Well-posedness is established for multi-dimensional mean-field stochastic Volterra equations with Lipschitz-continuous coefficients, allowing for singular kernels as well as for one-dimensional mean-field stochastic Volterra equations with Hölder-continuous diffusion coefficients and sufficiently regular kernels. In these different settings, quantitative, pointwise propagation of chaos results are derived for the associated Volterra-type interacting particle systems.

We describe a prolonged outbreak of Salmonella enterica serotype Poona (S. Poona) sequence type (ST) 308, which comprised 13 cases occurring intermittently in North West England between 2016 and 2021. Whole genome sequencing (WGS) results indicated potential exposure to a single source but a lack of good quality data from routine surveillance questionnaires initially made it challenging to identify the cause. Continuing identification of cases in young children in a small geographical area prompted further public health actions, including trawling interviews which identified that ten cases attended the same nursery. As part of enhanced case finding in this nursery, childcare staff were asked to submit faecal samples. One asymptomatic staff member was positive for S. Poona and had worked at another nursery, attended at the time by the first S. Poona child case in this outbreak. Further investigations revealed that the case had previously undergone a cholecystectomy. We report an outbreak caused by persistent carriage and shedding of S. Poona in an asymptomatic individual working with vulnerable groups, which necessitated introduction of risk management measures similar to that for Typhoidal Salmonella. We also demonstrate the utility of combining epidemiological and WGS data in the public health response to Salmonella outbreaks.

In low-prevalence settings, the epidemiological yield of screening strategies for controlling vancomycin-resistant enterococci (VRE) outbreaks has not been fully established. We retrospectively analysed a prolonged VRE outbreak at a 536-bed tertiary-care hospital in Japan from 2010 to 2021 to evaluate sequential screening strategies across epidemic phases and to identify risk factors for VRE acquisition. Hospital-wide, admission-based, antimicrobial exposure-based, passive, and haemodialysis-targeted screening strategies were implemented over time. Screening yields were compared longitudinally, and a retrospective case–control study was performed using data from the initial hospital-wide screening phase. Molecular epidemiology was assessed by pulsed-field gel electrophoresis (PFGE). In total, 169 VRE-positive patients were identified, including seven infections and 162 asymptomatic carriers. Hospital-wide screening in the early epidemic phase showed the highest positivity rate (0.91%), whereas targeted strategies consistently yielded lower rates (0.09–0.34%). Haemodialysis, specific oral care practices, and prior exposure to carbapenems, glycopeptides, and piperacillin/tazobactam were independently associated with VRE acquisition. PFGE revealed substantial genetic diversity, suggesting sustained nosocomial transmission with repeated introductions. Early broad-based screening may be epidemiologically efficient in the initial phase of VRE outbreaks in low-prevalence settings, followed by adaptive refinement for long-term control.

The COVID-19 pandemic has highlighted limitations in case-based surveillance due to inconsistent testing and reporting. Wastewater-based epidemiology (WBE) has emerged as a complementary surveillance approach for tracking SARS-CoV-2 transmission, capturing both symptomatic and asymptomatic infections. The aim of this study was to evaluate the effectiveness of WBE in estimating the effective reproduction number ($ {R}_t $) of SARS-CoV-2 in Georgia, USA. We used a Generalized Linear Mixed Model (GLMM) to analyse viral concentration data from multiple wastewater treatment plants (WWTPs) collected between 1 June 2022 and 15 December 2022. After controlling for flow rates and site-level heterogeneity, model residuals were transformed into a non-negative incidence-like series used to estimate wastewater-based $ {R}_t $. Wastewater-based $ {R}_t $was compared with case-based $ {R}_t $estimates using Spearman correlation. The two $ {R}_t $ estimates showed concordant temporal patterns across most sites, with stronger correlations in areas with higher case counts (Spearman correlations ranging from 0.39 to 0.84, $ p<0.001 $). Wastewater-based $ {R}_t $ tracked increases and decreases in transmission over similar time scales as case-based estimates, while exhibiting reduced sensitivity to short-term changes in clinical testing and reporting behaviour. These findings suggest that WBE can support estimation of transmission trends and complement traditional case-based surveillance for public health monitoring.

