Decumulation Pathways are proposed to help achieve better retirement outcomes for those with Defined Contribution (DC) pensions. The DC fund is split into two parts, in proportions of the consumer’s choice. Most is allocated to the Pension Fund to provide a lifetime income, while the rest is placed in the Flexible Fund for flexible access and/or to leave as a legacy. The Flexible Fund is invested in flexi-access drawdown. The Pension Fund is invested in a guaranteed annuity, Collective Defined Contribution, or a Pooled Pension Fund which maintains individual DC funds but pools longevity risk between participants. An illustrative standard Decumulation Pathway is intended as a default solution, or can be tailored by the consumer. It uses the Pooled Pension Fund, an automated withdrawal strategy which ensures a lifetime income is provided and one that aims to increase in line with inflation, and a moderate risk investment strategy. The standard approach is evaluated using various metrics, indicating that it has as a strong chance of providing a higher income than could be obtained from an annuity or drawdown, with limited downside risk.

Tuberculosis is a major public health issue in Yemen, a country located at the southwestern tip of the Arabian Peninsula, while the situation of tuberculosis had been further exacerbated since the war started in 2015. The objective of this study is to investigate the incidence of tuberculosis in Yemen before the outbreak of COVID-19, from 2006 to 2018. During the 13-year period, 92 482 patients were enrolled in the TB programme records from the 22 governorates. Almost equal number of cases were diagnosed between males and females (a male to female ratio, 1.03:1). A notable rising incidence was observed in all age groups starting from 2011. The sharpest increase occurred in children under age 15, rising by 8.0-fold from 0.5 in the period 2006–2010 to 4.1 in the period 2011–2018. Paediatric TB accounted for 9.6% of all reported cases. In terms of the patient residence, incidence has more than doubled in Sana'a city, Sana'a Gov., Hajjah and Saadah. Concomitant diseases with tuberculosis included diabetes mellitus (14.0%), brucellosis (6.1%), hepatitis (6.0%), rheumatoid arthritis (4.3%), renal disorders (2.5%) and HIV infection (2.5%). Development of interventions to reduce tuberculosis incidence in children and concomitant communicable diseases is urgently needed.

This paper uses a robust method of spatial epidemiological analysis to assess the spatial growth rate of multiple lineages of SARS-CoV-2 in the local authority areas of England, September 2020–December 2021. Using the genomic surveillance records of the COVID-19 Genomics UK (COG-UK) Consortium, the analysis identifies a substantial (7.6-fold) difference in the average rate of spatial growth of 37 sample lineages, from the slowest (Delta AY.4.3) to the fastest (Omicron BA.1). Spatial growth of the Omicron (B.1.1.529 and BA) variant was found to be 2.81× faster than the Delta (B.1.617.2 and AY) variant and 3.76× faster than the Alpha (B.1.1.7 and Q) variant. In addition to AY.4.2 (a designated variant under investigation, VUI-21OCT-01), three Delta sublineages (AY.43, AY.98 and AY.120) were found to display a statistically faster rate of spatial growth than the parent lineage and would seem to merit further investigation. We suggest that the monitoring of spatial growth rates is a potentially valuable adjunct to outbreak response procedures for emerging SARS-CoV-2 variants in a defined population.

We consider the assignment of servers to two phases of service in a two-stage tandem queueing system when customers can abandon from each stage of service. New jobs arrive at both stations. Jobs arriving at station 1 may go through both phases of service and jobs arriving at station 2 may go through only one phase of service. Stage-dependent holding and lump-sum abandonment costs are incurred. Continuous-time Markov decision process formulations are developed that minimize discounted expected and long-run average costs. Because uniformization is not possible, we use the continuous-time framework and sample path arguments to analyze control policies. Our main results are conditions under which priority rules are optimal for the single-server model. We then propose and evaluate threshold policies for allocating one or more servers between the two stages in a numerical study. These policies prioritize a phase of service before “switching” to the other phase when total congestion exceeds a certain number. Results provide insight into how to adjust the switching rule to significantly reduce costs for specific input parameters as well as more general multi-server situations when neither preemption or abandonments are allowed during service and service and abandonment times are not exponential.

