Help-seeking helps: help-seeking and group image

Seeking help from an outgroup can be difficult, especially when the outgroup is known to stereotype the ingroup negatively and the potential recipient cares strongly about its social image. However, we ask if even highly-identified ingroup members may seek help from a judgemental outgroup if doing so allows them to disconfirm the outgroup’s negative stereotype of the ingroup. We presented participants with one of two negative outgroup stereotypes of their ingroup. One could be disconfirmed through seeking help, the other could not. Study 1 (N = 43) showed group members were aware of the strategic implications of seeking help for disconfirming these stereotypes. Study 2 (N = 43) showed high identifiers acted on such strategic knowledge by seeking more help from the outgroup when help-seeking could disconfirm a negative stereotype of their group (than when it could not). Implications for the seeking and acceptance of help are discussed

A linear noise approximation for stochastic epidemic models fit to partially observed incidence counts

Stochastic epidemic models (SEMs) fit to incidence data are critical to elucidating outbreak dynamics, shaping response strategies, and preparing for future epidemics. SEMs typically represent counts of individuals in discrete infection states using Markov jump processes (MJPs), but are computationally challenging as imperfect surveillance, lack of subject-level information, and temporal coarseness of the data obscure the true epidemic. Analytic integration over the latent epidemic process is impossible, and integration via Markov chain Monte Carlo (MCMC) is cumbersome due to the dimensionality and discreteness of the latent state space. Simulation-based computational approaches can address the intractability of the MJP likelihood, but are numerically fragile and prohibitively expensive for complex models. A linear noise approximation (LNA) that approximates the MJP transition density with a Gaussian density has been explored for analyzing prevalence data in large-population settings, but requires modification for analyzing incidence counts without assuming that the data are normally distributed. We demonstrate how to reparameterize SEMs to appropriately analyze incidence data, and fold the LNA into a data augmentation MCMC framework that outperforms deterministic methods, statistically, and simulation-based methods, computationally. Our framework is computationally robust when the model dynamics are complex and applies to a broad class of SEMs. We evaluate our method in simulations that reflect Ebola, influenza, and SARS-CoV-2 dynamics, and apply our method to national surveillance counts from the 2013--2015 West Africa Ebola outbreak

Efficient data augmentation for fitting stochastic epidemic models to prevalence data

Stochastic epidemic models describe the dynamics of an epidemic as a disease spreads through a population. Typically, only a fraction of cases are observed at a set of discrete times. The absence of complete information about the time evolution of an epidemic gives rise to a complicated latent variable problem in which the state space size of the epidemic grows large as the population size increases. This makes analytically integrating over the missing data infeasible for populations of even moderate size. We present a data augmentation Markov chain Monte Carlo (MCMC) framework for Bayesian estimation of stochastic epidemic model parameters, in which measurements are augmented with subject-level disease histories. In our MCMC algorithm, we propose each new subject-level path, conditional on the data, using a time-inhomogeneous continuous-time Markov process with rates determined by the infection histories of other individuals. The method is general, and may be applied, with minimal modifications, to a broad class of stochastic epidemic models. We present our algorithm in the context of multiple stochastic epidemic models in which the data are binomially sampled prevalence counts, and apply our method to data from an outbreak of influenza in a British boarding school

Final evaluation of the saving gateway 2 pilot: main report

The Saving Gateway is a government initiative aimed at encouraging savings behaviour among people who do not usually save. Each pound placed into a Saving Gateway account is matched by the government at a certain rate and up to a monthly contribution limit. Matching provides a transparent and understandable incentive for eligible individuals to place funds in an account

Improving construction productivity: implications of even flow production principles

Subcontracting has been widely used in order to address the high level of variability and associated risks in the complex configuration of residential construction production systems. However, the explosion of subcontracting and the parade of trades have made the construction operations very fragmented, leading to lack of predictability and adequate control on schedules and quality. The present paper proposes a set of system configurations for residential construction. On this basis, after an extensive review of the efforts to model construction production problems, several discrete event simulation models have been developed so as to assess tangible performance measures. Comparing and contrasting the results, two attributes of the system were found to be critical to yield better performance measures. In the proposed flexible system design, fewer cross-trained subcontractors undertake integrated work processes. Also, the number of houses under construction does not grow infinitely and is proportional to the system capacity

