We discuss the emerging technology of digital twins (DTs) and the expected demands as they scale to represent increasingly complex, interconnected systems. Several examples are presented to illustrate core use cases, highlighting a progression to represent both natural and engineered systems. The forthcoming challenges are discussed around a hierarchy of scales, which recognises systems of increasing aggregation. Broad implications are discussed, encompassing sensing, modelling, and deployment, alongside ethical and privacy concerns. Importantly, we endorse a modular and peer-to-peer view for aggregate (interconnected) DTs. This mindset emphasises that DT complexity emerges from the framework of connections (Wagg et al. [2024, The philosophical foundations of digital twinning, Preprint]) as well as the (interpretable) units that constitute the whole.

Reliability analysis of stress–strength models usually assumes that the stress and strength variables are independent. However, in numerous real-world scenarios, stress and strength variables exhibit dependence. This paper investigates the reliability estimation in a multicomponent stress–strength model for parallel-series system assuming that the dependence between stress and strength is based on the Clayton copula. The estimators for the unknown parameters and system reliability are derived using the two-step maximum likelihood estimation and the maximum product spacing methods. Additionally, confidence intervals are constructed by utilizing asymptotically normal distribution theory and bootstrap method. Furthermore, Monte Carlo simulations are conducted to compare the effectiveness of the proposed inference methods. Finally, a real dataset is analyzed for illustrative purposes.

In this paper, the model of bisexual branching processes affected by viral infectivity and with random control functions in independent and identically distributed (i.i.d.) random environments is established and the Markov property is given firstly. Then the relations of the probability generating functions of this model are studied, and some sufficient conditions for process extinction under common mating functions are presented. Finally, the limiting behaviors of the considered model after proper normalization, such as the sufficient conditions for the convergence in L1 and L2 and almost everywhere convergence, are investigated under the condition that the random control functions are super additive.

Multiple osteoarticular tuberculosis (MOT) represents an uncommon yet severe form of tuberculosis, characterized by a lack of systematic analysis and comprehension. Our objective was to delineate MOT’s epidemiological characteristics and establish a scientific foundation for prevention and treatment. We conducted searches across eight databases to identify relevant articles. Pearson’s chi-square test (Fisher’s exact test) and Bonferroni method were employed to assess osteoarticular involvement among patients of varying age and gender (α = 0.05). The study comprised 98 articles, encompassing 151 cases from 22 countries, with China and India collectively contributing 67.55% of cases. MOT predominantly affected individuals aged 0–30 years (58.94%). Pulmonary tuberculosis was evident in 16.55% of cases, with spinal involvement prevalent (57.62%). Significant differences were noted in trunk, spine, thoracic, and lumbar vertebrae involvement, as well as type I lesions across age groups, increasing with age. Moreover, significant differences were observed in upper limb bone involvement and type II lesions across age groups, decreasing with age. Gender differences were not significant. MOT primarily manifests in China and India, predominantly among younger individuals, indicating age-related variations in osteoarticular involvement. Enhanced clinical awareness is crucial for accurate MOT diagnosis, mitigating missed diagnoses and misdiagnoses.

Cause-of-death mortality forecasting, a key topic in public health and actuarial science, is a challenging task due to the difficulty of modeling that accounts for dependencies among causes of death. While several cause-of-death mortality models have been proposed to address this difficulty, little attention has been paid to improving their predictive performance. Recently, purely data-driven approaches using tensor decomposition methods have been introduced to cause-of-death mortality modeling, demonstrating strong out-of-sample predictive performance compared to existing models. However, these methods have difficulties in the interpretability of multi-rank tensor components to achieve strong predictive performance. In response, we propose a novel tensor-based cause-of-death mortality model by replacing the tensor decomposition with a convolutional autoencoder with a one-dimensional latent layer that provides a Lee-Carter-like time-series factor; the model also provides the age sensitivity of cause-specific log mortality to the time-series factor. Due to the representational capability of the neural network, our model achieves better predictive performance compared to the existing tensor decomposition-based models, despite the simplified latent layer for model interpretability.

