In this paper, we introduce a novel way to quantify the remaining inaccuracy of order statistics by utilizing the concept of extropy. We explore various properties and characteristics of this new measure. Additionally, we expand the notion of inaccuracy for ordered random variables to a dynamic version and demonstrate that this dynamic information measure provides a unique determination of the distribution function. Moreover, we investigate specific lifetime distributions by analyzing the residual inaccuracy of the first-order statistics. Nonparametric kernel estimation of the proposed measure is suggested. Simulation results show that the kernel estimator with bandwidth selection using the cross-validation method has the best performance. Finally, an application of the proposed measure on the model selection is provided.

Legionellosis is a disease caused by the bacterium Legionella that most commonly presents as Legionnaires’ disease (LD), a severe form of pneumonia. From 2015 to 2019, an average of 438 LD cases per year were reported in Canada. However, it is believed that the actual number of cases is much higher, since LD may be underdiagnosed and underreported. The purpose of this study was to develop an estimate of the true incidence of illnesses, hospitalizations, and deaths associated with LD in Canada. Values were derived using a stochastic model, based on Canadian surveillance data from 2015 to 2019, which were scaled up to account for underdiagnosis and underreporting. Overall, there were an estimated 1,113 (90% CrI: 737–1,730) illnesses, 1,008 (90% CrI: 271–2,244) hospitalizations, and 34 (90% CrI: 4–86) deaths due to domestically acquired waterborne LD annually in Canada from 2015 to 2019. It was further estimated that only 36% of illnesses and 39% of hospitalizations and deaths were captured in surveillance, and that 22% of illnesses were caused by Legionella serogroups and species other than Legionella pneumophila serogroup 1 (non-Lp1). This study highlights the true burden and areas for improvement in Canada’s surveillance and detection of LD.

Adherence to post-exposure prophylaxis and post-exposure vaccination (PEV) is an important measure to prevent rabies. The purpose of this study was to explore the adherence to the vaccination protocol and its influencing factors among rabies-exposed patients in Shenzhen, China. A cross-sectional survey was conducted in a tertiary hospital in Shenzhen, China, to obtain epidemiological characteristics of patients; knowledge, attitude, and practice scores of rabies prevention; and medical records. A total of 326 patients requiring full rabies PEV were included in this study, and only 62% (202) completed the full course of vaccination according to the norms of the vaccination guidelines. After multifactor logistic regression, the factors influencing adherence to vaccination were as follows: age 31 to 40 years, time spent to reach the nearest rabies prevention clinic was >60 min, the time of injury was at night to early morning, the place of injury was a school/laboratory, the animal causing injury was a cat, the health status of the animal causing injury could not be determined, and patients with higher practice scores (all p<0.05). Understanding the factors influencing rabies vaccination adherence among rabies-exposed patients in urban areas of China and promote changes in patients’ practice toward rabies prevention is essential for rabies elimination by 2030.

The seismic response of a wide variety of structures, from small but irreplaceable museum exhibits to large bridge systems, is characterized by rocking. In addition, rocking motion is increasingly being used as a seismic protective strategy to limit the amount of seismic actions (moments) developed at the base of structures. However, rocking is a highly nonlinear phenomenon governed by non-smooth dynamic phases that make its prediction difficult. This study presents an alternative approach to rocking estimation based on a physics-informed convolutional neural network (PICNN). By training a group of PICNNs using limited datasets obtained from numerical simulations and encoding the known physics into the PICNNs, important predictive benefits are obtained relieving difficulties associated with over-fitting and minimizing the requirement for a large training database. Two models are created depending on the validation of the deep PICNN: the first model assumes that state variables including rotations and angular velocities are available, while the second model is useful when only acceleration measurements are known. The analysis is initiated by implementing K-means clustering. This is followed by a detailed statistical assessment and a comparative analysis of the response-histories of a rocking block. It is observed that the deep PICNN is capable of effectively estimating the seismic rocking response history when the rigid block does not overturn.