Predictive Modeling of Cholera Outbreaks in Bangladesh

Despite seasonal cholera outbreaks in Bangladesh, little is known about the relationship between environmental conditions and cholera cases. We seek to develop a predictive model for cholera outbreaks in Bangladesh based on environmental predictors. To do this, we estimate the contribution of environmental variables, such as water depth and water temperature, to cholera outbreaks in the context of a disease transmission model. We implement a method which simultaneously accounts for disease dynamics and environmental variables in a Susceptible-Infected-Recovered-Susceptible (SIRS) model. The entire system is treated as a continuous-time hidden Markov model, where the hidden Markov states are the numbers of people who are susceptible, infected, or recovered at each time point, and the observed states are the numbers of cholera cases reported. We use a Bayesian framework to fit this hidden SIRS model, implementing particle Markov chain Monte Carlo methods to sample from the posterior distribution of the environmental and transmission parameters given the observed data. We test this method using both simulation and data from Mathbaria, Bangladesh. Parameter estimates are used to make short-term predictions that capture the formation and decline of epidemic peaks. We demonstrate that our model can successfully predict an increase in the number of infected individuals in the population weeks before the observed number of cholera cases increases, which could allow for early notification of an epidemic and timely allocation of resources.Comment: 43 pages, including appendices, 5 figures, 1 table in the main tex

The impact of construction commencement intervals on residential production building

One model of operation that production builders can use is continuous construction. They build typical house models and generally work with the same subcontractors. In this continuous operation, an order from the sales department triggers the process, which only commences construction when the first required crew becomes available. In this system the decision to commence construction relies on the readiness of the first activity. However the effects of this decision on the whole construction process are often ignored. This research aims to shed light on the importance of construction commencement decisions by highlighting the consequences of this decision on the whole production system

Framework for improving workflow stability: deployment of optimized capacity buffers in a synchronized construction production

Construction sites are dynamic environments due to the influence of variables such as changes in design and processes, unsteady demand, and unavailability of trades. These variables adversely affect productivity and can cause an unstable workflow in the network of trade contractors. Previous research on workflow stability in the construction and manufacturing domains has shown the effectiveness of 'pull' production or 'rate driven' construction. Pull systems authorize the start of construction when a job is completed and leaves the trade contractor network. However, the problem with pull systems is that completion dates are not explicitly considered and therefore additional mechanisms are required to ensure the due date integrity. On this basis, the aim of this investigation is to improve the coordination between output and demand using optimal-sized capacity buffers. Towards this aim, production data of two Australian construction companies were collected and analyzed. Capacity and cost optimizations were conducted to find the optimum buffer that strikes the balance between late completion costs and lost revenue opportunity. Following this, simulation experiments were designed and run to analyze different 'what-if' production scenarios. The findings show that capacity buffers enable builders to ensure a desired service level. Size of the capacity buffer is more sensitive to the level of variability in contractor processes than other production variables. This work contributes to the body-of-knowledge by improving production control in constructionanddeploymentof capacity buffers to achieve a stableworkflow.In addition, constructioncompaniescanuse the easy-to-use framework tested in this study to compute the optimal size for capacity buffers that maximizes profit and prevents late completions

Prevention through design: Trade-offs in reducing occupational health and safety risk for the construction and operation of a facility

Purpose - The research aims to explore the interaction between design decisions that reduce occupational health and safety (OHS) risk in the operation stage of a facility's life cycle and the OHS experiences of workers in the construction stage. Design/methodology/approach - Data was collected from three construction projects in Australia. Design decisions were examined to understand the reasons they were made and the impact that they had on OHS in the construction and operation stages. Findings - The case examples reveal that design decisions made to reduce OHS risk during the operation of a facility can introduce new hazards in the construction stage. These decisions are often influenced by stakeholders external to the project itself. Research limitations/implications - The results provide preliminary evidence of challenges inherent in designing for OHS across the lifecycle of a facility. Further research is needed to identify and evaluate methods by which risk reduction across all stages of a facility's life cycle can be optimised. Practical implications - The research highlights the need to manage tensions between designing for safe construction and operation of a facility. Originality/value - Previous research assumes design decisions that reduce OHS risk in one stage of a facility's life cycle automatically translate to a net risk reduction across the life cycle. The research highlights the need to consider the implications of PtD decision-making focused on one stage of the facility's life cycle for OHS outcomes in other stages