Vibration-based structural health monitoring (SHM) of (large) infrastructure through operational modal analysis (OMA) is a commonly adopted strategy. This is typically a four-step process, comprising estimation, tracking, data normalization, and decision-making. These steps are essential to ensure structural modes are correctly identified, and results are normalized for environmental and operational variability (EOV). Other challenges, such as nonstructural modes in the OMA, for example, rotor harmonics in (offshore) wind turbines (OWTs), further complicate the process. Typically, these four steps are considered independently, making the method simple and robust, but rather limited in challenging applications, such as OWTs. Therefore, this study aims to combine tracking, data normalization, and decision-making through a single machine learning (ML) model. The presented SHM framework starts by identifying a “healthy” training dataset, representative of all relevant EOV, for all structural modes. Subsequently, operational and weather data are used for feature selection and a comparative analysis of ML models, leading to the selection of tree-based learners for natural frequency prediction. Uncertainty quantification (UQ) is introduced to identify out-of-distribution instances, crucial to guarantee low modeling error and ensure only high-fidelity structural modes are tracked. This study uses virtual ensembles for UQ through the variance between multiple truncated submodel predictions. Practical application to monopile-supported OWT data demonstrates the tracking abilities, separating structural modes from rotor dynamics. Control charts show improved decision-making compared to traditional reference-based methods. A synthetic dataset further confirms the approach’s robustness in identifying relevant natural frequency shifts. This study presents a comprehensive data-driven approach for vibration-based SHM.

Let $\mathbb{P}_\kappa(n)$ be the probability that n points $z_1,\ldots,z_n$ picked uniformly and independently in $\mathfrak{C}_\kappa$, a regular $\kappa$-gon with area 1, are in convex position, that is, form the vertex set of a convex polygon. In this paper, we compute $\mathbb{P}_\kappa(n)$ up to asymptotic equivalence, as $n\to+\infty$, for all $\kappa\geq 3$, which improves on a famous result of Bárány (Ann. Prob. 27, 1999). The second purpose of this paper is to establish a limit theorem which describes the fluctuations around the limit shape of an n-tuple of points in convex position when $n\to+\infty$. Finally, we give an asymptotically exact algorithm for the random generation of $z_1,\ldots,z_n$, conditioned to be in convex position in $\mathfrak{C}_\kappa$.

Understanding and forecasting mortality by cause is an essential branch of actuarial science, with wide-ranging implications for decision-makers in public policy and industry. To accurately capture trends in cause-specific mortality, it is critical to consider dependencies between causes of death and produce forecasts by age and cause coherent with aggregate mortality forecasts. One way to achieve these aims is to model cause-specific deaths using compositional data analysis (CODA), treating the density of deaths by age and cause as a set of dependent, nonnegative values that sum to one. A major drawback of standard CODA methods is the challenge of zero values, which frequently occur in cause-of-death mortality modeling. Thus, we propose using a compositional power transformation, the $\alpha$-transformation, to model cause-specific life-table death counts. The $\alpha$-transformation offers a statistically rigorous approach to handling zero value subgroups in CODA compared to ad hoc techniques: adding an arbitrarily small amount. We illustrate the $\alpha$-transformation in England and Wales and US death counts by cause from the Human Cause-of-Death database, for cardiovascular-related causes of death. The results demonstrate the $\alpha$-transformation improves forecast accuracy of cause-specific life-table death counts compared with log-ratio-based CODA transformations. The forecasts suggest declines in the proportions of deaths from major cardiovascular causes (myocardial infarction and other ischemic heart diseases).

Klebsiella pneumoniae are opportunistic pathogens which can cause mastitis in dairy cattle. K. pneumoniae mastitis often has a poor cure rate and can lead to the development of chronic infection, which has an impact on both health and production. However, there are few studies which aim to fully characterize K. pneumoniae by whole-genome sequencing from bovine mastitis cases. Here, K. pneumoniae isolates associated with mastitis in dairy cattle were identified using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) and whole-genome sequencing. Furthermore, whole-genome sequence data were used for phylogenetic analyses and both virulence and antimicrobial resistance (AMR) prediction, in parallel with phenotypic AMR testing. Forty-two isolates identified as K. pneumoniae were subject to whole-genome sequencing, with 31 multi-locus sequence types being observed, suggesting the source of these isolates was likely environmental. Isolates were examined for key virulence determinants encoding acquired siderophores, colibactin, and hypermucoidy. The majority of these were absent, except for ybST (encoding yersiniabactin) which was present in six isolates. Across the dataset, there were notable levels of phenotypic AMR against streptomycin (26.2%) and tetracycline (19%), and intermediate susceptibility to cephalexin (26.2%) and neomycin (21.4%). Of importance was the detection of two ESBL-producing isolates, which demonstrated multi-drug resistance to amoxicillin-clavulanic acid, streptomycin, tetracycline, cefotaxime, cephalexin, and cefquinome.

