The timely identification of the high-risk groups for nosocomial infections (NIs) plays a vital role in its prevention and control. Therefore, it is crucial to investigate whether the ABO blood group is a risk factor for NI. In this study, patients with NI and non-infectionwere matched by the propensity score matching method and a logistic regression model was used to analyse the matched datasets. The study found that patients with the B&AB blood group were susceptible to Escherichia coli (OR = 1.783, p = 0.039); the A blood group were susceptible to Staphylococcus aureus (OR = 2.539, p = 0.019) and Pseudomonas aeruginosa (OR = 5.724, p = 0.003); the A&AB blood group were susceptible to Pseudomonas aeruginosa (OR = 4.061, p = 0.008); the AB blood group were vulnerable to urinary tract infection (OR = 13.672, p = 0.019); the B blood group were susceptible to skin and soft tissue infection (OR = 2.418, p = 0.016); and the B&AB blood group were vulnerable to deep incision infection (OR = 4.243, p = 0.043). Summarily, the patient’s blood group is vital for identifying high-risk groups for NIs and developing targeted prevention and control measures for NIs.

Ageism has become a social problem in an aged society. This study re-examines an ageism affirmation strategy; the designs and plans for this study were pre-registered. Participants were randomly assigned to either an experimental group (in which they read an explanatory text about the stereotype embodiment theory and related empirical findings) or a control group (in which they read an irrelevant text). The hypothesis was that negative attitudes toward older adults are reduced in the experimental group compared with the control group. Bayesian analysis was used for hypothesis testing. The results showed that negative attitudes toward older adults were reduced in the experimental group. These findings contribute to the development of psychological and gerontological interventions aimed at affirming ageism. In addition, continued efforts to reduce questionable research practices and the spread of Bayesian analysis in psychological research are expected.

In this paper, we analyze a two-queue random time-limited Markov-modulated polling model. In the first part of the paper, we investigate the fluid version: fluid arrives at the two queues as two independent flows with deterministic rate. There is a single server that serves both queues at constant speeds. The server spends an exponentially distributed amount of time in each queue. After the completion of such a visit time to one queue, the server instantly switches to the other queue, i.e., there is no switch-over time.

For this model, we first derive the Laplace–Stieltjes transform (LST) of the stationary marginal fluid content/workload at each queue. Subsequently, we derive a functional equation for the LST of the two-dimensional workload distribution that leads to a Riemann–Hilbert boundary value problem (BVP). After taking a heavy-traffic limit, and restricting ourselves to the symmetric case, the BVP simplifies and can be solved explicitly.

In the second part of the paper, allowing for more general (Lévy) input processes and server switching policies, we investigate the transient process limit of the joint workload in heavy traffic. Again solving a BVP, we determine the stationary distribution of the limiting process. We show that, in the symmetric case, this distribution coincides with our earlier solution of the BVP, implying that in this case the two limits (stationarity and heavy traffic) commute.

Increasingly, laws are being proposed and passed by governments around the world to regulate artificial intelligence (AI) systems implemented into the public and private sectors. Many of these regulations address the transparency of AI systems, and related citizen-aware issues like allowing individuals to have the right to an explanation about how an AI system makes a decision that impacts them. Yet, almost all AI governance documents to date have a significant drawback: they have focused on what to do (or what not to do) with respect to making AI systems transparent, but have left the brunt of the work to technologists to figure out how to build transparent systems. We fill this gap by proposing a stakeholder-first approach that assists technologists in designing transparent, regulatory-compliant systems. We also describe a real-world case study that illustrates how this approach can be used in practice.

The past decade has seen the rise of “data portals” as online devices for making data public. They have been accorded a prominent status in political speeches, policy documents, and official communications as sites of innovation, transparency, accountability, and participation. Drawing on research on data portals around the world, data portal software, and associated infrastructures, this paper explores three approaches for studying the social life of data portals as technopolitical devices: (a) interface analysis, (b) software analysis, and (c) metadata analysis. These three approaches contribute to the study of the social lives of data portals as dynamic, heterogeneous, and contested sites of public sector datafication. They are intended to contribute to critically assessing how participation around public sector datafication is invited and organized with portals, as well as to rethinking and recomposing them.

In this paper, we determine the fair value of a pension buyout contract under the assumption that changes in mortality can have an impact on financial markets. Our proposed model allows for shocks to occur simultaneously in mortality rates and financial markets, so that strong changes in mortality rates can affect interest rates and asset prices. This approach challenges the common but very strong assumption that mortality and market risk drivers are independent. A simulation-based pricing framework is applied to determine the buyout premium for a hypothetical fully funded pension scheme. The results of an extensive sensitivity analysis show how buyout prices are affected by changes in mortality and financial markets. Surprisingly, we find that the impact of shocks is similar whether or not these shocks occur simultaneously or not, although there are some differences in annuity prices and buyout premiums. We clearly see that the intensity and severity of shocks, and asset price volatility play a dominant role for buyout prices.

