System components usually attain marginal lifetimes with stochastic dependence in the context of load-sharing reliability structures. This study deals with the load-sharing parallel systems of two components. We prove that two marginal lifetimes are positively quadrant dependent when component lifetimes have continuous probability distributions, and such a stochastic dependence is upgraded to the total positive of order 2 in the setting of component lifetimes having an exponential distribution. In addition, we discuss how these findings shed light on related results for the load-sharing Ross model, the conditional residual lifetime, and the conditional inactivity time.

In this paper, we study the joint distribution of the forward and backward recurrence times in a delayed renewal process, as well as their marginal distributions. We obtain several exact results and bounds for these quantities. Some of these bounds are “general,” in the sense that the bounds are valid for any arbitrary distributions of the inter-arrival times, and some are based on aging properties of the distributions of the interarrival times of the renewals. Finally, several numerical examples are presented to illustrate the results.

In a recent paper, Juodis and Reese (2022, Journal of Business & Economic Statistics, 40, 1191–1203) (JR) show that the application of the CD test proposed by Pesaran (2004, General diagnostic tests for cross-sectional dependence in panels, CWPE 0435, Cambridge) to residuals from panels with latent factors results in over-rejection. They propose a randomized test statistic to correct for over-rejection, and add a screening component to achieve power. This article considers the same problem but from a different perspective and shows that the standard CD test remains valid if the latent factors are weak. A bias-corrected version, CD$^{\ast}$, is proposed which is shown to be asymptotically standard normal under the null of error cross-sectional independence which has power against network-type alternatives. This result is shown to hold for pure latent factor models as well as for panel regression models with latent factors. The case where the errors are serially correlated is also considered. Small sample properties of the CD$^{\ast}$ test are investigated by Monte Carlo experiments and are shown to have satisfactory small sample properties. In an empirical application, using the CD$^{\ast}$ test, it is shown that there remains spatial error dependence in a panel data model for real house price changes across 377 Metropolitan Statistical Areas in the United States, even after the effects of latent factors are filtered out.

We analysed weekly influenza A intensive care unit (ICU) or high dependency unit (HDU) admissions reported by age group and subtype by NHS trusts in England through mandatory surveillance during the 2023–2024 influenza season. We investigated whether subtype reporting varied with patient age group, NHS trust type and region. We estimated the subtype ratio and explored whether this estimate varied among subsets of trusts grouped by the regularity of subtype reporting. Our aim was to explore factors relating to subtype reporting and investigate how these affect subtype ratio estimates. 112 NHS trusts reported data, with 86 trusts reporting influenza A cases and 28 trusts reporting subtyped influenza A cases. The proportion of subtype reporting trusts varied with region and trust type, but not patient age group. The estimated ratio of influenza A(H1N1)pdm09 to influenza A(H3N2) was 3.13 (95% CI: 2.17, 4.51), indicating that influenza A(H1N1)pdm09 was dominant; this was approximately similar across levels of regularity of trust subtype reporting. The accuracy of subtype ratio estimates depends on the availability of influenza A subtype information and data representativeness. We identified low levels of subtype reporting, which likely limits early recognition of new influenza strains and informing of the prescription of antivirals in influenza outbreaks.

Following the pivotal work of Sevastyanov (1957), who considered branching processes with homogeneous Poisson immigration, much has been done to understand the behaviour of such processes under different types of branching and immigration mechanisms. Recently, the case where the times of immigration are generated by a non-homogeneous Poisson process has been considered in depth. In this work, we demonstrate how we can use the framework of point processes in order to go beyond the Poisson process. As an illustration, we show how to transfer techniques from the case of Poisson immigration to the case where it is spanned by a determinantal point process.

We employ an appropriate change of measure technique to offer a general result connecting a general form of the Gerber–Shiu function with the distribution of the deficit at ruin under the new (exponentially tilted) measure. Exploiting this result, we extract closed-form formulae for special forms of the Gerber–Shiu function assuming two cases of bivariate distributions that describe the dependence structure between claim sizes and inter-claim times. More specifically, initially, we employ the Downton–Moran bivariate exponential distribution, and we offer explicit formulae for cases of the Gerber–Shiu functions that include the time and the number of claims until ruin. In addition, we derive a closed formula for the defective discounted joint density of the number of claims until ruin, the deficit at ruin, and the time until ruin. The same is achieved for the joint density of the number of claims and the deficit at ruin. We further generalize these results by assuming that the inter-claim times and the claim sizes follow a Kibble–Moran bivariate Erlang distribution. Finally, we offer numerical examples in order to illustrate our main results.

