Early detection and active management of invasive group A Streptococcus (iGAS) infection outbreaks are essential. Here, we describe the changing epidemiology of outbreaks of iGAS in England between 2015 and 2019, a period of increasing incidence of iGAS infection. Data on iGAS infections were extracted from national public health management records and laboratory records. Outbreaks were described in size, duration, setting, and emm type. Overall, 194 outbreaks were identified, and reports increased each year, from 16 outbreaks in 2015 to 61 in 2019. The median outbreak size was 3 cases (n = 37; 19%), with 27% of outbreaks recording 4–10 cases (n = 53) and 7% recording more than 10 cases (n = 13). Outbreak duration ranged from 0 to 170 weeks (median 7). Settings of outbreaks changed over the study period, with increasing numbers observed in multiple settings. This study provides new insights into the changing burden of iGAS infection and outbreaks in England.

Methicillin-resistant Staphylococcus aureus (MRSA) spa type t4549 is increasingly prevalent in Denmark, yet its epidemiological sources remain unclear. This study aimed to generate hypotheses about possible risk factors that may be associated with MRSA t4549 infections. We conducted a nationwide case – case questionnaire study comparing MRSA t4549 cases to other MRSA types (t002, t008, t127, t304, and t223) reported between January 2022 and November 2023. The analysis, which included descriptive statistics and logistic regression, found that 75% of MRSA t4549 cases were male. Infections were significantly more frequent in the foot (28%) and toe (54%) compared to other MRSA types. Key risk factors identified were contact with pheasants (OR = 8.70; 95%CI 1.25–174.29), participation in indoor team sports (OR = 7.54, 95%CI: 1.58–54.82) and swimming (OR = 4.15, 95%CI: 1.97–9.03). Although the limited number of cases warrants cautious interpretation, it is crucial to emphasize the need for preventive measures at both the individual and sports facility levels. Further environmental studies are needed to clarify the role of the environment and wildlife in MRSA t4549 transmission. The increasing prevalence of this spa type in Denmark underlines the importance of implementing effective public health strategies to reduce the risk of MRSA transmission.

We study Pareto optimality in a decentralized peer-to-peer risk-sharing market where agents’ preferences are represented by robust distortion risk measures that are not necessarily convex. We obtain a characterization of Pareto-optimal allocations of the aggregate risk in the market, and we show that the shape of the allocations depends primarily on each agent’s assessment of the tail of the aggregate risk. We quantify the latter via an index of probabilistic risk aversion, and we illustrate our results using concrete examples of popular families of distortion functions. As an application of our results, we revisit the market for flood risk insurance in the United States. We present the decentralized risk sharing arrangement as an alternative to the current centralized market structure, and we characterize the optimal allocations in a numerical study with historical flood data. We conclude with an in-depth discussion of the advantages and disadvantages of a decentralized insurance scheme in this setting.

Metal–organic polyhedra (MOPs) are discrete, porous metal–organic assemblies known for their wide-ranging applications in separation, drug delivery, and catalysis. As part of The World Avatar (TWA) project—a universal and interoperable knowledge model—we have previously systematized known MOPs and expanded the explorable MOP space with novel targets. Although these data are available via a complex query language, a more user-friendly interface is desirable to enhance accessibility. To address a similar challenge in other chemistry domains, the natural language question-answering system “Marie” has been developed; however, its scalability is limited due to its reliance on supervised fine-tuning, which hinders its adaptability to new knowledge domains. In this article, we introduce an enhanced database of MOPs and a first-of-its-kind question-answering system tailored for MOP chemistry. By augmenting TWA’s MOP database with geometry data, we enable the visualization of not just empirically verified MOP structures but also machine-predicted ones. In addition, we renovated Marie’s semantic parser to adopt in-context few-shot learning, allowing seamless interaction with TWA’s extensive MOP repository. These advancements significantly improve the accessibility and versatility of TWA, marking an important step toward accelerating and automating the development of reticular materials with the aid of digital assistants.