With the recent outbreak of COVID-19, evaluating the epidemic risk appears to be a pressing issue of global concern and one of the major challenges recently. In the fight against pandemics, the ability to understand, model, and forecast the transmission dynamics of infectious diseases plays a crucial role. This paper provides an overview of foundational compartment models and introduces the Susceptible-Exposed-Infected-Containing-3-Substates-Recovered-Dead model to study the dynamics of COVID-19. A meticulous data calibration procedure is employed to study the evolution trend of an actual pandemic using real-world data from Victoria, Australia. Additionally, the paper discusses innovative applications of epidemic models to the insurance industry, which are currently under investigation. Through the use of the newly developed analytically tractable model, insurance companies are able to determine fair premium levels during an outbreak. Moreover, the paper provides practical guidance for insurance companies by examining the variation in reserve levels over time.

This paper introduces a new class of time-varying vector moving average processes of infinite order. These processes serve dual purposes: (1) they can be used to model time-varying dependence structures, and (2) they can be used to establish asymptotic theories for multivariate time series models. To illustrate these two points, we first establish some fundamental asymptotic properties and use them to infer the trending term of a vector moving average infinity process. We then investigate a class of time-varying VARX models. Finally, we demonstrate the empirical relevance of the theoretical results using extensive simulated and real data studies.

Salmonella enterica continues to be a leading cause of foodborne morbidity worldwide. A quantitative risk assessment model was developed to evaluate the impact of pathogen enumeration and serotyping strategies on public health after consumption of undercooked contaminated ground turkey in the USA. The risk assessment model predicted more than 20,000 human illnesses annually that would result in ~700 annual reported cases. Removing ground turkey lots contaminated with Salmonella exceeding 10 MPN/g, 1 MPN/g, and 1 MPN/25 g would decrease the mean number of illnesses by 38.2, 73.1, and 95.0%, respectively. A three-class mixed sampling plan was tested to allow the detection of positive lots above threshold levels with 2–6 (c = 1) and 3–8 samples per lot (c = 2) using 25-g and 325-g sample sizes for a 95% probability of rejecting a contaminated lot. Removal of positive lots with the presence of highly virulent serotypes would decrease the number of illnesses by 44.2–87.0%. Based on these model prediction results, risk management strategies should incorporate pathogen enumeration and/or serotyping. This would have a direct impact on illness incidence linking public health outcomes with measurable food safety objectives, at the cost of diverting production lots.

In addition to the well-known differences among the four dengue serotypes, intra-serotypic antigenic diversity has been proposed to play a role in viral evolution and epidemic fluctuation. A replacement of genotype II by genotype III of dengue virus serotype 3 (DENV3) occurred in Thailand during 2007–2014, raising questions about the role of intra-serotypic antigenic differences in this genotype shift. We characterized the antigenic difference of DENV3 of genotypes II and III in Thailand, utilizing a neutralizing antibody assay with DENV3 vaccine sera and monotypic DENV3 sera. Although there was significant antigenic diversity among the DENV3, it did not clearly associate with the genotype. Our data therefore do not support the role of intra-serotypic antigenic difference in the genotype replacement. Amino acid alignment showed that eight positions are potentially associated with diversity between distinct antigenic subgroups. Most of these amino acids were found in envelope domain II. Some positions (aa81, aa124, and aa172) were located on the surface of virus particles, probably involving the neutralization sensitivity. Notably, the strains of both genotypes II and III showed clear antigenic differences from the vaccine genotype I strain. Whether this differencewill affect vaccine efficacy requires further studies.

Compared to cervical cancer, little is known about human papillomavirus (HPV)-driven oropharyngeal cancer and their cofactors. Here, we investigated potential associations between Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) with oral HPV and HPV persistence, which are known cofactors in cervical carcinogenesis, and also play a role in HPV-driven oropharyngeal cancer. Saliva samples (n = 547) from 312 people were tested for CT and NG and whom had previously been tested for oral HPV infection in a longitudinal study. Eight participants were positive for CT (2.6%) and one for NG (0.3%). Six of these nine participants were also positive for oral HPV in at least one of their samples. We found no significant associations between HPV, CT, or NG infection in the saliva samples analyzed. These preliminary data suggest CT and NG have little influence on oral HPV-positivity and persistence in a general population. However, larger studies focusing on ‘at risk’ population cohorts are necessary to assess potential associations between oral sexually transmissible infections and oral HPV infections, and their outcomes.

