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.

This paper considers variable annuity (VA) contracts embedded with guaranteed minimum accumulation benefit (GMAB) riders when policyholder’s proceeds are taxed upon early surrender or maturity. These contracts promise the return of the premium paid by the policyholder, or a higher rolled-up value, at the end of the investment period. A partial differential equation valuation framework which exploits the numerical method of lines is used to determine fair fees that render the policyholder and insurer breakeven. Two taxation regimes are considered: one where capital gains are allowed to offset losses and a second where gains do not offset losses. Most insurance providers highlight the tax-deferred features of VA contracts. We show that the regime under which the insured is taxed significantly impacts prices. If losses are allowed to offset gains then this enhances the market, increasing the policyholder’s willingness to participate in the market compared to the case when losses are not allowed to offset gains. With fair fees from the policyholder’s perspective, we show that the net profit is generally positive for insurance companies offering the contract as a naked option without any hedge. We also show how investment policy, as reflected in the Sharpe ratio, impacts and interacts with policyholder persistency.

Pension funds and insurers face difficulties in hedging their longevity risk, which is the uncertainty of how long their clients will live. A possible solution could be using longevity-linked securities to transfer some of this risk to other parties. However, these securities may not match the actual mortality rates of the insurer’s clients, resulting in a potential loss due to basis risk. In this paper, we measure this basis risk through the pricing of a longevity derivative under Solvency II. We also compare this method with other common pricing methods in finance. We explore and evaluate different hedging strategies for insurers, using a multi-population model derived from a two-dimensional Hull and White model that captures the dynamics of mortality over time.

There are now an estimated 114 million forcibly displaced people worldwide, some 88% of whom are in low- and middle-income countries. For governments and international organizations to design effective policies and responses, they require comparable and accessible socioeconomic data on those affected by forced displacement, including host communities. Such data is required to understand needs, as well as interactions between complex drivers of displacement and barriers to durable solutions. However, high-quality data of this kind takes time to collect and is costly. Can the ever-increasing volume of open data and evolving innovative techniques accelerate and enhance its generation? Are there applications of alternative data sources, advanced statistics, and machine-learning that could be adapted for forced displacement settings, considering their specific legal and ethical dimensions? As a catalytic bridge between the World Bank and UNHCR, the Joint Data Center on Forced Displacement convened a workshop to answer these questions. This paper summarizes the emergent messages from the workshop and recommendations for future areas of focus and ways forward for the community of practice on socioeconomic data on forced displacement. Three recommended areas of future focus are: enhancing and optimizing household survey sampling approaches; estimating forced displacement socioeconomic indicators from alternative data sources; and amplifying data accessibility and discoverability. Three key features of the recommended approach are: strong complementarity with the existing data-collection-to-use-pipeline; data responsibility built-in and tailored to forced displacement contexts; and iterative assessment of operational relevance to ensure continuous focus on improving outcomes for those affected by forced displacement.