Displacement continues to increase at a global scale and is increasingly happening in complex, multicrisis settings, leading to more complex and deeper humanitarian needs. Humanitarian needs are therefore increasingly outgrowing the available humanitarian funding. Thus, responding to vulnerabilities before disaster strikes is crucial but anticipatory action is contingent on the ability to accurately forecast what will happen in the future. Forecasting and contingency planning are not new in the humanitarian sector, where scenario-building continues to be an exercise conducted in most humanitarian operations to strategically plan for coming events. However, the accuracy of these exercises remains limited. To address this challenge and work with the objective of providing the humanitarian sector with more accurate forecasts to enhance the protection of vulnerable groups, the Danish Refugee Council has already developed several machine learning models. The Anticipatory Humanitarian Action for Displacement uses machine learning to forecast displacement in subdistricts in the Liptako-Gourma region in Sahel, covering Burkina Faso, Mali, and Niger. The model is mainly built on data related to conflict, food insecurity, vegetation health, and the prevalence of underweight to forecast displacement. In this article, we will detail how the model works, the accuracy and limitations of the model, and how we are translating the forecasts into action by using them for anticipatory action in South Sudan and Burkina Faso, including concrete examples of activities that can be implemented ahead of displacement in the place of origin, along routes and in place of destination.

Gut and Stadmüller (2021, 2022) initiated the study of the elephant random walk with limited memory. Aguech and El Machkouri (2024) published a paper in which they discuss an extension of the results by Gut and Stadtmüller (2022) for an ‘increasing memory’ version of the elephant random walk without stops. Here we present a formal definition of the process that was hinted at by Gut and Stadtmüller. This definition is based on the triangular array setting. We give a positive answer to the open problem in Gut and Stadtmüller (2022) for the elephant random walk, possibly with stops. We also obtain the central limit theorem for the supercritical case of this model.

With climate change, the geographic distribution of some VBDs has expanded, highlighting the need for adaptation, and managing the risks associated with emergence in new areas. We conducted a questionnaire survey on the knowledge, attitudes, and practices (KAP) about vector-borne diseases (VBDs) among sample of Finnish residents. The questions were scored and the level of KAP was determined based on scoring as poor, fair, good, or excellent. Binary logistic regression analysis was used to evaluate the associations of different KAP levels with sex, age, education, and possible previous VPD infection. We received 491/1995 (25%) responses across the country and detected generally good knowledge, but only fair practices towards VBDs. Sex and age of the respondents were most often significantly associated with the level of KAP (P > 0.05). Despite the generally good knowledge, we detected major gaps, especially regarding the distinction of tick-borne encephalitis and Lyme borreliosis (LB), risk of disease, and protective measures. Additionally, many respondents thought the vaccination protects against LB or tick bites. This calls for awareness raising on disease risk and prevention measures. With increasing cases and the effects of climate change, surveillance of VBDs communication to the general public should be strengthened.

Surveillance of antimicrobial consumption (AMC) is essential to anticipate and inform policies and public health decisions to prevent and/or contain antimicrobial resistance (AMR). This manuscript shares the experience on AMC data collection in Latin American & Caribbean (LAC). The WHO GLASS-AMC methodology for AMC surveillance was used for data registration during the period 2019–2022. Focal points belonging to each country were contacted and trained for AMC source of information detection, managing registration tools, and data analysis. Thirteen countries were enrolled with significant heterogeneity in the AMC results (range 2.55–36.26 DID-AMC). This experience reflects the heterogeneity of realities in LAC countries; how each one of the nations selected the best sources to collect AMC data, which were the main problems in applying the WHO-AMC collection tool, and the approach that each country gave to the analysis of its data. Finally, some examples are provided on the use of AMC information in making the best decision-making related to AMR control policies at the national level.

Hand, foot and mouth disease (HFMD) is a contagious communicable disease, with a high incidence in children aged under 10 years. It is a mainly self-limiting disease but can also cause serious neurological or cardiopulmonary complications in some cases, which can lead to death. Little is known about the burden of HMFD on primary care health care services in the UK. The aim of this work was to describe trends in general practitioner (GP) consultations for HFMD in England from January 2017 to December 2022 using a syndromic surveillance network of GPs. Daily GP consultations for HFMD in England were extracted from 1 January 2017 to 31 December 2022. Mean weekly consultation rates per 100,000 population and 95% confidence intervals (CI) were calculated. Consultation rates and rate ratios (RR) were calculated by age group and sex. During the study period, the mean weekly consultation rate for HFMD (per 100,000 registered GP patients) was 1.53 (range of 0.27 to 2.47). In England, children aged 1–4 years old accounted for the largest affected population followed by children <1 years old. We observed a seasonal pattern of HFMD incidence during the non-COVID years, with a seasonal peak of mean weekly rates between months of September and December. HFMD is typically diagnosed clinically rather than through laboratory sampling. Therefore, the ability to look at the daily HFMD consultation rates provides an excellent epidemiological overview on disease trends. The use of a novel GP-in-hours surveillance system allowed a unique epidemiological insight into the recent trends of general practitioner consultations for HFMD. We demonstrate a male predominance of cases, the impact of the non-pharmaceutical interventions during the COVID-19 pandemic, and a change in the week in which the peak number of cases happens post-pandemic.

