Given a fixed small graph H and a larger graph G, an H-factor is a collection of vertex-disjoint subgraphs $H'\subset G$, each isomorphic to H, that cover the vertices of G. If G is the complete graph $K_n$ equipped with independent U(0,1) edge weights, what is the lowest total weight of an H-factor? This problem has previously been considered for $H=K_2$, for example. We show that if H contains a cycle, then the minimum weight is sharply concentrated around some $L_n = \Theta(n^{1-1/d^*})$ (where $d^*$ is the maximum 1-density of any subgraph of H). Some of our results also hold for H-covers, where the copies of H are not required to be vertex-disjoint.

We consider the problem of identifying the parameters of a time-homogeneous bivariate Markov chain when only one of the two variables is observable. We show that, subject to conditions that we spell out, the transition kernel and the distribution of the initial condition are uniquely recoverable (up to an arbitrary relabelling of the state space of the latent variable) from the joint distribution of four (or more) consecutive time-series observations. The result is, therefore, applicable to (short) panel data as well as to (stationary) time series data.

Since the implementation of the Basel III Accord, expected shortfall (ES) has gained increasing attention from regulators as a complement to value-at-risk (VaR). The problem of elicitability for ES makes jointly modeling VaR and ES a popular method to study ES. In this article, we develop model averaging for joint VaR and ES regression models that selects the two weight vectors by minimizing a jackknife criterion. We show the large sample properties of the estimators under potential model misspecification with increasing dimension of parameters and the asymptotic optimality of the selected weights in the sense of minimizing the out-of-sample excess final prediction error. Simulation studies and three empirical analyses reveal good finite sample performance.

Spatial analysis and disease mapping have the potential to enhance understanding of tuberculosis (TB) dynamics, whose spatial dynamics may be complicated by the mix of short and long-range transmission and long latency periods. TB notifications in Nam Dinh Province for individuals aged 15 and older from 2013 to 2022 were analyzed with a variety of spatio-temporal methods. The study commenced with an analysis of spatial autocorrelation to identify clustering patterns, followed by the evaluation of several candidate Bayesian spatio-temporal models. These models varied from simple assessments of spatial heterogeneity to more complex configurations incorporating covariates and interactions. The findings highlighted a peak in the TB notification rate in 2017, with 98 cases per 100,000 population, followed by a sharp decline in 2021. Significant spatial autocorrelation at the commune level was detected over most of the 10-year period. The Bayesian model that best balanced goodness-of-fit and complexity indicated that TB trends were associated with poverty: each percentage point increase in the proportion of poor households was associated with a 1.3% increase in TB notifications, emphasizing a significant socioeconomic factor in TB transmission dynamics. The integration of local socioeconomic data with spatio-temporal analysis could further enhance our understanding of TB epidemiology.

Epidemic preparedness requires clear procedures and guidelines when a rapid risk assessment of a communicable disease threat is requested. In an evaluation of past risk assessments, we found that modifications to existing guidelines, such as the European Centre for Disease Prevention and Control’s (ECDC) rapid risk assessment operational tool, can strengthen this process. Therefore, we present alternative guidelines, in which we propose a unifying risk assessment terminology, describe how the risk question should be phrased by the risk manager, and redefine the probability and impact dimension of risk, including a methodology to express uncertainty. In our approach, probability refers to the probability of the introduction of a disease into a specified population in a specified time period, and impact combines the magnitude of spread and the severity of the health outcomes. Based on the collected evidence, both the probability of introduction and the magnitude of spread are quantitatively expressed by expert judgements, providing unambiguous risk assessment. We advise not to summarize the risk by a single qualification as ‘low’ or ‘high’. These alternative guidelines, which are illustrated by a hypothetical example on mpox, have been implemented at Statens Serum Institut in Denmark and can benefit other public health institutes.

The additive reserving model assumes the existence of volume measures such that the corresponding expected loss ratios are identical for all accident years. While classical literature assumes these volumes are known, in practice, accurate volume measures are often unavailable. The issue of uncertain volume measures in the additive model was addressed in a generalization of the loss ratio method published in 2018. The derivation is rather complex and the method is computationally intensive, especially for large loss development triangles. This paper introduces an alternative approach that leverages the well-established EM algorithm, significantly reducing computational requirements.