We prove polynomial ergodicity for the one-dimensional Zig-Zag process on heavy-tailed targets and identify the exact order of polynomial convergence of the process when targeting Student distributions.

Natural infection with the influenza virus is believed to generate cross-protective immunity across both types and subtypes. However, less is known about the persistence of this immunity and thus the susceptibility of individuals to repeat infection. We used 13 years (2005–2017) of surveillance data from Queensland, Australia, to describe the incidence and distribution of repeat influenza infections. Consecutive infections that occurred within 14 days of prior infection were considered a mixed infection; those that occurred more than 14 days later were considered separate (repeat) infections. Kaplan-Meier plots were used to investigate the probability of reinfection over time and the Prentice, Williams and Peterson extension of the Cox proportional hazards model was used to assess the association of age and gender with reinfection. Among the 188 392 notifications received during 2005–2017, 6165 were consecutively notified for the same individual (3.3% of notifications), and 2958 were mixed infections (1.6%). Overall, the probability of reinfection was low: the cumulative incidence was <1% after one year, 4.6% after five years, and 9.6% after ten years. The majority of consecutive infections were the result of two type A infections (43%) and were most common among females (adjusted hazard ratio (aHR): 1.15, 95% confidence interval (CI) 1.09–1.21), children aged less than 5 years (relative to adults aged 18–64 years aHR: 1.58, 95% CI 1.47–1.70) and older adults aged at least 65 years (aHR: 1.35; 95% CI 1.24–1.47). Our study suggests consecutive infections are possible but rare. These findings have implications for our understanding of population immunity to influenza.

Antimicrobial resistance (AMR) is a complex problem that is a threat to global public health. Consumption of turkey meat may be an important source of foodborne exposure to resistant bacteria; recent outbreaks of multi-drug-resistant Salmonella Reading in Canada and the USA have implicated raw turkey products. To better understand the epidemiology of AMR in farmed turkey production, a scoping review was conducted. The objectives were to identify (1) modifiable factors potentially associated with antimicrobial-resistant Campylobacter, Enterococcus, Escherichia coli and Salmonella enterica along the farm-to-fork pathway in turkeys, and (2) data gaps with respect to factors potentially associated with AMR and Canadian commercial turkey production. A comprehensive search of the peer-reviewed literature was conducted in 2019 and updated in 2021. Thirteen references were included, reporting 36 factors. Antimicrobial use factors and their potential associations with AMR were most frequently reported (n = 15 factors; 42%), followed by biosecurity (n = 11; 31%) and management practices (n = 10; 28%). This review revealed important data gaps; no factors pertaining to S. enterica or to stages other than the farm (e.g. abattoir, retail) were identified, and only one Canadian reference was identified. These findings will inform priorities for future research and surveillance regarding turkeys and AMR.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, first notified in China, has spread around the world causing high morbidity and mortality, which is due to factors such as the subversion of the immune response. The aims of the study are to summarise and present the immunopathological relationship of COVID-19 with innate immunity. This is a systematic review conducted by the National Library of Medicine – National Institutes of Health, USA (PUBMED), Latin American and Caribbean Literature on Health Sciences (LILACS), Medical Literature Analysis and Retrieval System Online (MEDLINE) and Scientific Electronic Library Online (SCIELO) databases with clinical trials, in vitro assays, case-controls, cohort studies, systematic reviews and meta-analyses between February 2020 and July 2021. The version 2 of the Cochrane risk-of-bias tool for RCTs (RoB 2), Joana Briggs Institute (JBI) Critical Appraisal (for the review articles) and the Risk of Bias in Non-randomised Studies of Interventions (ROBINS-I) tools were used to evaluate the quality and the risk of bias of the studies included in this review. The innate immune response through the generation of interferons, alternative pathways and complement system lectins and the joint action of innate immune cells and cytokines and chemokines lead to different clinical outcomes, taking into account the exacerbated inflammatory response and pathogenesis. Then, in addition to interacting as a bridge for adaptive immunity, the innate immune response plays an essential role in primary defense and is one of the starting points for immune evasion by SARS-CoV-2.