Risk measurements are clearly central to risk management, in particular for banks, (re)insurance companies, and investment funds. The question of the appropriateness of risk measures for evaluating the risk of financial institutions has been heavily debated, especially after the financial crisis of 2008/2009. Another concern for financial institutions is the pro-cyclicality of risk measurements. In this paper, we extend existing work on the pro-cyclicality of the Value-at-Risk to its main competitors, Expected Shortfall, and Expectile: We compare the pro-cyclicality of historical quantile-based risk estimation, taking into account the market state. To characterise the latter, we propose various estimators of the realised volatility. Considering the family of augmented GARCH(p, q) processes (containing well-known GARCH models and iid models, as special cases), we prove that the strength of pro-cyclicality depends on the three factors: the choice of risk measure and its estimators, the realised volatility estimator and the model considered, but, no matter the choices, the pro-cyclicality is always present. We complement this theoretical analysis by performing simulation studies in the iid case and developing a case study on real data.

Consider the problem of determining the Bayesian credibility mean $E(X_{n+1}|X_1,\cdots, X_n),$ whenever the random claims $X_1,\cdots, X_n,$ given parameter vector $\boldsymbol{\Psi},$ are sampled from the K-component mixture family of distributions, whose members are the union of different families of distributions. This article begins by deriving a recursive formula for such a Bayesian credibility mean. Moreover, under the assumption that using additional information $Z_{i,1},\cdots,Z_{i,m},$ one may probabilistically determine a random claim $X_i$ belongs to a given population (or a distribution), the above recursive formula simplifies to an exact Bayesian credibility mean whenever all components of the mixture distribution belong to the exponential families of distributions. For a situation where a 2-component mixture family of distributions is an appropriate choice for data modelling, using the logistic regression model, it shows that: how one may employ such additional information to derive the Bayesian credibility model, say Logistic Regression Credibility model, for a finite mixture of distributions. A comparison between the Logistic Regression Credibility (LRC) model and its competitor, the Regression Tree Credibility (RTC) model, has been given. More precisely, it shows that under the squared error loss function, it shows the LRC’s risk function dominates the RTC’s risk function at least in an interval which about $0.5.$ Several examples have been given to illustrate the practical application of our findings.

We study (asymmetric) $U$-statistics based on a stationary sequence of $m$-dependent variables; moreover, we consider constrained $U$-statistics, where the defining multiple sum only includes terms satisfying some restrictions on the gaps between indices. Results include a law of large numbers and a central limit theorem, together with results on rate of convergence, moment convergence, functional convergence, and a renewal theory version.

Special attention is paid to degenerate cases where, after the standard normalization, the asymptotic variance vanishes; in these cases non-normal limits occur after a different normalization.

The results are motivated by applications to pattern matching in random strings and permutations. We obtain both new results and new proofs of old results.

Innovative, responsible data use is a critical need in the global response to the coronavirus disease-2019 (COVID-19) pandemic. Yet potentially impactful data are often unavailable to those who could utilize it, particularly in data-poor settings, posing a serious barrier to effective pandemic mitigation. Data challenges, a public call-to-action for innovative data use projects, can identify and address these specific barriers. To understand gaps and progress relevant to effective data use in this context, this study thematically analyses three sets of qualitative data focused on/based in low/middle-income countries: (a) a survey of innovators responding to a data challenge, (b) a survey of organizers of data challenges, and (c) a focus group discussion with professionals using COVID-19 data for evidence-based decision-making. Data quality and accessibility and human resources/institutional capacity were frequently reported limitations to effective data use among innovators. New fit-for-purpose tools and the expansion of partnerships were the most frequently noted areas of progress. Discussion participants identified building capacity for external/national actors to understand the needs of local communities can address a lack of partnerships while de-siloing information. A synthesis of themes demonstrated that gaps, progress, and needs commonly identified by these groups are relevant beyond COVID-19, highlighting the importance of a healthy data ecosystem to address emerging threats. This is supported by data holders prioritizing the availability and accessibility of their data without causing harm; funders and policymakers committed to integrating innovations with existing physical, data, and policy infrastructure; and innovators designing sustainable, multi-use solutions based on principles of good data governance.

Measuring and quantifying dependencies between random variables (RVs) can give critical insights into a dataset. Typical questions are: ‘Do underlying relationships exist?’, ‘Are some variables redundant?’, and ‘Is some target variable Y highly or weakly dependent on variable X?’ Interestingly, despite the evident need for a general-purpose measure of dependency between RVs, common practice is that most data analysts use the Pearson correlation coefficient to quantify dependence between RVs, while it is recognized that the correlation coefficient is essentially a measure for linear dependency only. Although many attempts have been made to define more generic dependency measures, there is no consensus yet on a standard, general-purpose dependency function. In fact, several ideal properties of a dependency function have been proposed, but without much argumentation. Motivated by this, we discuss and revise the list of desired properties and propose a new dependency function that meets all these requirements. This general-purpose dependency function provides data analysts with a powerful means to quantify the level of dependence between variables. To this end, we also provide Python code to determine the dependency function for use in practice.