In today’s world, smart algorithms—artificial intelligence (AI) and other intelligent systems—are pivotal for promoting the development agenda. They offer novel support for decision-making across policy planning domains, such as analysing poverty alleviation funds and predicting mortality rates. To comprehensively assess their efficacy and implications in policy formulation, this paper conducts a systematic review of 207 publications. The analysis underscores their integration within and across stages of the policy planning cycle: problem diagnosis and goal articulation; resource and constraint identification; design of alternative solutions; outcome projection; and evaluation. However, disparities exist in smart algorithm applications across stages, economic development levels, and Sustainable Development Goals (SDGs). While these algorithms predominantly focus on resource identification (29%) and contribute significantly to designing alternatives—such as long-term national energy policies—and projecting outcomes, including predicting multi-scenario land-use ecological security strategies, their application in evaluation remains limited (10%). Additionally, low-income nations have yet to fully harness AI’s potential, while upper-middle-income countries effectively leverage it. Notably, smart algorithm applications for SDGs also exhibit unevenness, with more emphasis on SDG 11 than on SDG 5 and SDG 17. Our study identifies literature gaps. Firstly, despite theoretical shifts, a disparity persists between physical and socioeconomic/environmental planning applications. Secondly, there is limited attention to policy-making in development initiatives, which is critical for improving lives. Future research should prioritise developing adaptive planning systems using emerging powerful algorithms to address uncertainty and complex environments. Ensuring algorithmic transparency, human-centered approaches, and responsible AI are crucial for AI accountability, trust, and credibility.

In recent years, there has been a global trend among governments to provide free and open access to data collected by Earth-observing satellites with the purpose of maximizing the use of this data for a broad array of research and applications. Yet, there are still significant challenges facing non-remote sensing specialists who wish to make use of satellite data. This commentary explores an illustrative case study to provide concrete examples of these challenges and barriers. We then discuss how the specific challenges faced within the case study illuminate some of the broader issues in data accessibility and utility that could be addressed by policymakers that aim to improve the reach of their data, increase the range of research and applications that it enables, and improve equity in data access and use.

The alignment of artificial intelligence (AI) systems with societal values and the public interest is a critical challenge in the field of AI ethics and governance. Traditional approaches, such as Reinforcement Learning with Human Feedback (RLHF) and Constitutional AI, often rely on pre-defined high-level ethical principles. This article critiques these conventional alignment frameworks through the philosophical perspectives of pragmatism and public interest theory, arguing against their rigidity and disconnect with practical impacts. It proposes an alternative alignment strategy that reverses the traditional logic, focusing on empirical evidence and the real-world effects of AI systems. By emphasizing practical outcomes and continuous adaptation, this pragmatic approach aims to ensure that AI technologies are developed according to the principles that are derived from the observable impacts produced by technology applications.

The preferential attachment model is a natural and popular random graph model for a growing network that contains very well-connected ‘hubs’. We study the higher-order connectivity of such a network by investigating the topological properties of its clique complex. We concentrate on the Betti numbers, a sequence of topological invariants of the complex related to the numbers of holes (equivalently, repeated connections) of different dimensions. We prove that the expected Betti numbers grow sublinearly fast, with the trivial exceptions of those at dimensions 0 and 1. Our result also shows that preferential attachment graphs undergo infinitely many phase transitions within the parameter regime where the limiting degree distribution has an infinite variance. Regarding higher-order connectivity, our result shows that preferential attachment favors higher-order connectivity. We illustrate our theoretical results with simulations.

Machine learning’s integration into reliability analysis holds substantial potential to ensure infrastructure safety. Despite the merits of flexible tree structure and formulable expression, random forest (RF) and evolutionary polynomial regression (EPR) cannot contribute to reliability-based design due to absent uncertainty quantification (UQ), thus hampering broader applications. This study introduces quantile regression and variational inference (VI), tailored to RF and EPR for UQ, respectively, and explores their capability in identifying material indices. Specifically, quantile-based RF (QRF) quantifies uncertainty by weighting the distribution of observations in leaf nodes, while VI-based EPR (VIEPR) works by approximating the parametric posterior distribution of coefficients in polynomials. The compression index of clays is taken as an exemplar to develop models, which are compared in terms of accuracy and reliability, and also with deterministic counterparts. The results indicate that QRF outperforms VIEPR, exhibiting higher accuracy and confidence in UQ. In the regions of sparse data, predicted uncertainty becomes larger as errors increase, demonstrating the validity of UQ. The generalization ability of QRF is further verified on a new creep index database. The proposed uncertainty-incorporated modeling approaches are available under diverse preferences and possess significant prospects in broad scientific computing domains.

