State estimation for nonlinear state-space transmission models of tuberculosis

Given the high prevalence of tuberculosis (TB) and the mortality rate associated with the disease, numerous models, such as the Gammaitoni and Nucci (GN) model, were developed to model the risk of transmission. These models typically rely on a quanta generation rate as a measurement of infectivity. Since the quanta generation rate cannot be measured directly, the unique contribution of this work is to develop state estimators to estimate the quanta generation rate from available measurements. To estimate the quanta generation rate, the GN model is adapted into an augmented single-room GN model and a simplified two-room GN model. Both models are shown to be observable, i.e., it is theoretically possible to estimate the quanta generation rate given available measurements. Kalman filters are used to estimate the quanta generation rate. First, a continuous-time extended Kalman filter is used for both adapted models using a simulation and measurement sampling rate of 60 s. Accurate quanta generate rate estimates are achieved in both cases. A more realistic scenario is also considered with a measurement sampling rate of one day. For these estimates, a hybrid extended Kalman filter (HEKF) is used. Accurate quanta generation rate estimates are achieved for the more realistic scenario. Future work could potentially use the HEKFs, the adapted models, and real-time measurements in a control system feedback loop to reduce the transmission of TB in confined spaces such as hospitals.     

Replenishing the pipeline: A quantitative approach to optimising the sourcing of new projects

Large pharmaceutical companies maintain a portfolio of assets, some of which are projects under development while others are on the market and generating revenue. The budget allocated to R&D may not always be sufficient to fund all the available projects for development. Much attention has been paid to the selection of optimal subsets of available projects to fit within the available budget. In this paper, we argue the need for a forward-looking approach to portfolio decision-making. We develop a quantitative model that allows the portfolio management to evaluate the need for future inflow of new projects to achieve revenue at desired levels, often aspiring to a certain annual revenue growth. Optimisation methods are developed for the presented model, allowing an optimal choice of number, timing and type of projects to be added to the portfolio. The proposed methodology allows for a proactive approach to portfolio management, prioritisation, and optimisation. It provides a quantitatively based support for strategic decisions regarding the efforts needed to secure the future development pipeline and revenue stream of the company.     

Unemployment and Monetary Integration: On the relevance of wage-formation characteristics in a target-zone regime versus an irrevocably fixed exchange-rate regime

Abstract. This paper examines the relevance of wage-formation characteristics for the evolution of unemployment, comparing the current semi-fixed exchange rate system (EMS, described by a target zone regime) with an irrevocably fixed exchange-rate regime (EMU). The topic is analyzed in a model which emphasizes the working of the labour market and distinguishes between three types of shocks (a shock to money demand, goods demand and goods supply). The analysis points out that the impact of wage formation on unemployment depends crucially on the kind of shock as well as the exchange-rate regime in force.     

Research into unaccompanied refugee minors in family foster care: Promoting adaptive functioning?

Unaccompanied Refugee Minors (URMs) are at high risk for trauma and maladaptive functioning. Although foster care is best suited to come forth to their needs, obtaining a better understanding of the strengths and flaws of foster care for URM can help to improve their development and psychological wellbeing. Twenty-seven URM cared for in family foster care filled in questionnaires measuring trauma, behavioural difficulties, resilience and contact with persons from the home and host country, whilst their foster care workers assessed the quality of the living environment. URMs had more trauma symptoms than other immigrants and Belgian adolescents. Furthermore, compared to Belgian counterparts, levels of resilience were low. However, URM did not have more behavioural problems. The quality of the living environment in kinship care was worse than the quality in non-kinship foster care, and social support and cultural sensitivity were protective factors. This study found evidence for developmental risks for URM in foster care. Integration, social support and a connection with the home culture were protective factors. Contrary to other studies, no evidence was found of kinship foster care being preferential.     

Win statistics (win ratio,win odds,and net benefit) can complement one another to show the strength of the treatment effect on time-to-event outcomes

Conventional analyses of a composite of multiple time-to-event outcomes use the time to the first event. However, the first event may not be the most important outcome. To address this limitation, generalized pairwise comparisons and win statistics (win ratio, win odds, and net benefit) have become popular and have been applied to clinical trial practice. However, win ratio, win odds, and net benefit have typically been used separately. In this article, we examine the use of these three win statistics jointly for time-to-event outcomes. First, we explain the relation of point estimates and variances among the three win statistics, and the relation between the net benefit and the Mann–Whitney U statistic. Then we explain that the three win statistics are based on the same win proportions, and they test the same null hypothesis of equal win probabilities in two groups. We show theoretically that the Z-values of the corresponding statistical tests are approximately equal; therefore, the three win statistics provide very similar p-values and statistical powers. Finally, using simulation studies and data from a clinical trial, we demonstrate that, when there is no (or little) censoring, the three win statistics can complement one another to show the strength of the treatment effect. However, when the amount of censoring is not small, and without adjustment for censoring, the win odds and the net benefit may have an advantage for interpreting the treatment effect; with adjustment (e.g., IPCW adjustment) for censoring, the three win statistics can complement one another to show the strength of the treatment effect. For calculations we use the R package WINS, available on the CRAN (Comprehensive R Archive Network).