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.

In September 2023, the UK Health Security Agency’s (UKHSA) South West Health Protection Team received notification of patients with Pseudomonas aeruginosa perichondritis. All five cases had attended the same cosmetic piercing studio and a multi-disciplinary outbreak control investigation was subsequently initiated. An additional five cases attending the same studio were found. Seven of the ten cases had isolates available for Variable Number Tandem Repeat (VNTR) typing at the UKHSA national reference laboratory. Clinical and environmental P. aeruginosa isolates from the patients, handwash sink, tap water and throughout the wall-mounted point-of-use water heater (including outlet water) were indistinguishable by VNTR typing (11,6,2,2,1,3,6,3,11). No additional cases were identified after control measures were implemented, which included replacing the sink and point-of-use heater.

The lack of specific recommendations to control for P. aeruginosa within Council-adopted ear-piercing byelaws or national guidance means that a cosmetic piercing artist could inadvertently overlook the risks from this bacterial pathogen despite every intention to comply with the law and follow industry best practice advice. Clinicians, Environmental Health Officers and public health professionals should remain alert for single cases of Pseudomonas perichondritis infections associated with piercings and have a low threshold for notification to local health protection teams.

In this paper, we explore the optimal risk sharing problem in the context of peer-to-peer insurance. Using the criterion of minimizing total variance, we find that the optimal risk sharing strategy should take a linear form. Although linear risk sharing strategies have been examined in the literature, our study uncovers a significant finding: to minimize total variance, the linear strategy should be applied to the residual risks rather than the original risks, as commonly adopted in existing studies. By comparing with the existing models, we demonstrate the advantage of the linear residual risk sharing model in variance reduction and robustness. Furthermore, we develop and study a number of new models by incorporating some constraints, to reflect desirable properties required by the market. With those constraints, the optimal strategies turn out to favor market development, such as incentivize participation and guarantee fairness. A relevant model is considered at last, which establishes the connection among multiple optimization problems and provides insights on how to extend the models into a more general setup.

Forecasting international migration is a challenge that, despite its political and policy salience, has seen a limited success so far. In this proof-of-concept paper, we employ a range of macroeconomic data to represent different drivers of migration. We also take into account the relatively consistent set of migration policies within the European Common Market, with its constituent freedom of movement of labour. Using panel vector autoregressive (VAR) models for mixed-frequency data, we forecast migration in the short- and long-term horizons for 26 of the 32 countries within the Common Market. We demonstrate how the methodology can be used to assess the possible responses of other macroeconomic variables to unforeseen migration events—and vice versa. Our results indicate reasonable in-sample performance of migration forecasts, especially in the short term, although with varying levels of accuracy. They also underline the need for taking country-specific factors into account when constructing forecasting models, with different variables being important across the regions of Europe. For the longer term, the proposed methods, despite high prediction errors, can still be useful as tools for setting coherent migration scenarios and analysing responses to exogenous shocks.

This paper proposes an options pricing model that incorporates stochastic volatility, stochastic interest rates, and stochastic jump intensity. Market shocks are modeled using a jump process, with each jump governed by an asymmetric double-exponential distribution. The model also integrates a Markov regime-switching framework for volatility and the risk-free rate, allowing the market to alternate between a finite number of distinct economic states. A closed-form solution for European option pricing is derived. To demonstrate the significance of the proposed model, a comparison with various other models is performed, and the sensitivity of the various model parameters is illustrated.

We study a version of the stochastic control problem of minimizing the sum of running and controlling costs, where control opportunities are restricted to independent Poisson arrival times. Under a general setting driven by a general Lévy process, we show the optimality of a periodic barrier strategy, which moves the process upward to the barrier whenever it is observed to be below it. The convergence of the optimal solutions to those in the continuous-observation case is also shown.