The increasing popularity of large language models has not only led to widespread use but has also brought various risks, including the potential for systematically spreading fake news. Consequently, the development of classification systems such as DetectGPT has become vital. These detectors are vulnerable to evasion techniques, as demonstrated in an experimental series: Systematic changes of the generative models’ temperature proofed shallow learning—detectors to be the least reliable (Experiment 1). Fine-tuning the generative model via reinforcement learning circumvented BERT-based—detectors (Experiment 2). Finally, rephrasing led to a >90% evasion of zero-shot—detectors like DetectGPT, although texts stayed highly similar to the original (Experiment 3). A comparison with existing work highlights the better performance of the presented methods. Possible implications for society and further research are discussed.

We consider the moments and the distribution of hitting times on the lollipop graph which is the graph exhibiting the maximum expected hitting time among all the graphs having the same number of nodes. We obtain recurrence relations for the moments of all order and we use these relations to analyze the asymptotic behavior of the hitting time distribution when the number of nodes tends to infinity.

We show that for $\lambda\in[0,{m_1}/({1+\sqrt{1-{1}/{m_1}}})]$, the biased random walk’s speed on a Galton–Watson tree without leaves is strictly decreasing, where $m_1\geq 2$. Our result extends the monotonic interval of the speed on a Galton–Watson tree.

This article proposes Bayesian adaptive trials (BATs) as both an efficient method to conduct trials and a unifying framework for the evaluation of social policy interventions, addressing the limitations inherent in traditional methods, such as randomized controlled trials. Recognizing the crucial need for evidence-based approaches in public policy, the proposed approach aims to lower barriers to the adoption of evidence-based methods and to align evaluation processes more closely with the dynamic nature of policy cycles. BATs, grounded in decision theory, offer a dynamic, “learning as we go” approach, enabling the integration of diverse information types and facilitating a continuous, iterative process of policy evaluation. BATs’ adaptive nature is particularly advantageous in policy settings, allowing for more timely and context-sensitive decisions. Moreover, BATs’ ability to value potential future information sources positions it as an optimal strategy for sequential data acquisition during policy implementation. While acknowledging the assumptions and models intrinsic to BATs, such as prior distributions and likelihood functions, this article argues that these are advantageous for decision-makers in social policy, effectively merging the best features of various methodologies.

The tonuity, proposed by Chen et al. ((2019) ASTIN Bulletin: The Journal of the IAA, 49(1), 530.), is a combination of an immediate tontine and a deferred annuity. However, its switching time from tontine to annuity is fixed at the moment the contract is closed, possibly becoming sub-optimal if mortality changes over time. This article introduces an alternative tonuity product, wherein a dynamic switching condition is pivotal, relying on the observable mortality trends within a reference population. The switching from tontine to annuity then occurs automatically once the condition is satisfied. Using data from the Human Mortality Database and UK Continuous Mortality Investigation, we demonstrate that, in a changing environment, where an unforeseen mortality or longevity shock leads to an unexpected increase or decrease in mortality rates, the proposed dynamic tonuity contract can be preferable to the regular tonuity contract.

The Wiener–Hopf factors of a Lévy process are the maximum and the displacement from the maximum at an independent exponential time. The majority of explicit solutions assume the upward jumps to be either phase-type or to have a rational Laplace transform, in which case the traditional expressions are lengthy expansions in terms of roots located by means of Rouché’s theorem. As an alternative, compact matrix formulas are derived, with parameters computable by iteration schemes.

