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Respiratory syncytial virus (RSV) imposes a substantial burden on pediatric hospital capacity in Europe. Promising prophylactic interventions against RSV including monoclonal antibodies (mAb) and maternal immunizations (MI) are close to licensure. Therefore, we aimed to evaluate the cost-effectiveness of potential mAb and MI interventions against RSV in infants, for six European countries. We used a static cohort model to compare costs and health effects of four intervention programs to no program and to each other: year-round MI, year-round mAb, seasonal mAb (October to April), and seasonal mAb plus a catch-up program in October. Input parameters were obtained from national registries and literature. Influential input parameters were identified with the expected value of partial perfect information and extensive scenario analyses (including the impact of interventions on wheezing and asthma). From the health care payer perspective, and at a price of €50 per dose (mAb and MI), seasonal mAb plus catch-up was cost-saving in Scotland, and cost-effective for willingness-to-pay (WTP) values ≥€20,000 (England, Finland) or €30,000 (Denmark) per quality adjusted life-year (QALY) gained for all scenarios considered, except when using ICD-10 based hospitalization data. For the Netherlands, seasonal mAb was preferred (WTP value: €30,000-€90,000) for most scenarios. For Veneto region (Italy), either seasonal mAb with or without catch-up or MI was preferred, depending on the scenario and WTP value. From a full societal perspective (including leisure time lost), the seasonal mAb plus catch-up program was cost-saving for all countries except the Netherlands. The choice between a MI or mAb program depends on the level and duration of protection, price, availability, and feasibility of such programs, which should be based on the latest available evidence. Future research should focus on measuring accurately age-specific RSV-attributable hospitalizations in very young children.

The explicit consideration of uncertainty is essential in addressing most planning and operation issues encountered in the management of complex systems. Unfortunately, the resulting stochastic programming formulations, integer ones in particular, are generally hard to solve when applied to realistically-sized instances. A common approach is to consider the simpler deterministic version of the formulation, even if it is well known that the solution quality could be arbitrarily bad. In this paper, we aim to identify meaningful information, which can be extracted from the solution of the deterministic problem, in order to reduce the size of the stochastic one. Focusing on two-stage formulations, we show how and under which conditions the reduced costs associated to the variables in the deterministic formulation can be used as an indicator for excluding/retaining decision variables in the stochastic model. We introduce a new measure, the Loss of Reduced Costs-based Variable Fixing (LRCVF), computed as the difference between the optimal values of the stochastic problem and its reduced version obtained by fixing a certain number of variables. We relate the LRCVF with existing measures and show how to select the set of variables to fix. We then illustrate the interest of the proposed LRCVF and related heuristic procedure, in terms of computational time reduction and accuracy in finding the optimal solution, by applying them to a wide range of problems from the literature.

Many salamander populations are declining, and methods to determine how best to allocate limited resources to slow or reverse these declines could support land managers in their decision‐making processes. Multiple types of uncertainty may delay management decisions, including when (1) knowledge of a species' ecology is incomplete, (2) climate change effects on environmental covariates are uncertain, and (3) the efficacy of management alternatives is unknown. For management decisions, a value‐of‐information analysis can identify which uncertainties are critical to reduce in order to identify an optimal strategy from a set of possible management actions. If the same management action is optimal across the full range of uncertainties, then resources for research can be redirected toward active management. Using value‐of‐information analyses, we examine the effect of uncertainty on identifying optimal management to maximize the future expected occupancy of Plethodon shenandoah, a Federally Endangered high‐elevation endemic salamander that is threatened by climate change. Out of 11 management actions proposed by National Park Service managers, those that increase environmental moisture are expected to maximize occupancy, and we find that the selection of this action is robust to all the identified uncertainties. We show that, even in systems with multiple sources of large uncertainty, value of information analyses discriminate among investments in species management.

Why do we sometimes opt for actions or items that we do not value the most? Under current neurocomputational theories, such preference reversals are typically interpreted in terms of errors that arise from the unreliable signaling of value to brain decision systems. But, an alternative explanation is that people may change their mind because they are reassessing the value of alternative options while pondering the decision. So, why do we carefully ponder some decisions, but not others? In this work, we derive a computational model of the metacognitive control of decisions or MCD. In brief, we assume that fast and automatic processes first provide initial (and largely uncertain) representations of options' values, yielding prior estimates of decision difficulty. These uncertain value representations are then refined by deploying cognitive (e.g., attentional, mnesic) resources, the allocation of which is controlled by an effort-confidence tradeoff. Importantly, the anticipated benefit of allocating resources varies in a decision-by-decision manner according to the prior estimate of decision difficulty. The ensuing MCD model predicts response time, subjective feeling of effort, choice confidence, changes of mind, as well as choice-induced preference change and certainty gain. We test these predictions in a systematic manner, using a dedicated behavioral paradigm. Our results provide a quantitative link between mental effort, choice confidence, and preference reversals, which could inform interpretations of related neuroimaging findings.