This study explores the relationship between alter centrality in various social domains and the perception of linguistic similarity within personal networks. Linguistic similarity perception is defined as the extent to which individuals perceive others to speak similarly to themselves. A survey of 126 college students and their social connections (n = 1035) from the French-speaking region of Switzerland was conducted. We applied logistic multilevel regressions to account for the hierarchical structure of dyadic ties. The results show that alters holding central positions in supportive networks are positively associated with perceived linguistic similarity, while those who are central in conflict networks show a negative association. The role of ambivalence yielded mixed results, with a positive and significant association emerging when ambivalence was linked to family members.

Digital twins are a new paradigm for our time, offering the possibility of interconnected virtual representations of the real world. The concept is very versatile and has been adopted by multiple communities of practice, policymakers, researchers, and innovators. A significant part of the digital twin paradigm is about interconnecting digital objects, many of which have previously not been combined. As a result, members of the newly forming digital twin community are often talking at cross-purposes, based on different starting points, assumptions, and cultural practices. These differences are due to the philosophical world-view adopted within specific communities. In this paper, we explore the philosophical context which underpins the digital twin concept. We offer the building blocks for a philosophical framework for digital twins, consisting of 21 principles that are intended to help facilitate their further development. Specifically, we argue that the philosophy of digital twins is fundamentally holistic and emergentist. We further argue that in order to enable emergent behaviors, digital twins should be designed to reconstruct the behavior of a physical twin by “dynamically assembling” multiple digital “components”. We also argue that digital twins naturally include aspects relating to the philosophy of artificial intelligence, including learning and exploitation of knowledge. We discuss the following four questions (i) What is the distinction between a model and a digital twin? (ii) What previously unseen results can we expect from a digital twin? (iii) How can emergent behaviours be predicted? (iv) How can we assess the existence and uniqueness of digital twin outputs?

This article establishes a data-driven modeling framework for lean hydrogen ($ {\mathrm{H}}_2 $)-air reaction rates for the Large Eddy Simulation (LES) of turbulent reactive flows. This is particularly challenging since $ {\mathrm{H}}_2 $ molecules diffuse much faster than heat, leading to large variations in burning rates, thermodiffusive instabilities at the subfilter scale, and complex turbulence-chemistry interactions. Our data-driven approach leverages a Convolutional Neural Network (CNN), trained to approximate filtered burning rates from emulated LES data. First, five different lean premixed turbulent $ {\mathrm{H}}_2 $-air flame Direct Numerical Simulations (DNSs) are computed each with a unique global equivalence ratio. Second, DNS snapshots are filtered and downsampled to emulate LES data. Third, a CNN is trained to approximate the filtered burning rates as a function of LES scalar quantities: progress variable, local equivalence ratio, and flame thickening due to filtering. Finally, the performances of the CNN model are assessed on test solutions never seen during training. The model retrieves burning rates with very high accuracy. It is also tested on two filter and downsampling parameters and two global equivalence ratios between those used during training. For these interpolation cases, the model approximates burning rates with low error even though the cases were not included in the training dataset. This a priori study shows that the proposed data-driven machine learning framework is able to address the challenge of modeling lean premixed $ {\mathrm{H}}_2 $-air burning rates. It paves the way for a new modeling paradigm for the simulation of carbon-free hydrogen combustion systems.

Artificial intelligence (AI) requires new ways of evaluating national technology use and strategy for African nations. We conduct a survey of existing “readiness” assessments both for general digital adoption and AI policy in particular. We conclude that existing global readiness assessments do not fully capture African states’ progress in AI readiness and lay the groundwork for how assessments can be better used for the African context. We consider the extent to which these indicators map to the African context and what these indicators miss in capturing African states’ on-the-ground work in meeting AI capability. Through case studies of four African nations of diverse geographic and economic dimensions, we identify nuances missed by global assessments and offer high-level policy considerations for how states can best improve their AI readiness standards and prepare their societies to capture the benefits of AI.