The H3N2 canine influenza virus (CIV) emerged from an avian reservoir in Asia to circulate entirely among dogs for the last 20 years. The virus was first seen circulating outside Asian dog populations in 2015, in North America. Utilizing viral genomic data in addition to clinical reports and diagnostic testing data, we provide an updated analysis of the evolution and epidemiology of the virus in its canine host. CIV in dogs in North America is marked by a complex life history – including local outbreaks, regional lineage die-outs, and repeated reintroductions of the virus (with diverse genotypes) from different regions of Asia. Phylogenetic and Bayesian analysis reveal multiple CIV clades, and viruses from China have seeded recent North American outbreaks, with 2 or 3 introductions in the past 3 years. Genomic epidemiology confirms that within North America the virus spreads very rapidly among dogs in kennels and shelters in different regions – but then dies out locally. The overall epidemic therefore requires longer-distance dispersal of virus to maintain outbreaks over the long term. With a constant evolutionary rate over 20 years, CIV still appears best adapted to transmission in dense populations and has not gained properties for prolonged circulation among dogs.

This paper characterizes irreducible phase-type representations for exponential distributions. Bean and Green (2000) gave a set of necessary and sufficient conditions for a phase-type distribution with an irreducible generator matrix to be exponential. We extend these conditions to irreducible representations, and we thus give a characterization of all irreducible phase-type representations for exponential distributions. We consider the results in relation to time-reversal of phase-type distributions, PH-simplicity, and the algebraic degree of a phase-type distribution, and we give applications of the results. In particular we give the conditions under which a Coxian distribution becomes exponential, and we construct bivariate exponential distributions. Finally, we translate the main findings to the discrete case of geometric distributions.

This paper demonstrates how learning the structure of a Bayesian network, often used to predict and represent causal pathways, can be used to inform policy decision-making.

We show that Bayesian networks are a rigorous and interpretable representation of interconnected factors that affect the complex environment in which policy decisions are made. Furthermore, Bayesian structure learning differentiates between proximal or immediate factors and upstream or root causes, offering a comprehensive set of potential causal pathways leading to specific outcomes.

We show how these causal pathways can provide critical insights into the impact of a policy intervention on an outcome. Central to our approach is the integration of causal discovery within a Bayesian framework, which considers the relative likelihood of possible causal pathways rather than only the most probable pathway.

We argue this is an essential part of causal discovery in policy making because the complexity of the decision landscape inevitably means that there are many near equally probable causal pathways. While this methodology is broadly applicable across various policy domains, we demonstrate its value within the context of educational policy in Australia. Here, we identify pathways influencing educational outcomes, such as student attendance, and examine the effects of social disadvantage on these pathways. We demonstrate the methodology’s performance using synthetic data and its usefulness by applying it to real-world data. Our findings in the real example highlight the usefulness of Bayesian networks as a policy decision tool and show how data science techniques can be used for practical policy development.

Clostridiodes difficile’s epidemiology has evolved over the past decades, being recognized as an important cause of disease in the community setting. Even so, there has been heterogeneity in the reports of CA-CDI. Therefore, the aim of this study was to assess the epidemiologic profile of CA-CDI.

This systematic review and meta-analysis were conducted according to PRISMA checklist and Cochrane guidelines (CRD42023451134). Literature search was performed by an experienced librarian from inception to April 2023, searching in databases like MEDLINE, Scopus, Web of Science, EMBASE, CCRCC, CDSR, and ClinicalTrials. Observational studies that reported prevalence, incidence of CA-CDI, or indicators to calculate them were included. Pool analysis was performed using a binomial-normal model via the generalized linear mixed model. Subgroup analysis and publication bias were also explored. A total of 49 articles were included, obtaining a prevalence of 5% (95% CI 3–8) and an incidence of 7.53 patients (95% CI 4.45–12.74) per 100,000 person-years.

In conclusion, this meta-analysis underscores that among the included studies, the prevalence of CA-CDI stands at 5%, with an incidence rate of 7.3 cases per 100,000 person-years. Noteworthy risk factors identified include prior antibiotic exposure and age.