Effective enforcement of laws and regulations hinges heavily on robust inspection policies. While data-driven approaches to testing the effectiveness of these policies are gaining popularity, they suffer significant drawbacks, particularly a lack of explainability and generalizability. This paper proposes an approach to crafting inspection policies that combines data-driven insights with behavioral theories to create an agent-based simulation model that we call a theory-infused phenomenological agent-based model (TIP-ABM). Moreover, this approach outlines a systematic process for combining theories and data to construct a phenomenological ABM, beginning with defining macro-level empirical phenomena. Illustrated through a case study of the Dutch inland shipping sector, the proposed methodology enhances explainability by illuminating inspectors’ tacit knowledge while iterating between statistical data and underlying theories. The broader generalizability of the proposed approach beyond the inland shipping context requires further research.

In this paper, we propose a network model to explain the implications of the pressure to share resources. Individuals use the network to establish social interactions that allow them to increase their income. They also use the network as a safety and to ask for assistance in case of need. The network is therefore a system characterized by social pressure to share and redistribute surplus of resources among members. The main result is that the potential redistributive pressure from other network members causes individuals to behave inefficiently. The number of social interactions used to employ workers displays a non-monotonic pattern with respect to the number of neighbors (degree): it increases for intermediate degree and decreases for high degree. Respect to a benchmark case without social pressure, individuals with few (many) network members interact more (less). Finally, we show that these predictions are consistent with the results obtained in a set of field experiments run in rural Tanzania.

Deep learning (DL) has become the most effective machine learning solution for addressing and accelerating complex problems in various fields, from computer vision and natural language processing to many more. Training well-generalized DL models requires large amounts of data which allows the model to learn the complexity of the task it is being trained to perform. Consequently, performance optimization of the deep-learning models is concentrated on complex architectures with a large number of tunable model parameters, in other words, model-centric techniques. To enable training such large models, significant effort has also gone into high-performance computing and big-data handling. However, adapting DL to tackle specialized domain-related data and problems in real-world settings presents unique challenges that model-centric techniques do not suffice to optimize. In this paper, we tackle the problem of developing DL models for seismic imaging using complex seismic data. We specifically address developing and deploying DL models for salt interpretation using seismic images. Most importantly, we discuss how looking beyond model-centric and leveraging data-centric strategies for optimization of DL model performance was crucial to significantly improve salt interpretation. This technique was also key in developing production quality, robust and generalized models.

A liquefied natural gas (LNG) facility often incorporates replicate liquefaction trains. The performance of equivalent units across trains, designed using common numerical models, might be expected to be similar. In this article, we discuss statistical analysis of real plant data to validate this assumption. Analysis of operational data for end flash vessels from a pair of replicate trains at an LNG facility indicates that one train produces 2.8%–6.4% more end flash gas than the other. We then develop statistical models for train operation, facilitating reduced flaring and hence a reduction of up to 45% in CO2 equivalent flaring emissions, noting that flaring emissions for a typical LNG facility account for ~4%–8% of the overall facility emissions. We recommend that operational data-driven models be considered generally to improve the performance of LNG facilities and reduce their CO2 footprint, particularly when replica units are present.

This study introduces an advanced reinforcement learning (RL)-based control strategy for heating, ventilation, and air conditioning (HVAC) systems, employing a soft actor-critic agent with a customized reward mechanism. This strategy integrates time-varying outdoor temperature-dependent weighting factors to dynamically balance thermal comfort and energy efficiency. Our methodology has undergone rigorous evaluation across two distinct test cases within the building optimization testing (BOPTEST) framework, an open-source virtual simulator equipped with standardized key performance indicators (KPIs) for performance assessment. Each test case is strategically selected to represent distinct building typologies, climatic conditions, and HVAC system complexities, ensuring a thorough evaluation of our method across diverse settings. The first test case is a heating-focused scenario in a residential setting. Here, we directly compare our method against four advanced control strategies: an optimized rule-based controller inherently provided by BOPTEST, two sophisticated RL-based strategies leveraging BOPTEST’s KPIs as reward references, and a model predictive control (MPC)-based approach specifically tailored for the test case. Our results indicate that our approach outperforms the rule-based and other RL-based strategies and achieves outcomes comparable to the MPC-based controller. The second scenario, a cooling-dominated environment in an office setting, further validates the versatility of our strategy under varying conditions. The consistent performance of our strategy across both scenarios underscores its potential as a robust tool for smart building management, adaptable to both residential and office environments under different climatic challenges.