The goal of this paper is to go further in the analysis of the behavior of the number of descents in a random permutation. Via two different approaches relying on a suitable martingale decomposition or on the Irwin–Hall distribution, we prove that the number of descents satisfies a sharp large-deviation principle. A very precise concentration inequality involving the rate function in the large-deviation principle is also provided.

Schizophrenia is recognized as a significant risk factor for tuberculosis (TB). This study aimed to evaluate the effectiveness and cost-effectiveness of interferon-γ release assay (IGRA) with preventive treatment for screening of latent tuberculosis infection (LTBI) in individuals with schizophrenia. A state transition model was developed from a healthcare payer perspective on a lifetime horizon. Ten strategies were compared by combining two different tests for LTBI, i.e. IGRA and tuberculin skin test (TST), and five different preventive treatments, i.e. 9-month isoniazid (9H), 3-month isoniazid and rifapentine (3HP) by directly observed therapy, 3HP by self-administered therapy, 3-month isoniazid and rifampin (3RH), and 4-month rifampin (4R). The main outcomes were costs, quality-adjusted life-years (QALYs), life expectancy life-years (LYs), incremental cost-effectiveness ratios, drug-sensitive tuberculosis (DS-TB) cases, and TB-related deaths. For both bacillus Calmette–Guérin (BCG)-vaccinated and non-BCG-vaccinated individuals, IGRA with 4R was the most cost-effective and TST with 3RH was the least effective. Among schizophrenic individuals in Japan, IGRA with 4R saved US$17.8 million, increased 58,981 QALYs and 935 LYs, and prevented 222 DS-TB cases and 75 TB-related deaths compared with TST with 3RH. In individuals with schizophrenia, IGRA with 4R is recommended for LTBI screening with preventive treatment to reduce costs, morbidity, and mortality from TB.

In a subgroup analysis for an actuarial problem, the goal is for the investigator to classify the policyholders into unique groups, where the claims experience within each group are made as homogenous as possible. In this paper, we illustrate how the alternating direction method of multipliers (ADMM) approach for subgroup analysis can be modified so that it can be more easily incorporated into an insurance claims analysis. We present an approach to penalize adjacent coefficients only and show how the algorithm can be implemented for fast estimation of the parameters. We present three different cases of the model, depending on the level of dependence among the different coverage groups within the data. In addition, we provide an interpretation of the credibility problem using both random effects and fixed effects, where the fixed effects approach corresponds to the ADMM approach to subgroup analysis, while the random effects approach represents the classic Bayesian approach. In an empirical study, we demonstrate how these approaches can be applied to real data using the Wisconsin Local Government Property Insurance Fund data. Our results show that the presented approach to subgroup analysis could provide a classification of the policyholders that improves the prediction accuracy of the claim frequencies in case other classifying variables are unavailable in the data.

Using two rounds of serosurveillance, we aimed to observe the COVID-19 vaccination status and the dynamics of antibody responses to different vaccines among urban slum and non-slum populations of Bangladesh. Adults (>18 years) and children (10–17 years) were enrolled in March and October 2022. Data including COVID-19 vaccine types and dosage uptake were collected. SARS-CoV-2 spike (S)-specific antibodies were measured in blood. The proportion of vaccinated children was significantly lower among slum than non-slum populations. Two doses of vaccines showed an increase in the level of anti-S-antibodies up to 2 months, followed by reduced levels at 2–6 months and a resurgence at 6–12 months. Children showed significantly higher anti-S-antibodies after two doses of the Pfizer–BioNTech vaccine than adults; however, after 6 months, the level of antibodies declined in younger children (10 - < 12 years). In a mixed vaccine approach, mRNA vaccines contributed to the highest antibody response whether given as the first two doses or as the third dose. Our findings emphasized the need for increasing the coverage of COVID-19 vaccination among slum children and booster dosing among all children. The use of mRNA vaccines in the mixed vaccination approach was found to be useful in boosting the antibody response to SARS-CoV-2.