We evaluate the effect of reciprocal trust within pairs of individuals—gauged by total potential earnings in a trust experiment—on the probability of relationship formation, in comparison with well-known determinants of social ties, such as time of exposure and homophily along demographic traits. We measured trust and trustworthiness for every individual in an incoming cohort of undergraduate students before they began interacting. Using relationship data sourced from surveys and campus entry/exit times between one month and two years after the trust experiment, we find that reciprocal trust is neither a statistically nor an economically significant factor in determining the students’ social networks. Instead, time of exposure, prior acquaintance, and other demographic characteristics play important and persistent roles in relationship formation.

We revisit the question of how to include parameter uncertainty in univariate parametric models of losses and loss ratios. We first review the statistical theory for including parameter uncertainty based on right Haar priors (RHPs), which applies to many commonly used models. In this theory, the prior is chosen in such a way as to ensure matching between predicted probabilities and the relative frequencies of future outcomes in repeated tests. This property is known as reliability, or calibration. We then test priors for including parameter uncertainty in a number of models not covered by RHP theory. For these models, we find priors that generate predictions that are more reliable than predictions based on maximum likelihood, although they are not perfectly reliable. We discuss numerical schemes that can be used to generate Bayesian predictions, including a novel use of asymptotic expansions, and we include an example in which we show the impact of including parameter uncertainty in the modeling of extreme hurricane losses. The tail loss estimates show material increases due to the inclusion of parameter uncertainty. Finally, we describe a new software library that makes it straightforward to apply the methods we describe.

Shiga toxin-producing Escherichia coli (STEC) are zoonotic, foodborne pathogens that cause outbreaks of infectious gastrointestinal disease, including haemolytic uraemic syndrome (HUS) which can be fatal. In November 2023, a foodborne outbreak of STEC serotype O26:H11 stx2a/eae, involving 40 cases (54% female and 76% aged 0–9 years old), including 19 children with HUS. Whole-genome sequencing analysis revealed the outbreak strain was multidrug resistant and likely originated from outside the United Kingdom. Epidemiological analysis showed greatest odds of exposure among cases for consumption of a dried fruit product, predominantly in multi-packs. Batch numbers of the packs consumed by cases were rarely available, and where recorded, other packs in the same the batch were unavailable for testing; therefore, targeted microbiological testing was not possible. Fruit for drying can become contaminated when the crop is exposed to irrigation water or rainwater run off containing animal faeces. For STEC, where detection of the causative agent in food is challenging, we recommend establishing multi-source weight of evidence frameworks that promote the application of epidemiological and food chain evidence for public health action and the expansion of global surveillance networks to enhance the detection of foodborne threats at home and abroad.

In France, HIV prevention measures including HIV testing, treatment, and uptake of pre-exposure prophylaxis (PrEP), have increased throughout the last decade. To analyse their impact, we performed a time series analysis of monthly HIV diagnoses reported via the national HIV surveillance database. In addition, we compared the timing of HIV promotional campaigns with monthly trends in HIV testing and PrEP initiation. From January 2012 to December 2022, new HIV diagnoses steadily decreased among men who have sex with men (MSM) born in France and heterosexuals born in France, whereas HIV diagnoses increased among MSM born abroad. HIV testing activity and PrEP use in France both steadily increased from 2014 to 2020, during which multiple campaigns targeting HIV testing and prevention occurred. The decline in HIV diagnoses among MSM born in France preceded the introduction of PrEP in 2016 and continued post-2016 without any acceleration in the rate of decline. Increased awareness of, access to and uptake of HIV prevention measures remain essential to progress towards HIV elimination in France, especially among MSM born abroad.

