Based on the recent papers, two distributions for the total claims amount (loss cost) are considered: compound Poisson-gamma and Tweedie. Each is used as an underlying distribution in the Bonus-Malus Scale (BMS) model. The BMS model links the premium of an insurance contract to a function of the insurance experience of the related policy. In other words, the idea is to model the increase and the decrease in premiums for insureds who do or do not file claims. We applied our approach to a sample of data from a major insurance company in Canada. Data fit and predictability were analyzed. We showed that the studied models are exciting alternatives to consider from a practical point of view, and that predictive ratemaking models can address some important practical considerations.

This paper presents the AffineMortality R package which performs parameter estimation, goodness-of-fit analysis, simulation, and projection of future mortality rates for a set of affine mortality models for use in pricing and reserving. The computational routines build on the univariate Kalman Filtering approach of Koopman and Durbin ((2000). Journal of Time Series Analysis, 21(3), 281–296.) along other numerical methods to enhance the robustness of the results. This paper provides a discussion of how the package works in order to effectively estimate and project survival curves, and describes the available functions. Illustration of the package for mortality analysis of the US male data set is provided.

This study investigates the benefits and drawbacks of pension plan consolidation by quantifying the impact of mergers of heterogeneous plans on different stakeholders in a unique Canadian implementation of defined benefit plans. Using a comprehensive framework that combines a realistic economic scenario generator, a stochastic mortality model that captures differences among subpopulations, a cost model with economies of scale, and a dynamic asset allocation methodology, we evaluate the combined effect of asset- and liability-side changes on three groups of measures: plan-related risk measures assessing profits from an economic capital perspective, consumption-based metrics to understand the impact on members, and contribution risk measures capturing the risk from the employer’s viewpoint. We apply the framework to a hypothetical and empirically relevant merger and find that consolidation is favorable under most circumstances: the positive impacts of better diversification and economies of scale continue to outweigh the negative effects of heterogeneity even when the merging plans have different mortality expectations, different maturity levels, or modest differences in initial funded ratios.

We demonstrate that a large class of discrete choice models of demand can be approximated by real analytic demand models. We obtain this result by combining (i) a novel real analytic property of the mixed logit and the mixed probit models with any distribution of random coefficients and (ii) an approximation property of finite mixtures of Gumbel and Gaussian distributions. To illustrate some of the implications of this result, we discuss how real analyticity facilitates nonparametric and semi-nonparametric identification, extrapolation to hypothetical counterfactuals, numerical implementation of demand inverses, and numerical implementation of the maximum likelihood estimator.

As a generalization of random recursive trees and preferential attachment trees, we consider random recursive metric spaces. These spaces are constructed from random blocks, each a metric space equipped with a probability measure, containing a labelled point called a hook, and assigned a weight. Random recursive metric spaces are equipped with a probability measure made up of a weighted sum of the probability measures assigned to its constituent blocks. At each step in the growth of a random recursive metric space, a point called a latch is chosen at random according to the equipped probability measure, and a new block is chosen at random and attached to the space by joining together the latch and the hook of the block. We use martingale theory to prove a law of large numbers and a central limit theorem for the insertion depth, the distance from the master hook to the latch chosen. We also apply our results to further generalizations of random trees, hooking networks, and continuous spaces constructed from line segments.

We establish power-series expansions for the asymptotic expectations of the vertex number and missed area of random disc-polygons in planar convex bodies with $C^{k+1}_+$-smooth boundaries. These results extend asymptotic formulas proved in Fodor et al. (2014).

It is so far unclear how the COVID-19 winter waves started and what should be done to prevent possible future waves. In this study, we deciphered the dynamic course of a winter wave in 2021 in Saxony, a state in Eastern Germany neighbouring the Czech Republic and Poland. The study was carried out through the integration of multiple virus genomic epidemiology approaches to track transmission chains, identify emerging variants and investigate dynamic changes in transmission clusters. For identified local variants of interest, functional evaluations were performed. Multiple long-lasting community transmission clusters have been identified acting as driving force for the winter wave 2021. Analysis of the dynamic courses of two representative clusters indicated a similar transmission pattern. However, the transmission cluster caused by a locally occurring new Delta variant AY.36.1 showed a distinct transmission pattern, and functional analyses revealed a replication advantage of it. This study indicated that long-lasting community transmission clusters starting since early autumn caused by imported or locally occurring variants all contributed to the development of the 2021 winter wave. The information we achieved might help future pandemic prevention.