A network game assigns a level of collectively generated wealth to every network that can form on a given set of players. A variable network game combines a network game with a network formation probability distribution, describing certain restrictions on network formation. Expected levels of collectively generated wealth and expected individual payoffs can be formulated in this setting. We investigate properties of the resulting expected wealth levels as well as the expected variants of well-established network game values as allocation rules that assign to every variable network game a payoff to the players in a variable network game. We establish two axiomatizations of the Expected Myerson Value, originally formulated and proven on the class of communication situations, based on the well-established component balance, equal bargaining power and balanced contributions properties. Furthermore, we extend an established axiomatization of the Position Value based on the balanced link contribution property to the Expected Position Value.

In this paper, we propose two new optimal reinsurance models in which both premium budget constraints and the reinsurer’s risk limits are taken into account. To be precise, we assume that the reinsurance premium has an upper bound, and that the admissible ceded loss functions have a pre-specified upper limit. Moreover, we assume that the reinsurance premium principle is calculated by the expected value premium principle. Under the optimality criteria of minimizing the value at risk and conditional value at risk of the insurer’s total risk exposure, we derive the explicit optimal reinsurance treaties, which are layer reinsurance treaties. A new approach is developed to construct the optimal reinsurance treaties. Comparisons with existing studies are also made. Finally, we provide a numerical study based on real data and an example to illustrate the proposed models and results. Our work provides a novel generalization of several known achievements in the literature.

In recent years, deep learning methods have been widely used in combination with control charts to improve the monitoring efficiency of complete data. However, due to time and cost constraints, data obtained from reliability life tests are often type-I right censored. Traditional control charts become inefficient for monitoring this type of data. Thus, researchers have proposed various control charts with conditional expected values (CEV) or conditional median (CM) to improve efficiency for right-censored data under normal and non-normal conditions. This study combines the exponentially weighted moving average (EWMA) CEV and CM chart with deep learning methods to increase efficiency for gamma type-I right-censored data. A statistical simulation and a real-world case are presented to assess the proposed method, which outperforms the traditional EWMA charts with CEV and CM in various skewness coefficient values and censoring rates for gamma type-I right-censored data.

In this paper, a general method of calculating the expected values and variances for degree-based topological indices in random spiro chains are obtained. Based on the general method, the explicit analytical expressions for the expected values and variances of some important degree-based topological indices in random spiro chains are presented, in which some known results are included. Besides, the expected values and variances for degree-based topological indices in random spiro chains are compared. In the end, the extremal values and the average values for degree-based topological indices of a spiro chain with n hexagons are determined.

We derive a new theoretical lower bound for the expected supremum of drifted fractional Brownian motion with Hurst index $H\\in(0,1)$ over a (in)finite time horizon. Extensive simulation experiments indicate that our lower bound outperforms the Monte Carlo estimates based on very dense grids for $H\\in(0,\\tfrac{1}{2})$ . Additionally, we derive the Paley–Wiener–Zygmund representation of a linear fractional Brownian motion in the general case and give an explicit expression for the derivative of the expected supremum at $H=\\tfrac{1}{2}$ in the sense of Bisewski, Dȩbicki and Rolski (2021).

Background Difficulties in cognitive control over negative emotional stimuli are a key characteristic of depression. The Expected Value of Control (EVC) provides a framework for understanding how cognitive control is allocated, focusing on the motivational factors of efficacy and reward. Efficacy is the likelihood that an effort will result in a specific result, while reward is the value assigned to that outcome. However, the impact of emotion on the estimation of EVC has not been explored. We investigated the interplay between emotion and motivation, using the EVC theoretical framework, in depression. Methods We utilized a within-between-subject design. The subjects were healthy controls ( n  = 31) and those with depression ( n  = 36), who underwent a clinical diagnostic interview, completed the General Health Questionnaire-12, the Beck Depression Inventory-II, and participated in an incentivized Emotional Stroop Paradigm, whereby participants received cues indicating different levels of efficacy (low vs. high) and reward (low vs. high) prior to the targeted stimuli. Results Significant interactions were detected between a) group × emotional valence × efficacy, and b) group × reward regarding accuracy rates on the Emotional Stroop Task. Follow-up analyses revealed that during high-efficacy trials, the Control group demonstrated significantly greater accuracy than the Depressed group for both positive and neutral stimuli. In low-efficacy trials, the Controls were also significantly more accurate than the Depressed group when responding to negative stimuli. Additionally, the Depressed group performed significantly worse than Controls on high-reward trials, no significant difference was detected between the two groups on low-reward trials. Conclusion The emotional valence of stimuli can influence the assessment of reward efficacy, and individuals with depression may have difficulties focusing on reward cues. Further research is necessary to incorporate emotion into the EVC framework.