Relatively, recent work by Jeganathan (2008, Cowles Foundation Discussion Paper 1649) and Wang (2014, Econometric Theory, 30(3), 509–535) on generalized martingale central limit theorems (MCLTs) implicitly introduces a new class of instrument arrays that yield (mixed) Gaussian limit theory irrespective of the persistence level in the data. Motivated by these developments, we propose a new semiparametric method for estimation and inference in nonlinear predictive regressions with persistent predictors. The proposed method that we term chronologically trimmed least squares (CTLS) is comparable to the IVX method of Phillips and Magdalinos (2009, Econometric inference in the vicinity of unity. Mimeo, Singapore Management University) and yields conventional inference in regressions where the nature and extent of persistence in the data are uncertain. In terms of model generality, our contribution to the existing literature is twofold. First, our covariate model space allows for both nearly integrated (NI) and fractional processes (stationary and nonstationary) as a special case, while the vast majority of articles in this area only consider NI arrays. Second, we allow for nonlinear regression functions. The CTLS estimator is obtained by applying certain chronological trimming to the OLS instruments using appropriate kernel functions of time trend variables. In particular, the instruments under consideration are a generalized (averaged) version of those widely used for time-varying parameter (TVP) models. For the purposes of our analysis, we develop a novel asymptotic theory for sample averages of various processes weighted by such kernel functionals which is of independent interest and highly relevant to the TVP literature. Leveraging our nonlinear framework, we also provide an investigation on the effects of misbalancing on the predictability hypothesis. A new methodology is proposed to mitigate misbalancing effects. These methods are used for exploring the predictability of SP500 returns.

We aimed to identify risk factors related to COVID-19 reinfection in Hong Kong. We performed a population-based retrospective cohort study and reviewed case-based data on COVID-19 infections reported to the Centre for Health Protection from 8 January 2020 to 29 January 2023. We analyzed the epidemiology of COVID-19 infections and performed a Cox regression analysis. In this period, 3.32% (103,065/3,106,579) of COVID-19 infections recorded were classified as reinfection. Compared with primarily infected cases, a higher proportion of re-infected cases had chronic diseases (33.54% vs. 27.27%) and were residents of residential care homes (RCH) (10.99% vs. 1.41%). The time interval between the two episodes ranged from 31 to 1,050 days (median 282 days). Cox regression analysis of Omicron cases with the adjustment of covariates showed that being female (Hazard Ratio [HR] 1.12, 95% CI 1.11–1.13), chronic diseases (HR 1.18, 95% CI 1.16–1.20) and RCH residents (HR 6.78, 95% CI 6.61–6.95) were associated with reinfection, while additional vaccination after primary infection was protective (HR 0.80, 95% CI 0.79–0.81). Further analytical studies on the risk factors and protectors of COVID-19 reinfection are needed to guide targeted interventions.

Dientamoeba fragilis (D. fragilis) is an intestinal protozoan parasite with uncertain pathogenic potential. In the United States, data on D. fragilis in the era of molecular detection are limited. The aim of this retrospective chart review was to evaluate the epidemiology and clinical characteristics of D. fragilis cases identified using polymerase chain reaction assays between 2016 and 2024 at our academic medical centre located in Utah. We identified 28 unique cases with varying gastrointestinal symptomatology including diarrhoea, abdominal pain, nausea, vomiting, and bloating. Approximately half (52%) of patients with follow-up data demonstrated improvement in symptoms following initial treatment for D. fragilis. The overall prevalence of D. fragilis was low among those tested (0.6% positivity). Additional research, including case-control studies, is needed to better describe the etiologic role of D. fragilis.

This paper initiates the explicit study of face numbers of matroid polytopes and their computation. We prove that, for the large class of split matroid polytopes, their face numbers depend solely on the number of cyclic flats of each rank and size, together with information on the modular pairs of cyclic flats. We provide a formula which allows us to calculate $f$-vectors without the need of taking convex hulls or computing face lattices. We discuss the particular cases of sparse paving matroids and rank two matroids, which are of independent interest due to their appearances in other combinatorial and geometric settings.