Healthcare-associated foodborne outbreaks (HA-FBOs) can cause significant morbidity and mortality, affecting particularly vulnerable hospital populations. Electronic records of food served in healthcare facilities (HCFs) could be useful for timely investigations of HA-FBOs. We explored the availability and usability of electronic food menu data to support investigations of HA-FBOs through a survey among 35 HCFs in Germany (n = 13) and in Italy (n = 22). Large variability was reported in the storage time of menu data (from no storage up to 10 years) and their formats, including paper, electronic (PDF, Word, Excel), or fully searchable databases (15/22 in Italian HCFs, 3/13 in German HCFs). Food products that may present a risk to vulnerable persons – including deli salads, raw/fermented sausage products, soft cheese, smoked fish or frozen berries – were offered on the menu of all HCFs in Germany, and one-third of the Italian HCFs. The usability of electronic food menu data for the prevention or investigation of HA-FBOs may be suboptimal in a large number of HCFs in Germany, as well as in some HCFs in Italy. Standardised collection for use of electronic food menu data might help discover the association between illnesses and food eaten during outbreak investigations. Hospital hygienists, food safety and public health authorities should collaborate to increase implementation of food safety guidelines.

Rabies virus (RABV) is a deadly zoonosis that circulates in wild carnivore populations in North America. Intensive management within the USA and Canada has been conducted to control the spread of the raccoon (Procyon lotor) variant of RABV and work towards elimination. We examined RABV occurrence across the northeastern USA and southeastern Québec, Canada during 2008–2018 using a multi-method, dynamic occupancy model. Using a 10 km × 10 km grid overlaid on the landscape, we examined the probability that a grid cell was occupied with RABV and relationships with management activities (oral rabies vaccination (ORV) and trap-vaccinate-release efforts), habitat, neighbour effects and temporal trends. We compared raccoon RABV detection probabilities between different surveillance samples (e.g. animals that are strange acting, road-kill, public health samples). The management of RABV through ORV was found to be the greatest driver in reducing the occurrence of rabies on the landscape. Additionally, RABV occupancy declined further with increasing duration of ORV baiting programmes. Grid cells north of ORV management were at or near elimination ($\hat{\psi }_{{\rm north}}$ = 0.00, s.e. = 0.15), managed areas had low RABV occupancy ($\hat{\psi }_{{\rm managed}}$ = 0.20, s.e. = 0.29) and enzootic areas had the highest level of RABV occupancy ($\hat{\psi }_{{\rm south}}$ = 0.83, s.e. = 0.06). These results provide evidence that past management actions have been being successful at the goals of reducing and controlling the raccoon variant of RABV. At a finer scale we also found that vaccine bait type and bait density impacted RABV occupancy. Detection probabilities varied; samples from strange acting animals and public health had the highest detection rates. Our results support the movement of the ORV zone south within the USA due to high elimination probabilities along the US border with Québec. Additional enhanced rabies surveillance is still needed to ensure elimination is maintained.

During an epidemic outbreak, typically only partial information about the outbreak is known. A common scenario is that the infection times of individuals are unknown, but individuals, on displaying symptoms, are identified as infectious and removed from the population. We study the distribution of the number of infectives given only the times of removals in a Markovian susceptible–infectious–removed (SIR) epidemic. Primary interest is in the initial stages of the epidemic process, where a branching (birth–death) process approximation is applicable. We show that the number of individuals alive in a time-inhomogeneous birth–death process at time $t \geq 0$, given only death times up to and including time t, is a mixture of negative binomial distributions, with the number of mixing components depending on the total number of deaths, and the mixing weights depending upon the inter-arrival times of the deaths. We further consider the extension to the case where some deaths are unobserved. We also discuss the application of the results to control measures and statistical inference.

From 1 January 2022 to 4 September 2022, a total of 53 996 mpox cases were confirmed globally. Cases are predominantly concentrated in Europe and the Americas, while other regions are also continuously observing imported cases. This study aimed to estimate the potential global risk of mpox importation and consider hypothetical scenarios of travel restrictions by varying passenger volumes (PVs) via airline travel network. PV data for the airline network, and the time of first confirmed mpox case for a total of 1680 airports in 176 countries (and territories) were extracted from publicly available data sources. A survival analysis technique in which the hazard function was a function of effective distance was utilised to estimate the importation risk. The arrival time ranged from 9 to 48 days since the first case was identified in the UK on 6 May 2022. The estimated risk of importation showed that regardless of the geographic region, most locations will have an intensified importation risk by 31 December 2022. Travel restrictions scenarios had a minor impact on the global airline importation risk against mpox, highlighting the importance to enhance local capacities for the identification of mpox and to be prepared to carry out contact tracing and isolation.