Let $X_1,\ldots, X_n$ be independent integers distributed uniformly on [M], $M\ge 2$. A partition S of [n] into $\nu$ non-empty subsets $S_1,\ldots, S_{\nu}$ is called perfect if all $\nu$ values $\sum_{j\in S_{\alpha}}X_j$ are equal. For a perfect partition to exist, $\sum_j X_j$ has to be divisible by $\nu$. In 2001, for $\nu=2$, Christian Borgs, Jennifer Chayes, and the author proved that, conditioned on $\sum_j X_j$ being even, with high probability a perfect partition exists if $\kappa\;:\!=\; \lim {{n}/{\log M}}>{{1}/{\log 2}}$, and that with high probability no perfect partition exists if $\kappa<{{1}/{\log 2}}$. Responding to a question by George Varghese, we prove that for $\nu\ge 3$ with high probability no perfect partition exists if $\kappa<{{2}/{\log \nu}}$, which is twice as large as the naive threshold $1/\log 3$ for $\nu=3$. We identify the range of $\kappa$ where the expected number of perfect partitions is exponentially high. We show that for $\kappa> {{2(\nu-1)}/{\log[(1-2\nu^{-2})^{-1}]}}$ the total number of perfect partitions is exponentially high with probability $\gtrsim (1+\nu^2)^{-1}$, i.e. below $1/\nu$, the limiting probability that $\sum_j X_j$ is divisible by $\nu$.

This article proposes sequential randomized tests to locate the presence of jumps on the paths of efficient asset prices in a continuous-time model. The randomized statistics are generated by artificially adding randomness to the robust approximations of the locally averaged returns of the efficient price. In the case of finite activity jumps, we derive the asymptotic distribution of the maximum of all the local statistics unaffected by jumps, which makes it feasible to control the limiting probability of the global type I error and demonstrate the power of the test. We also present the theoretical results to illustrate the behaviors of the test statistics in the presence of infinite activity jumps. Simulation studies indicate the favorable performance of the proposed test in finite samples, and we also apply the test to the stock price data of Apple and Microsoft.

Over the past years, the concept of open research data (ORD) has gained traction as part of broader Open Science initiatives. The benefits of ORD, such as increased cost-effectiveness, transparency, and visibility, are well documented. However, researchers face barriers, which may be perceived rather than real, hindering the adoption of ORD practices. To address this challenge, we propose using ORD support services as sustainable enablers to stimulate cultural change around ORD. We engaged stakeholders across the University of Zurich and the Swiss ORD community, differentiating between researchers and ORD experts, to identify which services would best serve as sustainable enablers. After defining ORD support services and categorizing them into six key areas, we conducted surveys and interviews to gather insights on service preferences and barriers to ORD adoption. Among researchers, we identified a trend toward simpler and lower-resource services, highlighting the need for user-friendly and easily accessible support. ORD experts emphasized the importance of professional data stewardship, robust research data management (RDM) practices, and customized support to address discipline-specific needs. By combining survey and interview results, we provide a detailed overview of stakeholders’ ideas and suggestions for each proposed support area. Our study results in recommendations for academic institutions aiming to stimulate a cultural shift toward ORD. By focusing on findable, accessible, and user-friendly services, equipping researchers with fundamental RDM skills, and professionalizing data stewardship to provide customized support, institutions can foster the adoption of ORD practices. Ultimately, these measures can enhance the impact and reproducibility of scientific research.

Marco Lippi was born in Rome in 1943. An indefatigable and inspiring pedagogue, he has been teaching mathematics, economics, the history of economic thought, and econometrics to generations of students at the Universities of Perugia, Rome (La Sapienza, Tor Vergata, and LUISS), Modena, the Scuola Superiore Sant’Anna in Pisa, and the European Center for Advanced Research in Economics and Statistics (ECARES) in Brussels. As a fellow of the Einaudi Institute for Economics and Finance (EIEF), he still teaches, with the indomitable enthusiasm that has become legendary among his students and colleagues, Master and Ph.D. courses offered by this renowned Roman institution.

Influenza increases the risk of secondary diseases, but other than pneumonia, many of these diseases (e.g., sinusitis, otitis media, acute myocardial infarctions) are not consistently considered in estimates of influenza burden. We used the Merative Marketscan database (2001–2019) and time-series methods to identify age-specific categories of diseases that were temporally associated with patterns of influenza activity. Next, we estimated hypothetical reductions in the incidence and costs of these diseases if influenza incidence were reduced. Of 282 different disease categories evaluated, 23 (8.2%) were strongly associated with influenza (e.g., acute bronchitis, otitis media, myocardial infarctions, sinusitis, COPD) in at least one age group. For example, we estimated a 20% decrease in peak influenza incidence could decrease acute bronchitis cases by 6.5% and pneumonia cases by 5.3%, corresponding to a $1.6 billion reduction in healthcare costs. Excluding secondary diseases associated with influenza may lead to substantial underestimates of influenza’s burden and costs.