We assessed the validity of serum total anti-nucleoprotein Immunoglobulin (N-antibodies) to identify SARS-CoV-2 (re)infections by estimating the persistence of N-antibody seropositivity and boosting following infection. From a prospective Dutch cohort study (VASCO), we included adult participants with ≥2 consecutive self-collected serum samples, 4–8 months apart, between May 2021–May 2023. Sample pairs were stratified by N-seropositivity of the first sample and by self-reported infection within the sampling interval. We calculated the proportions of participants with N-seroconversion and fold-increase (1.5, 2, 3, 4) of N-antibody concentration over time since infection and explored determinants. We included 67,632 sample pairs. Pairs with a seronegative first sample (70%) showed 89% N-seroconversion after reported infection and 11% when no infection was reported. In pairs with a seropositive first sample (30%), 82%–65% showed a 1.5- to 4-fold increase with a reported reinfection, and 19%–10% without a reported reinfection, respectively. After one year, 83% remained N-seropositive post-first infection and 93%–61% showed a 1.5-fold to 4-fold increase post-reinfection. Odds for seroconversion/fold increase were higher for symptomatic infections and Omicron infections. In the current era with limited antigen or PCR testing, N-serology can be validly used to detect SARS-CoV-2 (re)infections at least up to a year after infection, supporting the monitoring of COVID-19 burden and vaccine effectiveness.

Sexual and gender–based violence (SGBV) is a multifaceted, endemic, and nefarious phenomenon that remains poorly measured and understood, despite greater global awareness of the issue. While efforts to improve data collection methods have increased–including the implementation of the Demographic and Health Survey (DHS) in some countries–the lack of reliable SGBV data remains a significant challenge to developing targeted policy interventions and advocacy initiatives. Using a recent mixed–methods research project conducted by the authors in Sierra Leone as a case study, this paper discusses the current status of SGBV data, challenges faced, and potential research a pproaches.

Climate change exacerbates existing risks and vulnerabilities for people globally, and migration is a longstanding adaptation response to climate risk. The mechanisms through which climate change shapes human mobility are complex, however, and gaps in data and knowledge persist. In response to these gaps, the United Nations Development Programme’s (UNDP) Predictive Analytics, Human Mobility, and Urbanization Project employed a hybrid approach that combined predictive analytics with participatory foresight to explore climate change-related mobility in Pakistan and Viet Nam from 2020 to 2050. Focusing on Karachi and Ho Chi Minh City, the project estimated temporal and spatial mobility patterns under different climate change scenarios and evaluated the impact of such in-migration across key social, political, economic, and environmental domains. Findings indicate that net migration into these cities could significantly increase under extreme climate scenarios, highlighting both the complex spatial patterns of population change and the potential for anticipatory policies to mitigate these impacts. While extensive research exists on foresight methods and theory, process reflections are underrepresented. The innovative approach employed within this project offers valuable insights on foresight exercise design choices and their implications for effective stakeholder engagement, as well as the applicability and transferability of insights in support of policymaking. Beyond substantive findings, this paper offers a critical reflection on the methodological alignment of data-driven and participatory foresight with the aim of anticipatory policy ideation, seeking to contribute to the enhanced effectiveness of foresight practices.

The Flexible Farrington Algorithm (FFA) is widely used to detect infectious disease outbreaks at national/regional levels on a weekly basis. The rapid spread of SARS-CoV-2 alongside the speed at which diagnostic and public health interventions were introduced made the FFA of limited use. We describe how the methodology was adapted to provide a daily alert system to support local health protection teams (HPTs) working in the 316 English lower-tier local authorities. To minimize the impact of a rapidly changing epidemiological situation, the FFA was altered to use 8 weeks of data. The adapted algorithm was based on reported positive counts using total tests as an offset. Performance was assessed using the root mean square error (RMSE) over a period. Graphical reports were sent to local teams enabling targeted public health action. From 1 July 2020, results were routinely reported. Adaptions accommodated the impact on reporting because of changes in diagnostic strategy (introduction of lateral flow devices). RMSE values were relatively small compared to observed counts, increased during periods of increased reporting, and were relatively higher in the northern and western areas of the country. The exceedance reports were well received. This presentation should be considered as a successful proof-of-concept.