Hepatitis E virus (HEV) is one of the most common causes of viral hepatitis. We examined HEV seroprevalence and associations of sociodemographic and lifestyle characteristics with HEV immunoglobulin G (IgG) seropositivity in the Arab population. A cross-sectional single-centre study was conducted among adults in the Nazareth area during 2022. Blood samples were tested using the Altona Real-Star HEV-RNA and the Wantai IgG assays. Data on sociodemographics, health status, and lifestyle were collected using structured questionnaires.

Overall, 490 individuals (55.9% males) aged 18 − 96 (mean = 53.2, SD = 28.0) were enrolled. HEV IgG seropositivity was estimated at 21.4% (95% CI 17.9–25.3). No samples were HEV-RNA positive. The correlates of HEV IgG seropositivity were older age (prevalence ratio (PR) 1.07, 95% CI 1.04–1.09, P < 0.001) and consuming beef frequently (PR 2.81, 95% CI 1.40–5.63, P = 0.003). No associations were found between Arab religious groups (Muslim, Christian or Druze, representing different socioeconomic status and dietary habits) or pork consumption and HEV IgG seropositivity. In conclusion, HEV seropositivity was high in the Arab population, and assessing HEV in Ruminants, particularly cows, is warranted.

The growing concern over cyber risk has become a pivotal issue in the business world. Firms can mitigate this risk through two primary strategies: investing in cybersecurity practices and purchasing cyber insurance. Cybersecurity investments reduce the compromise probability, while cyber insurance transfers potential losses to insurers. This study employs a network model for the spread of infection among interconnected firms and investigates how each firm’s decisions impact each other. We analyze a non-cooperative game in which each firm aims to optimize its objective function through choices of cybersecurity level and insurance coverage ratio. We find that each firm’s cybersecurity investment and insurance purchase are strategic complements. Within this game, we derive sufficient conditions for the existence and uniqueness of Nash equilibrium and demonstrate its inefficiency. These theoretical results form the foundation for our numerical studies, allowing us compute firms’ equilibrium decisions on cybersecurity investments and insurance purchases across various network structures. The numerical results shed light on the impact of network structure on equilibrium decisions and explore how varying insurance premiums influence firms’ cybersecurity investments.

The rise in interest rates globally in 2022–23 led to improved scheme funding for many defined-benefit pension schemes. Many schemes in the UK now find themselves closer to, or at, a fully funded position on a low-risk basis (annuity buyout or self-sufficiency). Finishing the journey while managing the risk of losses on that journey is highly desirable, but may be difficult to achieve in practice.

However many schemes are not yet sufficiently funded to buy out liabilities in full with an insurer. Others may not wish to, and many who can afford to do so are not yet able to for investment reasons (such as holding illiquid assets) or operational reasons (such as the time needed to resolve member data issues). For schemes that instead look to adopt self-sufficient ongoing management with low dependency on the sponsoring employer, this may be difficult to maintain in practice. In short, there remains a risk that benefits will not be secured in full, which with hindsight could have been avoided.

The addition of capital to pension scheme assets has long been deployed to enhance the security of member benefits e.g., capital from insurers in the case of a buyout or capital from sponsors in the form of contingent assets.

More recently, providers have developed a diverse set of arrangements that draw on external capital to aid trustees and corporates to meet scheme funding ambitions. Capital Backed Funding Arrangements (“CBFA”) are in this context an additional tool in the trustee toolkit for delivering funding strategies.

This paper focusses on the UK-defined benefit market but the dynamics are applicable to other jurisdictions, with CBFAs being developed for wider markets (e.g., Ireland).

In this paper we:

• survey the current scheme funding landscape and consider the need in this environment for arrangements to support scheme funding journeys to deliver benefits in full

• summarise the key features of arrangements in the market that may support these objectives

• set out considerations for trustees and sponsoring companies when assessing these arrangements.

The aim of this paper is educational – to increase awareness of the key issues and potential solutions. Professional advice will always be required prior to any transaction. We welcome feedback from readers on further material that would be beneficial to support consideration of these arrangements.