Panel data often contain stayers and slow movers. The literature proposes an estimator for the average partial effects (APEs) for this setting without a formal theory. The literature is also silent about inference in the presence of stayers and many slow movers. We contribute to this state of the art. First, we develop an asymptotic theory to guarantee that such an estimator is consistent in the presence of stayers and slow movers. Second, we propose its standard error. Third, we relax the existing assumption to allow for “many” slow movers. Fourth, we generalize the existing estimator. Fifth, we establish that this generalized estimator can achieve larger extents of bias reduction and hence faster convergence rates. Simulation studies demonstrate that the conventional 95% confidence interval covers the true value of the APE with 37%–93% frequencies whereas our proposed one achieves 93%–96% coverage frequencies. Using the U.S. Panel Study of Income Dynamics, we find that estimates of the marginal propensity to consume based on our generalized estimator remarkably differ in values from those of the existing estimators. Moreover, the generalized estimator achieves more than three times as small standard errors as those of the existing robust estimator.

Community-acquired pneumonia (CAP) remains an important public-health problem, and the COVID-19 pandemic and non-pharmaceutical interventions (NPIs) may have altered its burden. This study aimed to provide updated CAP burden among adults in Shanghai from 2016–2023.We analysed 61,230 participants aged 20–74 years from the Shanghai Suburban Adult Cohort and Biobank. CAP episodes were ascertained via ICD codes and clinical diagnoses. We calculated incidence rates before, during, and after NPIs, conducted subgroup analyses by age, sex, comorbidity and lifestyle. We used Poisson regression to compare stages, and Cox models to identify risk factors. The Overall CAP incidence was 42.1 per 1,000 person–years (95% CI 41.3–42.8). Incidence declined during NPIs (24.2/1,000 py) and rose after NPIs (95.9/1,000 py). The inpatient-to-outpatient ratio increased to 10.1% during NPIs and fell to 5.7% post–NPI. Among those without underlying conditions, rates were 40.1, 20.1 and 73.6/1,000 py before, during and after NPIs. Incidence was higher in participants ≥60 years and in those with multiple comorbidities, especially respiratory diseases. CAP burden temporarily fell during NPIs but resurged post–NPI, notably among high–risk groups. These findings highlight the need for targeted preventive strategies and continued CAP surveillance in the post-pandemic era.

Orientia tsutsugamushi, the causative agent of scrub typhus, is endemic to the Asia–Pacific region. In South Korea, the Boryong strain is considered dominant; however, nationwide phylogeographic distribution and genetic diversity based on clinical isolates remain incompletely characterized. In this study, 121 O. tsutsugamushi clinical isolates were collected from scrub typhus patients at 11 hospitals across South Korea between 2015 and 2024. Isolates were genotyped using 56-kDa gene sequencing and multilocus sequence typing (MLST) of seven housekeeping genes. Sequence analysis and phylogenetic reconstruction were performed using BLAST, PubMLST, BURST, MEGA11, DnaSP6, and R-based tools. Five 56-kDa genotypes were identified: Boryong (93.4%), Ikeda, Je-cheon, Young-worl, and Yeo-joo. MLST revealed 11 sequence types (STs), including five novel STs. While the Boryong strain and related STs were distributed nationwide, minor strains showed restricted distribution in northern regions. Several isolates sharing the same 56-kDa genotype exhibited different MLST STs, indicating possible recombination or local microevolution. This study provides the first nationwide MLST-based characterization of O. tsutsugamushi in South Korea and demonstrates the dominance of the Boryong strain alongside localized diversity. Our findings underscore the utility of MLST for higher-resolution typing and support the need for continued molecular surveillance to inform regional epidemiology and disease management.

This study examined whether coronavirus disease 2019 (COVID-19) infection experience enhances preventive behaviour (i.e., hand disinfection and mask-wearing), with risk perception acting as a mediating factor. The study included participants aged ≥18 years residing in Japan, enrolled in a 30-wave cohort study conducted from January 2020 to March 2024. Using propensity score matching, 135 pairs of participants with and without infection were extracted, adjusting for dread and unknown risk perception, preventive behaviours, sociopsychological variables, and individual attributes. Comparisons of risk perception and preventive behaviour were made between groups post-infection experience, and mediation analysis was conducted to test whether risk perception mediated the effect of infection experience on preventive behaviour. Following the infection experience, participants in the infection group reported significantly higher scores for one item of unknown risk perception and a greater proportion of mask-wearing. The indirect effect of infection experience on mask-wearing, mediated by the unknown risk perception item, was significant. COVID-19 infection experience increased perceptions of unknowable exposure, which in turn promoted mask-wearing behaviour. Incorporating insights from personal infection experiences into public health messaging may enhance risk perception and promote preventive behaviour among non-infected individuals, offering a novel approach to infection control at the population level.

