Inferring an Augmented Bayesian Network to Confront a Complex Quantitative Microbial Risk Assessment Model with Durability Studies: Application to Bacillus Cereus on a Courgette Purée Production Chain

The Monte Carlo (MC) simulation approach is traditionally used in food safety risk assessment to study quantitative microbial risk assessment (QMRA) models. When experimental data are available, performing Bayesian inference is a good alternative approach that allows backward calculation in a stochastic QMRA model to update the experts’ knowledge about the microbial dynamics of a given food‐borne pathogen. In this article, we propose a complex example where Bayesian inference is applied to a high‐dimensional second‐order QMRA model. The case study is a farm‐to‐fork QMRA model considering genetic diversity of Bacillus cereus in a cooked, pasteurized, and chilled courgette purée. Experimental data are Bacillus cereus concentrations measured in packages of courgette purées stored at different time‐temperature profiles after pasteurization. To perform a Bayesian inference, we first built an augmented Bayesian network by linking a second‐order QMRA model to the available contamination data. We then ran a Markov chain Monte Carlo (MCMC) algorithm to update all the unknown concentrations and unknown quantities of the augmented model. About 25% of the prior beliefs are strongly updated, leading to a reduction in uncertainty. Some updates interestingly question the QMRA model.     

The Effectiveness of Educational Programs to Improve Recognition and Reporting of Elder Abuse and Neglect: A Systematic Review of the Literature

Health professionals often lack adequate protocols or knowledge to detect, manage, and prevent elder maltreatment. This systematic review describes and evaluates existing literature on the effectiveness of educational interventions to improve health professionals' recognition and reporting of elder abuse and neglect. Fourteen articles described 22 programs ranging from brief didactics to experiential learning and targeted a variety of health and social service audiences. Most evaluations were limited to satisfaction measures. These programs may result in increased awareness, collaboration, and improved case finding. However, using the published literature to guide new program planning is constrained by lack of details and limited evaluations.     

Harnessing the reliability of outcome measures

A method is presented for engineering the necessary levels of measurement reliability for evaluating ongoing programs. Two studies of levels of client functioning at a community mental health center (CMHC), in which an outcome measure, the Global Assessment Scale (GAS), did not perform as expected, drew attention to the need for better control of outcome measure reliabilities. Drawing from generalizability theory, a study was conducted of three sources of GAS score variance — clients, raters, and training in the use of the scale. Several estimates of reliability (ERs) were developed, depending on the manner in which the GAS ratings were, or would be, obtained in the CMHC. The differences among these ERs clarified why the GAS had lower reliability when used in our setting. Finally, two hypothetical examples are described to illustrate the utility of applying generalizability theory to achieve higher reliabilities for outcome measures.     

Consumer Behaviour and Order Fulfilment in Online Retailing: A Systematic Review

This paper provides a systematic review of consumer behaviour and order fulfilment in online retailing. The objective of this review is threefold: first, to identify elements of order‐fulfilment operations that are relevant to online consumer behaviour (purchase, repurchase, product return); second, to understand the relationship between order‐fulfilment performance and consumer behaviour; and third, to inspire future research on developing consumer service strategies that takes account of these behavioural responses to order‐fulfilment performance outcomes. The paper is based on a systematic review of literature on online consumer behaviour and order‐fulfilment operations, mainly in the fields of marketing and operations, published in international peer‐reviewed journals between 2000 and September 2015. This study indicates that the current literature on online consumer behaviour focuses mainly on the use of marketing tools to improve consumer service levels. Very little research has been conducted on the use of consumer service instruments to steer consumer behaviour or, consequently, to manage related order‐fulfilment activities better. The study culminates in a framework that encompasses elements of order‐fulfilment operations and their relationship to online consumer behaviour. This paper is the first comprehensive review of online consumer behaviour that takes aspects of order‐fulfilment operations into account from both marketing and operations perspectives.     

Partial covariate adjusted regression

Covariate adjusted regression (CAR) is a recently proposed adjustment method for regression analysis where both the response and predictors are not directly observed [?entürk, D., Müller, H.G., 2005. Covariate adjusted regression. Biometrika 92, 75–89]. The available data have been distorted by unknown functions of an observable confounding covariate. CAR provides consistent estimators for the coefficients of the regression between the variables of interest, adjusted for the confounder. We develop a broader class of partial covariate adjusted regression (PCAR) models to accommodate both distorted and undistorted (adjusted/unadjusted) predictors. The PCAR model allows for unadjusted predictors, such as age, gender and demographic variables, which are common in the analysis of biomedical and epidemiological data. The available estimation and inference procedures for CAR are shown to be invalid for the proposed PCAR model. We propose new estimators and develop new inference tools for the more general PCAR setting. In particular, we establish the asymptotic normality of the proposed estimators and propose consistent estimators of their asymptotic variances. Finite sample properties of the proposed estimators are investigated using simulation studies and the method is also illustrated with a Pima Indians diabetes data set.     

A second-order iterated smoothing algorithm

Simulation-based inference for partially observed stochastic dynamic models is currently receiving much attention due to the fact that direct computation of the likelihood is not possible in many practical situations. Iterated filtering methodologies enable maximization of the likelihood function using simulation-based sequential Monte Carlo filters. Doucet et al. (2013) developed an approximation for the first and second derivatives of the log likelihood via simulation-based sequential Monte Carlo smoothing and proved that the approximation has some attractive theoretical properties. We investigated an iterated smoothing algorithm carrying out likelihood maximization using these derivative approximations. Further, we developed a new iterated smoothing algorithm, using a modification of these derivative estimates, for which we establish both theoretical results and effective practical performance. On benchmark computational challenges, this method beat the first-order iterated filtering algorithm. The method’s performance was comparable to a recently developed iterated filtering algorithm based on an iterated Bayes map. Our iterated smoothing algorithm and its theoretical justification provide new directions for future developments in simulation-based inference for latent variable models such as partially observed Markov process models.