Investment communities: Behavioral attitudes and economic dynamics

Using a real-world data set encompassing the daily portfolio holdings and exposures of complex investment funds, we derive a set of quantitative attributes to capture essential behavioral features of fund managers. We find the existence and stability of three investment attitudes, namely the conservative, the reactive, and the pro-active profiles, defining communities that respond differently when facing external shocks. The conservative community has behavioral similarities that tend to decrease due to external shocks, the reactive community members greatly increase their activity level especially during turmoil phases, while delegated investors in the pro-active community are more resilient to turbulence and counterbalance the impact of the events by adjusting their portfolio exposures in advance. We show that exogenous shocks only temporarily perturb the behavioral traits of the communities which then go back to their original states once the distress is embedded.     

An Agent‐Based Model for Pathogen Persistence and Cross‐Contamination Dynamics in a Food Facility

We used an agent‐based modeling (ABM) framework and developed a mathematical model to explain the complex dynamics of microbial persistence and spread within a food facility and to aid risk managers in identifying effective mitigation options. The model explicitly considered personal hygiene practices by food handlers as well as their activities and simulated a spatially explicit dynamic system representing complex interaction patterns among food handlers, facility environment, and foods. To demonstrate the utility of the model in a decision‐making context, we created a hypothetical case study and used it to compare different risk mitigation strategies for reducing contamination and spread of Listeria monocytogenes in a food facility. Model results indicated that areas with no direct contact with foods (e.g., loading dock and restroom) can serve as contamination niches and recontaminate areas that have direct contact with food products. Furthermore, food handlers’ behaviors, including, for example, hygiene and sanitation practices, can impact the persistence of microbial contamination in the facility environment and the spread of contamination to prepared foods. Using this case study, we also demonstrated benefits of an ABM framework for addressing food safety in a complex system in which emergent system‐level responses are predicted using a bottom‐up approach that observes individual agents (e.g., food handlers) and their behaviors. Our model can be applied to a wide variety of pathogens, food commodities, and activity patterns to evaluate efficacy of food‐safety management practices and quantify contamination reductions associated with proposed mitigation strategies in food facilities.     

Predictive Hierarchic Modeling of Operational Characteristics in Clinical Trials

An analytic methodology for patient enrollment modeling using a Poisson-gamma model is developed by Anisimov & Fedorov (2005–2007). For modeling hierarchic processes associated with enrollment, a new methodology using evolving stochastic processes is proposed. This provides rather general and unified framework to describe various operational processes associated with enrollment. The technique for calculating predictive distributions, mean, and credibility bounds for evolving processes is developed. Some applications to modeling operational characteristics in clinical trials are considered with focus to modeling events associated with incoming and follow-up patients in different settings. For these models, predictive characteristics are derived in a closed form.     

3-6岁学前儿童情绪与行为问题的调查与分析——以惠州市为例

摘 要：应用儿童长处与困难（SDQ）问卷（父母版），采用分层随机抽样方法，对惠州市1528名3-6岁儿童的情绪与行为问题进行调查。研究发现：惠州市3-6岁儿童情绪与行为问题异常情况比较严峻，儿童心理健康状况值得重视；不同地域、年龄学前儿童存在不同的情绪与行为问题，需要分类干预。其中农村儿童的情绪与行为问题异常情况尤为严重，需要得到广泛的关注和解决。     

Campylobacter QMRA: A Bayesian Estimation of Prevalence and Concentration in Retail Foods Under Clustering and Heavy Censoring

A Bayesian statistical temporal‐prevalence‐concentration model (TPCM) was built to assess the prevalence and concentration of pathogenic campylobacter species in batches of fresh chicken and turkey meat at retail. The data set was collected from Finnish grocery stores in all the seasons of the year. Observations at low concentration levels are often censored due to the limit of determination of the microbiological methods. This model utilized the potential of Bayesian methods to borrow strength from related samples in order to perform under heavy censoring. In this extreme case the majority of the observed batch‐specific concentrations was below the limit of determination. The hierarchical structure was included in the model in order to take into account the within‐batch and between‐batch variability, which may have a significant impact on the sample outcome depending on the sampling plan. Temporal changes in the prevalence of campylobacter were modeled using a Markovian time series. The proposed model is adaptable for other pathogens if the same type of data set is available. The computation of the model was performed using OpenBUGS software.     