A first-order Gaussian autoregressive model is considered. The exact finite-sample joint density of the minimal sufficient statistic is derived, for any value of the autoregressive parameter. This allows us to derive explicitly the exact density of the autocorrelation coefficient and its Studentized t-ratio, whose densities were available only in the asymptotic case and not for all values of the parameter and the statistic. This article also demonstrates how to solve a general problem in statistical distribution theory (well beyond the specific case of autoregressive models), that of inverting confluent characteristic functions in multiple variables.

Accidental escapes of pathogens from laboratories continue to cause outbreaks in the community today, posing significant risks to the general public, animal communities and the environment. These incidents, as well as the uncertainties surrounding the origins of the COVID-19 pandemic, highlight the need to consider unnatural origins as part of emerging outbreak surveillance and detection. Identifying recurring patterns and distinctive factors of laboratory-associated disease outbreaks can aid in successfully preventing and mitigating these occurrences. Seventy incidents of laboratory-associated leaks that led to outbreaks in the wider public have been reported (Supplementary Appendix S1). Seven renowned cases that have been comprehensively studied were selected for review: (i) 1955 Polio vaccine incident in western USA, (ii) 1977 H1N1 influenza virus re-emergence in China and the Soviet Union, (iii) 1979 Anthrax release in Sverdlovsk, Soviet Union, (iv) 1995 Venezuelan equine encephalitis epidemics in Venezuela and Colombia, (v) 2003–4 SARS-CoV-1 escapes from Singapore, Taiwan and China, (vi) 2007 Foot-and-Mouth disease virus outbreak in Pirbright, England and (vii) 2019 Brucella leak in Lanzhou, China. These outbreaks were selected because data on their geographical spread, genetics, phylogeny, epidemiological factors (including attack rates, infectious dose, time, location and season of spread) and governmental and institutional responses to the incidents had been previously analysed and published. Thematic analysis of these lines of evidence revealed seven recurring insights described in historically confirmed laboratory-associated outbreaks: unusual strain characteristics, peculiar clinical manifestations or affected demographics, unusual geographical features, atypical epidemiological patterns, delayed government action and communication to the public, misinformation and disinformation spread to the public and biosafety concerns/incidents predating the event. The outbreaks exhibited between 13 and 19 retrospectively identified indicators. These indicators were used to develop preliminary risk criteria intended to support structured, hypothesis-generating assessment of outbreaks, rather than to establish origin.

The activity of respiratory viruses (RVs) displays large variability in tropical regions, posing challenges for public health response strategies. Data from most RVs in south-eastern Mexico remain limited, particularly in the Yucatan Peninsula, the largest tourism hub in the country. This retrospective study analyses the regional epidemiology of RVs in Merida, the largest city in the region, using laboratory test data from a local hospital (January 2018–April 2024). Test results of 143292 RVs were collected, including 121976 for SARS-CoV-2, 19355 for influenza A and B viruses, and 1961 for 17 distinct RVs. We found that non-SARS-CoV-2 RVs circulated year-round, with higher activity in autumn and spring, while SARS-CoV-2 peaked in summer and winter. Influenza A virus, respiratory syncytial virus, and influenza B virus reached their highest activity in autumn, earlier than in other regions of Mexico. Human metapneumovirus peaked during autumn-winter. Rhinovirus/enterovirus and parainfluenza showed year-round activity, with peaks in autumn and spring. Other coronaviruses were more frequent during winter-spring. In post-pandemic years (2022–2023), adenovirus outbreaks emerged, as well as an increased prevalence of non-SARS-CoV-2 RV co-infections. This study highlights the need for region-specific public health strategies, including optimized vaccination schedules, such as for influenza A virus, and enhanced diagnostic surveillance.

This article presents novel methods and theories for estimation and inference about parameters in statistical models using machine learning for nuisance parameter estimation when data are dyadic. We propose a dyadic cross-fitting method to remove over-fitting biases under arbitrary dyadic dependence. Together with the use of Neyman orthogonal scores, this novel cross-fitting method enables root-n consistent estimation and inference robustly against dyadic dependence. We demonstrate its versatility by applying it to high-dimensional network formation models and reexamine the determinants of free trade agreements.