Shiga toxin-producing Escherichia coli (STEC) transmission occurs in ruminant contact settings and can lead to post-diarrheal hemolytic uremic syndrome (HUS). We investigated whether exposure setting (ruminant exposure from living or working on a farm, visiting a farm or animal contact venue, or both) influenced HUS development among individuals with laboratory-confirmed STEC infections using Minnesota surveillance data from 2010 to 2019. Logistic regression was performed to determine whether exposure setting was associated with HUS independent of age, gender, stx2 gene detection, and county ruminants per capita. Among confirmed STEC cases, ruminant exposure only from living or working on a farm was not significantly associated with HUS compared to cases without any ruminant exposure (OR: 1.25; 95% CI: 0.51, 3.04). However, ruminant exposure only from visiting a farm or public animal contact venue was associated with HUS (OR: 2.53; 95% CI: 1.50, 4.24). Exposure from both settings was also associated with HUS (OR: 3.71; 95% CI: 1.39, 9.90). Exposure to ruminants when visiting farms or animal contact venues is an important predictor of HUS, even among people who live or work on farms with ruminants. All people, regardless of routine ruminant exposure, should take care in settings with ruminants to avoid infection with STEC.

The moderation of user-generated content on online platforms remains a key solution to protecting people online, but also remains a perpetual challenge as the appropriateness of content moderation guidelines depends on the online community that they aim to govern. This challenge affects marginalized groups in particular, as they more frequently experience online abuse but also end up falsely being the target of content-moderation guidelines. While there have been calls for democratic, community-moderation, there has so far been little research into how to implement such approaches. Here, we present the co-creation of content moderation strategies with the users of an online platform to address some of these challenges. Within the context of AutSPACEs—an online citizen science platform that aims to allow autistic people to share their own sensory processing experiences publicly—we used a community-based and participatory approach to co-design a content moderation solution that would fit the preferences, priorities, and needs of its autistic user community. We outline how this approach helped us discover context-specific moderation dilemmas around participant safety and well-being and how we addressed those. These trade-offs have resulted in a moderation design that differs from more general social networks in aspects such as how to contribute, when to moderate, and what to moderate. While these dilemmas, processes, and solutions are specific to the context of AutSPACEs, we highlight how the co-design approach itself could be applied and useful for other communities to uncover challenges and help other online spaces to embed safety and empowerment.

The theory of utility is a well-known method of constructing insurance premiums (see e.g., Newton et al. (1986) Actuarial Mathematics. Itasca, Illinois: The Society of Actuaries.). Furman and Zitikis ((2008) Insurance: Mathematics and Economics, 42, 459–465.) proposed an alternative method using the mean value of a weighted random variable. According to this approach, for various choices of weighting, popular premiums such as net premium, modified variance premium, Esscher premium, and Kamps premium are obtained. On the other hand, some premiums cannot be obtained with this method, such as the premium of the exponential principle. In this paper, we provide a complementary theory by introducing a family of unimodal weighted distributions for which the mode is a premium principle.

China is still among the 30 high-burden tuberculosis (TB) countries in the world. Few studies have described the spatial epidemiological characteristics of pulmonary TB (PTB) in Jiangsu Province. The registered incidence data of PTB patients in 95 counties of Jiangsu Province from 2011 to 2021 were collected from the Tuberculosis Management Information System. Three-dimensional spatial trends, spatial autocorrelation, and spatial–temporal scan analysis were conducted to explore the spatial clustering pattern of PTB. From 2011 to 2021, a total of 347,495 newly diagnosed PTB cases were registered. The registered incidence rate of PTB decreased from 49.78/100,000 in 2011 to 26.49/100,000 in 2021, exhibiting a steady downward trend (χ2 = 414.22, P < 0.001). The average annual registered incidence rate of PTB was higher in the central and northern regions. Moran’s I indices of the registered incidence of PTB were all >0 (P< 0.05) except in 2016, indicating a positive spatial correlation overall. Local autocorrelation analysis showed that ‘high–high’ clusters were mainly distributed in northern Jiangsu, and ‘low–low’ clusters were mainly concentrated in southern Jiangsu. The results of this study assist in identifying settings and locations of high TB risk and inform policy-making for PTB control and prevention.

This paper studies a hierarchical risk model where an accident can cause a combination of different types of claims, whose sizes could be dependent. In addition, the frequencies of accidents that cause the different combinations of claims are dependent. We first derive formulas for computing risk measures, such as the Tail Conditional Expectation and Tail Variance of the aggregate losses for a portfolio of businesses. Then, we present formulas for performing the associated capital allocation to different types of claims in the portfolio. The main tool we used is the moment (or size-biased) transform of the multivariate distributions.

Simultaneous localization and mapping (SLAM) is the task of building a map representation of an unknown environment while at the same time using it for positioning. A probabilistic interpretation of the SLAM task allows for incorporating prior knowledge and for operation under uncertainty. Contrary to the common practice of computing point estimates of the system states, we capture the full posterior density through approximate Bayesian inference. This dynamic learning task falls under state estimation, where the state-of-the-art is in sequential Monte Carlo methods that tackle the forward filtering problem. In this paper, we introduce a framework for probabilistic SLAM using particle smoothing that does not only incorporate observed data in current state estimates, but it also backtracks the updated knowledge to correct for past drift and ambiguities in both the map and in the states. Our solution can efficiently handle both dense and sparse map representations by Rao-Blackwellization of conditionally linear and conditionally linearized models. We show through simulations and real-world experiments how the principles apply to radio (Bluetooth low-energy/Wi-Fi), magnetic field, and visual SLAM. The proposed solution is general, efficient, and works well under confounding noise.