We study the asymptotic properties, in the weak sense, of regenerative processes and Markov renewal processes. For the latter, we derive both renewal-type results, also concerning the related counting process, and ergodic-type results, including the so-called $\varphi$-mixing property. This theoretical framework permits us to study the weak limit of the integral of a semi-Markov process, which can be interpreted as the position of a particle moving with finite velocities, taken for a random time according to the Markov renewal process underlying the semi-Markov one. Under mild conditions, we obtain the weak convergence to scaled Brownian motion. As a particular case, this result establishes the weak convergence of the classical generalized telegraph process.

This study assessed whether systematically using finetype data in national surveillance of invasive meningococcal disease serogroup B (IMD-B) in the Netherlands could improve cluster detection in order to prevent further cases through public health actions. We analysed 2005–2023 data, including 1,642 IMD-B cases with complete finetype and municipality information (95%; N = 1729). Using a generalized linear model, we calculated expected baselines for each finetype, including temporal trends. Using SaTScan™, we applied Poisson scan-statistics with a 365-day window to identify spatiotemporal clusters, comparing results to epidemiological and core-genome multi-locus sequence typing (cgMLST) data. Of 453 finetypes, 308 (68%) occurred once; diversity was high (Gini-Simpson index 0.96). We identified 42 spatiotemporal clusters across 37 finetypes, comprising 132 cases (8%), with a median cluster size of two (range 2–21) and duration of 45 days (range 6–356). Between zero and five clusters were detected yearly. Among 18 cases with known epidemiological links, 14 (78%) were within detected spatiotemporal clusters. CgMLST data from eight clusters supported some clusters but rejected others. Systematic cluster detection using finetype could reveal missed epidemiological links, potentially enabling public health action. However, its impact in preventing additional IMD-B cases is likely limited due to small cluster sizes, though meaningful given the severity of IMD-B. Simple finetype mapping may provide a resource-efficient alternative to SaTScan™.

Construction safety inspections typically involve a human inspector identifying safety concerns on-site. With the rise of powerful vision language models (VLMs), researchers are exploring their use for tasks such as detecting safety rule violations from on-site images. However, there is a lack of open datasets to comprehensively evaluate and further fine-tune VLMs in construction safety inspection. Current applications of VLMs use small, supervised datasets, limiting their applicability in tasks they are not directly trained for. In this article, we propose the ConstructionSite 10 k, featuring 10,000 construction site images with annotations for three inter-connected tasks, including image captioning, safety rule violation visual question answering (VQA), and construction element visual grounding. Our subsequent evaluation of current state-of-the-art large pre-trained VLMs shows notable generalization abilities in zero-shot and few-shot settings, while additional training is needed to make them applicable to actual construction sites. This dataset allows researchers to train and evaluate their own VLMs with new architectures and techniques, providing a valuable benchmark for construction safety inspection.

Global mortality rates continue to decline, and life expectancy continues its upward trend. Besides mortality levels, policymakers and providers of financial and health services would also be interested in disability prevalence and its potential future trajectories. The length of time in good health versus the duration with major disabilities or long-term illnesses has significant financial implications for both individuals and society. In this paper, we develop Bayesian common factor models to analyse Australian age- and sex-specific disability prevalence rates. In particular, there are one or more common factors shared by both sexes, as well as specific factors for each sex. Retirement villages are purpose-built residential complexes designed for relatively healthy retirees to live as neighbours and share a communal lifestyle. We apply the model forecasts and simulations to valuate a typical retirement village contract. The cost of this accommodation service is determined by the resident’s total length of stay, which can be estimated using forecasted and simulated disability prevalence rates and mortality rates from our proposed models.