In this paper, we study the tail risk measures for several commonly used multivariate aggregate loss models where the claim frequencies are dependent but the claim sizes are mutually independent and independent of the claim frequencies. We first develop formulas for the moment (or size biased) transforms of the multivariate aggregate losses, showing their relationship with the moment transforms of the claim frequencies and claim sizes. Then, we apply the formulas to compute some popular risk measures such as the tail conditional expectation and tail variance of the multivariate aggregated losses and to perform capital allocation analysis.

Functional coefficient (FC) regressions allow for systematic flexibility in the responsiveness of a dependent variable to movements in the regressors, making them attractive in applications where marginal effects may depend on covariates. Such models are commonly estimated by local kernel regression methods. This paper explores situations where responsiveness to covariates is locally flat or fixed. The paper develops new asymptotics that take account of shape characteristics of the function in the locality of the point of estimation. Both stationary and integrated regressor cases are examined. The limit theory of FC kernel regression is shown to depend intimately on functional shape in ways that affect rates of convergence, optimal bandwidth selection, estimation, and inference. In FC cointegrating regression, flat behavior materially changes the limit distribution by introducing the shape characteristics of the function into the limiting distribution through variance as well as centering. In the boundary case where the number of zero derivatives tends to infinity, near parametric rates of convergence apply in stationary and nonstationary cases. Implications for inference are discussed and a feasible pre-test inference procedure is proposed that takes unknown potential flatness into consideration and provides a practical approach to inference.

In this paper, we discuss a generalization of the classical compound Poisson model with claim sizes following a compound distribution. As applications, we consider models involving zero-truncated geometric, zero-truncated negative-binomial and zero-truncated binomial batch-claim arrivals. We also provide some ruin-related quantities under the resulting risk models. Finally, through numerical examples, we visualize the behavior of these quantities.

The Covid-19 pandemic caused an alarming mortality stress. The evidence shows that a significant proportion of people who die from Covid-19 are in a frail state. According to this consideration, we assume that the mortality shocks are related to a group of the individuals with some co-morbidities at Covid-19 diagnosis. In other words, the mortality shocks present a specific characterisation, which consists of a causal connection with pre-existing conditions, and the phenomenon could be described as a mortality acceleration. In this paper, an Accelerated Mortality Model is proposed in order to capture the different effects on mortality that depend on the evolution of the pandemic and the presence of co-morbidities at diagnosis. Furthermore, we assess the impact of Covid-19 mortality acceleration on a set of traditional life insurance contracts. We observe that, although mortality acceleration by Covid-19 affects more markedly the elderly and unhealthy sub-populations, it could be considered as a temporary shock with a limited impact on the life insurance market.

We study an example of a hit-and-run random walk on the symmetric group $\mathbf S_n$. Our starting point is the well-understood top-to-random shuffle. In the hit-and-run version, at each single step, after picking the point of insertion j uniformly at random in $\{1,\ldots,n\}$, the top card is inserted in the jth position k times in a row, where k is uniform in $\{0,1,\ldots,j-1\}$. The question is, does this accelerate mixing significantly or not? We show that, in $L^2$ and sup-norm, this accelerates mixing at most by a constant factor (independent of n). Analyzing this problem in total variation is an interesting open question. We show that, in general, hit-and-run random walks on finite groups have non-negative spectrum.

In October 2021, the WHO published an ambitious strategy to ensure that all countries had vaccinated 40% of their population by the end of 2021 and 70% by mid-2022. The end of June 2022 marks 18 months of implementation of coronavirus disease 2019 (COVID-19) vaccination in the African region and provides an opportunity to look back and think ahead about COVID-19 vaccine set targets, demand and delivery strategies. As of 26 June 2022 two countries in the WHO African region have achieved this target (Mauritius and Seychelles) and seven are on track, having vaccinated between 40% and 69% of their population. By the 26 June 2022, seven among the 20 countries that had less than 10% of people fully vaccinated at the end of January 2022, have surpassed 15% of people fully vaccinated at the end of June 2022. This includes five targeted countries, which are being supported by the WHO Regional Office for Africa through the Multi-Partners' Country Support Team Initiative. As we enter the second semester of 2022, a window of opportunity has opened to provide new impetus to COVID-19 vaccination rollout in the African region guided by the four principles: Scale-up, Transition, Consolidation and Communication. Member States need to build on progress made to ensure that this impetus is not lost and that the African region does not remain the least vaccinated global region, as economies open up and world priorities change.