We consider a discrete-time population growth system called the Bienaymé–Galton–Watson stochastic branching system. We deal with a noncritical case, in which the per capita offspring mean $m\neq1$. The famous Kolmogorov theorem asserts that the expectation of the population size in the subcritical case $m<1$ on positive trajectories of the system asymptotically stabilizes and approaches ${1}/\mathcal{K}$, where $\mathcal{K}$ is called the Kolmogorov constant. The paper is devoted to the search for an explicit expression of this constant depending on the structural parameters of the system. Our argumentation is essentially based on the basic lemma describing the asymptotic expansion of the probability-generating function of the number of individuals. We state this lemma for the noncritical case. Subsequently, we find an extended analogue of the Kolmogorov constant in the noncritical case. An important role in our discussion is also played by the asymptotic properties of transition probabilities of the Q-process and their convergence to invariant measures. Obtaining the explicit form of the extended Kolmogorov constant, we refine several limit theorems of the theory of noncritical branching systems, showing explicit leading terms in the asymptotic expansions.

This paper provides a review of cyber risk research accomplished in different disciplines, with a primary goal to aid researchers in the field of insurance and actuarial science in identifying potential research gaps as well as leveraging useful models and techniques that have been considered in the literature. We highlight the recent advancements in cyber risk prediction, modeling, management, and insurance achieved in different domains including computer engineering, actuarial science, and business studies. The surveyed works are classified according to their respective modeling approaches, allowing readers to more easily compare the technical aspects of the surveyed works and spot out research gaps based on the research tools of their liking. We conclude this paper with a summary of possible research directions that are identified from the review.

This study aimed to understand rural–urban differences in the uptake of COVID-19 vaccinations during the peak period of the national vaccination roll-out in Aotearoa New Zealand (NZ). Using a linked national dataset of health service users aged 12+ years and COVID-19 immunization records, age-standardized rates of vaccination uptake were calculated at fortnightly intervals, between June and December 2021, by rurality, ethnicity, and region. Rate ratios were calculated for each rurality category with the most urban areas (U1) used as the reference. Overall, rural vaccination rates lagged behind urban rates, despite early rapid rural uptake. By December 2021, a rural–urban gradient developed, with age-standardized coverage for R3 areas (most rural) at 77%, R2 81%, R1 83%, U2 85%, and U1 (most urban) 89%. Age-based assessments illustrate the rural–urban vaccination uptake gap was widest for those aged 12–44 years, with older people (65+) having broadly consistent levels of uptake regardless of rurality. Variations from national trends are observable by ethnicity. Early in the roll-out, Indigenous Māori residing in R3 areas had a higher uptake than Māori in U1, and Pacific peoples in R1 had a higher uptake than those in U1. The extent of differences in rural–urban vaccine uptake also varied by region.

Climate change is at the forefront of discussions for many companies. Climate change-related disclosures and reporting are important tools and allow stakeholders to understand climate-related risks a company is facing and help various stakeholders to take informed decisions.

The landscape for climate change-related reporting requirements is ever evolving, with a trend from voluntary to mandatory, with many global disclosure standards and requirements influencing local requirements and other related standards.

This paper explores these ideas further, giving a general background to disclosure requirements, discusses greenwashing, details disclosure organisations including TCFD and the ISSB, and provides details on a country level including green taxonomies.

The paper develops a methodology to enable microscopic models of transportation systems to be accessible for a statistical study of traffic accidents. Our approach is intended to permit an understanding not only of historical losses but also of incidents that may occur in altered, potential future systems. Through such a counterfactual analysis, it is possible, from an insurance, but also from an engineering perspective, to assess the impact of changes in the design of vehicles and transport systems in terms of their impact on road safety and functionality.

Structurally, we characterize the total loss distribution approximatively as a mean-variance mixture. This also yields valuation procedures that can be used instead of Monte Carlo simulation. Specifically, we construct an implementation based on the open-source traffic simulator SUMO and illustrate the potential of the approach in counterfactual case studies.