A stochastic model for the spread of an SIR (susceptible $\to$ infective $\to$ removed) epidemic is considered. Infectives have independent and identically distributed infectivity profiles, which describe their infectiousness as a function of time since infection. The individual-to-individual infection rate depends also on the number of susceptibles present in the population. Exact results are derived for the distribution of statistics defined on the final outcome of the epidemic, including its final size. These are proved by using a generalisation of a Sellke construction to show that the distribution of the final outcome of the epidemic is the same as that of an associated discrete-time epidemic process, in which infectives are considered one at a time, and exploiting connection with death processes to analyse the final outcome of the latter. The results generalise easily to multipopulation epidemics.

In this paper we propose a new efficient algorithm to compute the value function for zero-sum stopping games featuring two players with opposing interests. This can be seen as a game version of the ‘forward algorithm’ for (one-player) optimal stopping problems, first introduced by Irle (2006) for discrete-time Markov chains and later revisited by Miclo and Villeneuve (2021) for continuous-time Markov processes on general state spaces. This paper focuses on a game driven by a homogeneous continuous-time Markov chain taking values in a finite state space and also discusses the number of iterations needed. Illustrated computational implementations for a few particular examples are also provided.

Federated Learning is a novel method of training machine learning models, pioneered by Google, aimed for use on smartphones. In contrast to traditional machine learning, where data is centralised and brought to the model, Federated Learning involves the algorithm being brought to the data, ensuring privacy is preserved. This paper will demonstrate how insurance companies in a market could use this technique to build a claims frequency neural network prediction model collectively by combining and using all of their customer data, without actually sharing or compromising any sensitive information with each other. A simulated car insurance market with 10 players was created using the freMTPL2freq dataset. It was found that if all insurers were permitted to share their confidential data with each other, they could collectively build a model that achieved 5.57% of exposure weighted Poisson Deviance Explained (% PDE) on an unseen sample. However, if they are not permitted to share their customer data, none of them can achieve more than 3.82% exposure weighted PDE on the same unseen sample. With Federated Learning, they can retain all of their customer data privately and construct a model that achieves a similar level of accuracy to that achieved by centralising all the data for model training, reaching 5.34% exposure weighted PDE on the same unseen sample.

We study a stochastic control problem where the underlying process follows a spectrally negative Lévy process. A controller can continuously increase the process but only decrease it at independent Poisson arrival times. We show the optimality of the periodic–classical barrier strategy, which increases the process whenever it would fall below some lower barrier and decreases it whenever it is observed above a higher barrier. An optimal strategy and the value function are written semi-explicitly using scale functions. Numerical results are also given.

Owing to their innovative guarantee features, the popularity of variable annuities has gained significant traction as suitable retirement products in recent years. Amongst these guarantees, the guaranteed minimum income benefit (GMIB) stands out as an appealing rider that can be integrated into variable annuity contracts. In this research, we construct a comprehensive modelling framework that encompasses three sources of uncertainty, namely interest risk, mortality risk and investment risk, with the aim of valuing the GMIB. These risk factors are modelled stochastically whilst accounting for the interdependence between interest and mortality risks. The numéraire transformation technique is utilised in our approach, capitalising on the concepts of the forward and endowment-risk-adjusted measures. By considering two distinct settings of the Benefit Base functions, we derive an analytic solution for the GMIB. Our numerical findings demonstrate the superiority of our proposed methodology vis-á-vis the standard Monte Carlo simulation as a benchmark in terms of computational accuracy and efficiency, achieving a remarkable average improvement of 99% computing time reduction compared to the benchmark. Furthermore, we conduct an extensive sensitivity analysis to explore the levels of impact of various model parameters on the value of the GMIB.

Lifetime pension pools—also known as group self-annuitization plans, pooled annuity funds, and variable payment life annuities in the literature—offer retirees lifelong income by collectively managing mortality risk and adjusting benefits based on the investment performance and the mortality experience within the pool. The benefit structure hinges on two key design parameters: the investment policy and the hurdle rate. However, past research offers limited guidance on optimal asset allocation in such settings, often relying on overly simplistic strategies. Furthermore, the choice of hurdle rate has received virtually no attention in the literature. This study addresses this gap by jointly analyzing optimal hurdle rates and investment strategies using a dynamic programming approach that allows for varying degrees of risk aversion via a hyperbolic absolute risk aversion utility function. Our findings reveal that, as risk aversion increases, the model favours more conservative portfolios and lower hurdle rates; conversely, lower risk aversion supports riskier allocations and higher hurdle rates. The threshold parameter—which reflects the minimum acceptable level of consumption—plays a critical role in shaping the hurdle rate behaviour.