水驱砂岩油藏孔喉结构变化的三维网络模拟

以长期水驱实验为基础，建立了等效水驱砂岩储层孔喉结构变化的三维网络模拟模型，结合三维微粒运移机制和有限差分求解方法，得到了长期水驱砂岩油藏孔喉结构变化规律：（1） 冲刷后喉道半径呈增加趋势，喉道半径变化范围变大，极小喉道半径呈微弱减小趋势；（2） 孔隙网络模型中冲刷半径扩大的孔道分布形式与原始孔隙网络结构密切相关，并非所有的大孔道都串联起来贯穿岩芯孔隙网络的两个端面，但入口端和出口端部分大孔道相互连通，形成端面上的大孔道网络群。网络模拟注水结果结合采油井测试，可为注水剖面的调整提供更加可靠的依据。     

Small area estimation strategies for large population surveys: a comparison of design and model-based methods

Small area estimation (SAE) concerns with how to reliably estimate population quantities of interest when some areas or domains have very limited samples. This is an important issue in large population surveys, because the geographical areas or groups with only small samples or even no samples are often of interest to researchers and policy-makers. For example, large population health surveys, such as Behavioural Risk Factor Surveillance System and Ohio Mecaid Assessment Survey (OMAS), are regularly conducted for monitoring insurance coverage and healthcare utilization. Classic approaches usually provide accurate estimators at the state level or large geographical region level, but they fail to provide reliable estimators for many rural counties where the samples are sparse. Moreover, a systematic evaluation of the performances of the SAE methods in real-world setting is lacking in the literature. In this paper, we propose a Bayesian hierarchical model with constraints on the parameter space and show that it provides superior estimators for county-level adult uninsured rates in Ohio based on the 2012 OMAS data. Furthermore, we perform extensive simulation studies to compare our methods with a collection of common SAE strategies, including direct estimators, synthetic estimators, composite estimators, and Datta GS, Ghosh M, Steorts R, Maples J.'s [Bayesian benchmarking with applications to small area estimation. Test 2011;20(3):574–588] Bayesian hierarchical model-based estimators. To set a fair basis for comparison, we generate our simulation data with characteristics mimicking the real OMAS data, so that neither model-based nor design-based strategies use the true model specification. The estimators based on our proposed model are shown to outperform other estimators for small areas in both simulation study and real data analysis.     

Deriving Behavior Model Parameters from Survey Data: Self‐Protective Behavior Adoption During the 2009–2010 Influenza A(H1N1) Pandemic

In this paper, we demonstrate how public opinion surveys can be designed to collect information pertinent to computational behavior modeling, and we present the results of a public opinion and behavior survey conducted during the 2009–2010 H1N1 influenza pandemic. The results are used to parameterize the Health Belief Model of individual health‐protective decision making. Survey subjects were asked questions about their perceptions of the then‐circulating influenza and attitudes towards two personal protective behaviors: vaccination and avoidance of crowds. We empirically address two important issues in applying the Health Belief Model of behavior to computational infectious disease simulation: (1) the factors dynamically influencing the states of the Health Belief Model variables and (2) the appropriateness of the Health Belief Model in describing self‐protective behavior in the context of pandemic influenza.     

Introducing a Phobia Program into a Community Mental Health Center

No abstract available for this article.     

Prevalence of Elder Mistreatment as Reported by Social Workers in a Probability Sample of Adult Day Health Care Clients

ABSTRACT

There are growing numbers of Adult Day Health Care (ADHC) programs providing needed health services to community-dwelling elderly. Therefore, ADHC staff are in an opportune position to identify and to intervene in suspected elder mistreatment (EM) cases. In this paper, prevalence estimates of EM are reported for a probability sample of ADHC clients in New York State, using data provided during a social worker informant interview. The abuse “signs and symptoms” items in the social worker informant interview schedule were divided into two categories: (1) physical indicators and (2) client's behavioral indicators. Physical indicators included unexplained bruises and welts, unexplained burns, unexplained lacerations or abrasions, human bite marks, and frequent injuries that are “accidental” or “unexplained.” Client's behavioral indicators included apprehension, being frightened, and afraid to go home. EM prevalence for all 8 items was 12.3 percent. When “apprehensive” was excluded, the EM prevalence was 3.6 percent in this sample. These findings suggest that ADHC provides a point of contact for the assessment and intervention of EM that might otherwise be overlooked among elders who are often isolated or homebound.