated this study, whose aim is to explore the factors which contribute to such delays. The paper discusses the formulation and calibration of a system dynamics model of the interaction of demand pattern, A&E resource deployment, other hospital processes and bed numbers; and the outputs of policy analysis runs of the model which vary a number of the key parameters. Two significant findings have policy implications. One is that while some delays to patients are unavoidable, reductions can be achieved by selective augmentation of resources within, and relating to, the A&E unit. The second is that reductions in bed numbers do not increase waiting times for emergency admissions, their effect instead being to increase sharply the number of cancellations of admissions for elective surgery. This suggests that basing A&E policy solely on any single criterion will merely succeed in transferring the effects of a resource deficit to a different patient group.     

Surgical case scheduling as a generalized job shop scheduling problem

Surgical case scheduling allocates hospital resources to individual surgical cases and decides on the time to perform the surgeries. This task plays a decisive role in utilizing hospital resources efficiently while ensuring quality of care for patients. This paper proposes a new surgical case scheduling approach which uses a novel extension of the Job Shop scheduling problem called multi-mode blocking job shop (MMBJS). It formulates the MMBJS as a mixed integer linear programming (MILP) problem and discusses the use of the MMBJS model for scheduling elective and add-on cases. The model is illustrated by a detailed example, and preliminary computational experiments with the CPLEX solver on practical-sized instances are reported.     

A network model for nursing staff scheduling

The staffing of hospital nurses has become critical in recent years. As a means of containing skyrocketing costs, many hospitals are reducing nursing staff to a bare minimum. At the same time, the hospitals must maintain some minimal staffing level as insufficient staff could lead to a life threatening situation possibly with detrimental social, economic, and legal consequences. This article will present a model which can be used to determine the optimal scheduling of nursing staff under varying conditions. A typical problem will illustrate its use.     

Using a Markov reward model to estimate spend-down costs for a geriatric department

A Markov model is used to describe movements of geriatric patients within a hospital system where the states of the Markov chain are acute/rehabilitative, long-stay care, discharge or death. By assigning costs to the states of this model, we can estimate the spend-down costs of running down services given that there are no more admissions and different costs are assigned to acute/rehabilitative and long-stay care. The model is used to estimate the spend-down costs using data previously validated for three Departments of Geriatric Medicine in the South West Thames Region of England. Our approach allows hospital planners to identify cost-effective strategies which take into account the fact that some geriatric patients remain in long-stay care for very long periods of time.     

A pre-assignment heuristic algorithm for the Master Surgical Schedule Problem (MSSP)

In this paper a 0–1 linear programming model and a solution heuristic algorithm are developed in order to solve the so-called Master Surgical Schedule Problem (MSSP). Given a hospital department made up of different surgical units (i.e. wards) sharing a given number of Operating Rooms (ORs), the problem herein addressed is determining the assignment among wards and ORs during a given planning horizon, together with the subset of patients to be operated on during each day. Different resource constraints related to operating block time length, maximum OR overtime allowable by collective labour agreement and legislation, patient length of stay (LOS), available OR equipment, number of surgeons, number of stay and ICU beds, are considered. Firstly, a 0–1 linear programming model intended to minimise a cost function based upon a priority score, that takes into proper account both the waiting time and the urgency status of each patient, is developed. Successively, an heuristic algorithm that enables us to embody some pre-assignment rules to solve this NP-hard combinatorial optimisation problem, is presented. In particular, we force the assignment of each patient to a subset of days depending on his/her expected length of stay in order to allow closing some stay areas during the weekend and hence reducing overall hospitalisation cost of the department. The results of an extensive computational experimentation aimed at showing the algorithm efficiency in terms of computational time and solution effectiveness are given and analysed.