Extreme propensity scores arise in observational studies when treated and control units have very different characteristics. This is commonly referred to as limited overlap. In this paper, we propose a formal statistical test that helps assess the degree of limited overlap. Rejecting the null hypothesis in our test indicates either no or very mild degree of limited overlap and hence reassures that standard treatment effect estimators will be well behaved. One distinguishing feature of our test is that it only requires the use of a few extreme propensity scores, which is in stark contrast to other methods that require consistent estimates of some tail index. Without the need to extrapolate using observations far away from the tail, our procedure is expected to exhibit excellent size properties, a result that is also borne out in our simulation study.

Pulmonary tuberculosis (PTB) elimination efforts must consider the global growth of the ageing population. Here we used TB surveillance data from Texas, United States (2008–2020; total n = 10656) to identify unique characteristics and outcomes in older adults (OA, ≥65 years) with PTB, compared to young adults (YA, 18–39 years) or middle-aged adults (40–64 years). We found that the proportion of OA with PTB increased from 15% in 2008 to 24% in 2020 (trend p < 0.05). Diabetes was highly prevalent in OA (32%) but not associated with adverse outcomes. Death was 13-fold higher in OA compared to YA and was 7% at the time of diagnosis which suggests diagnostic delays. However, once TB was suspected, we found no differences in culture, smear, or nucleic acid detection of mycobacteria (although less lung cavitations) in OA. During treatment, OA had less drug-resistant TB, few adverse reactions and adhered with TB treatment. We recommend training healthcare workers to ‘think TB’ in OA, for prompt treatment initiation to diminish deaths. Furthermore, OA should be added as a priority group to the latent TB treatment guidelines by the World Health Organization, to prevent TB disease in this highly vulnerable group.

In 2020, an outbreak of Salmonella Hadar illnesses was linked to contact with non-commercial, privately owned (backyard) poultry including live chickens, turkeys, and ducks, resulting in 848 illnesses. From late 2020 to 2021, this Salmonella Hadar strain caused an outbreak that was linked to ground turkey consumption. Core genome multilocus sequence typing (cgMLST) analysis determined that the Salmonella Hadar isolates detected during the outbreak linked to backyard poultry and the outbreak linked to ground turkey were closely related genetically (within 0–16 alleles). Epidemiological and traceback investigations were unable to determine how Salmonella Hadar detected in backyard poultry and ground turkey were linked, despite this genetic relatedness. Enhanced molecular characterization methods, such as analysis of the pangenome of Salmonella isolates, might be necessary to understand the relationship between these two outbreaks. Similarly, enhanced data collection during outbreak investigations and further research could potentially aid in determining whether these transmission vehicles are truly linked by a common source and what reservoirs exist across the poultry industries that allow Salmonella Hadar to persist. Further work combining epidemiological data collection, more detailed traceback information, and genomic analysis tools will be important for monitoring and investigating future enteric disease outbreaks.

The COVID-19 pandemic modified the epidemiology and the transmission of respiratory syncytial virus (RSV). We collected data on RSV positivity and incidence from children hospitalized in the largest tertiary paediatric hospital in Greece before (2018–2020, period A), during (2020–2021, period B), and after (2021–2023, period C) the COVID-19 lockdown. A total of 9,508 children were tested for RSV. RSV positivity (%) was 17.6% (552/3,134) for period A, 2.1% (13/629) for period B, and 13.4% (772/5,745) for period C (p < 0.001). The mean age (±SD) of RSV-positive children among the three periods was A: 5.9(±9.3), B: 13.6 (±25.3), and C: 16.7 (±28.6) months (p < 0.001). The peak of RSV epidemiology was shifted from January–March (period A) to October–December (period C). RSV in-hospital incidence per 1,000 hospitalizations in paediatric departments was A:16.7, B:1.0, and C:28.1 (p < 0.001), and the incidence in the intensive care unit was A: 17.3, B: 0.6, and C: 26.6 (p < 0.001). A decrease in RSV incidence was observed during the COVID-19 lockdown period, whereas a significant increase was observed after the lockdown. A change in epidemiological patterns was identified after the end of the lockdown, with an earlier seasonal peak and an age shift of increased RSV incidence in older children.

By the technique of augmented truncations, we obtain the perturbation bounds on the distance of the finite-time state distributions of two continuous-time Markov chains (CTMCs) in a type of weaker norm than the V-norm. We derive the estimates for strongly and exponentially ergodic CTMCs. In particular, we apply these results to get the bounds for CTMCs satisfying Doeblin or stochastically monotone conditions. Some examples are presented to illustrate the limitation of the V-norm in perturbation analysis and to show the quality of the weak norm.