Simulated tempering is a popular method of allowing Markov chain Monte Carlo algorithms to move between modes of a multimodal target density $\pi$. Tawn, Moores and Roberts (2021) introduces the Annealed Leap-Point Sampler (ALPS) to allow for rapid movement between modes. In this paper we prove that, under appropriate assumptions, a suitably scaled version of the ALPS algorithm converges weakly to skew Brownian motion. Our results show that, under appropriate assumptions, the ALPS algorithm mixes in time $O(d [\log d]^2)$ or O(d), depending on which version is used.

The US government invests substantial sums to control the HIV/AIDS epidemic. To monitor progress toward epidemic control, PEPFAR, or the President’s Emergency Plan for AIDS Relief, oversees a data reporting system that includes standard indicators, reporting formats, information systems, and data warehouses. These data, reported quarterly, inform understanding of the global epidemic, resource allocation, and identification of trouble spots. PEPFAR has developed tools to assess the quality of data reported. These tools made important contributions but are limited in the methods used to identify anomalous data points. The most advanced consider univariate probability distributions, whereas correlations between indicators suggest a multivariate approach is better suited. For temporal analysis, the same tool compares values to the averages of preceding periods, though does not consider underlying trends and seasonal factors. To that end, we apply two methods to identify anomalous data points among routinely collected facility-level HIV/AIDS data. One approach is Recommender Systems, an unsupervised machine learning method that captures relationships between users and items. We apply the approach in a novel way by predicting reported values, comparing predicted to reported values, and identifying the greatest deviations. For a temporal perspective, we apply time series models that are flexible to include trend and seasonality. Results of these methods were validated against manual review (95% agreement on non-anomalies, 56% agreement on anomalies for recommender systems; 96% agreement on non-anomalies, 91% agreement on anomalies for time series). This tool will apply greater methodological sophistication to monitoring data quality in an accelerated and standardized manner.

While incidence studies based on hospitalisation counts are commonly used for public health decision-making, no standard methodology to define hospitals' catchment population exists. We conducted a review of all published community-acquired pneumonia studies in England indexed in PubMed and assessed methods for determining denominators when calculating incidence in hospital-based surveillance studies. Denominators primarily were derived from census-based population estimates of local geographic boundaries and none attempted to determine denominators based on actual hospital access patterns in the community. We describe a new approach to accurately define population denominators based on historical patient healthcare utilisation data. This offers benefits over the more established methodologies which are dependent on assumptions regarding healthcare-seeking behaviour. Our new approach may be applicable to a wide range of health conditions and provides a framework to more accurately determine hospital catchment. This should increase the accuracy of disease incidence estimates based on hospitalised events, improving information available for public health decision making and service delivery planning.

A system of mutually interacting superprocesses with migration is constructed as the limit of a sequence of branching particle systems arising from population models. The uniqueness in law of the superprocesses is established using the pathwise uniqueness of a system of stochastic partial differential equations, which is satisfied by the corresponding system of distribution function-valued processes.

$U{\hbox{-}}\textrm{max}$ statistics were introduced by Lao and Mayer in 2008. Such statistics are natural in stochastic geometry. Examples are the maximal perimeters and areas of polygons and polyhedra formed by random points on a circle, ellipse, etc. The main method to study limit theorems for $U{\hbox{-}}\textrm{max}$ statistics is via a Poisson approximation. In this paper we consider a general class of kernels defined on a circle, and we prove a universal limit theorem with the Weibull distribution as a limit. Its parameters depend on the degree of the kernel, the structure of its points of maximum, and the Hessians of the kernel at these points. Almost all limit theorems known so far may be obtained as simple special cases of our general theorem. We also consider several new examples. Moreover, we consider not only the uniform distribution of points but also almost arbitrary distribution on a circle satisfying mild additional conditions.