We analyze the long-term stability of a stochastic model designed to illustrate the adaptation of a population to variation in its environment. A piecewise deterministic process modeling adaptation is coupled to a Feller logistic diffusion modeling population size. As the individual features in the population become further away from the optimal ones, the growth rate declines, making population extinction more likely. Assuming that the environment changes deterministically and steadily in a constant direction, we obtain the existence and uniqueness of the quasi-stationary distribution, the associated survival capacity, and the Q-process. Our approach also provides several exponential convergence results (in total variation for the measures). From this synthetic information, we can characterize the efficiency of internal adaptation (i.e. population turnover from mutant invasions). When the latter is lacking, there is still stability, but because of the high level of population extinction. Therefore, any characterization of internal adaptation should be based on specific features of this quasi-ergodic regime rather than the mere existence of the regime itself.

Modelling mortality co-movements for multiple populations has significant implications for mortality/longevity risk management. This paper assumes that multiple populations are heterogeneous sub-populations randomly drawn from a hypothetical super-population. Those heterogeneous sub-populations may exhibit various patterns of mortality dynamics across different age groups. We propose a hierarchical structure of these age patterns to ensure the model stability and use a Vector Error Correction Model (VECM) to fit the co-movements over time. Especially, a structural analysis based on the VECM is implemented to investigate potential interdependence among mortality dynamics of the examined populations. An efficient Bayesian Markov Chain Monte-Carlo method is also developed to estimate the unknown parameters to address the computational complexity. Our empirical application to the mortality data collected for the Group of Seven nations demonstrates the efficacy of our approach.

Stepwise non-pharmaceutical interventions and health system changes implemented as part of the COVID-19 response have had implications on the incidence, diagnosis, and reporting of other communicable diseases. Here, we established the impact of the COVID-19 outbreak response on gastrointestinal (GI) infection trends using routinely collected surveillance data from six national English laboratory, outbreak, and syndromic surveillance systems using key dates of governmental policy to assign phases for comparison between pandemic and historic data. Following decreases across all indicators during the first lockdown (March–May 2020), bacterial and parasitic pathogens associated with foodborne or environmental transmission routes recovered rapidly between June and September 2020, while those associated with travel and/or person-to-person transmission remained lower than expected for 2021. High out-of-season norovirus activity was observed with the easing of lockdown measures between June and October 2021, with this trend reflected in laboratory and outbreak systems and syndromic surveillance indicators. Above expected increases in emergency department (ED) attendances may have reflected changes in health-seeking behaviour and provision. Differential reductions across specific GI pathogens are indicative of the underlying routes of transmission. These results provide further insight into the drivers for transmission, which can help inform control measures for GI infections.

Antifungal susceptibility of Candida species is decreasing. Successful treatment for antifungal-resistant candida infection is challenging and associated with significant mortality. We performed a prospective observational study to identify the species and antifungal susceptibilities of invasive isolates of Candida species over a 5-year period at a university hospital in southern Thailand. Between 2017 and 2021, the species distribution was 39.1% Candida tropicalis, 24.8% Candida albicans, 20.3% Candida parapsilosis complex, 10.5% Candida glabrata, and 5.2% miscellaneous Candida spp. Notable observations include elevated minimal inhibitory concentration (MIC) and decrease susceptibility of C. tropicalis and C. glabrata to echinocandin and all tested triazoles. A shift of MIC90 value in the COVID-19 era was seen in C. albicans and C. tropicalis with azoles and echinocandins. Azole resistance increased among C. tropicalis isolates, and echinocandin resistance also increased among C. parapsilosis and C. glabrata isolates. Novel alterations in FKS1 HS1 and HS2 were detected in both isolates of anidulafungin-resistant C. parapsilosis. As Candida species have become more resistant to azoles and less susceptible to echinocandin development, the need arose to observe the emergence of resistance to both antifungal classes in candida clinical isolates, for a more effective infection control in the hospital.

Newcastle disease (ND) is a notifiable disease affecting chickens and other avian species caused by virulent strains of Avian paramyxovirus type 1 (APMV-1). While outbreaks of ND can have devastating consequences, avirulent strains of APMV-1 generally cause subclinical infections or mild disease. However, viruses can cause different levels of disease in different species and virulence can evolve following cross-species transmission events. This report describes the detection of three cases of avirulent APMV-1 infection in Great Britain (GB). Case 1 emerged from the ‘testing to exclude’ scheme in chickens in Shropshire while cases 2 and 3 were made directly from notifiable avian disease investigations in chicken broilers in Herefordshire and on premises in Wiltshire containing ducks and mixed species, respectively). Class II/genotype I.1.1 APMV-1 from case 1 shared 99.94% identity to the Queensland V4 strain of APMV-1. Class II/genotype II APMV-1 was detected from case 2 while the class II/genotype I.2 virus from case 3 aligned closely with strains isolated from Anseriformes. Exclusion of ND through rapid detection of avirulent APMV-1 is important where clinical signs caused by avirulent or virulent APMV-1s could be ambiguous. Understanding the diversity of APMV-1s circulating in GB is critical to understanding disease threat from these adaptable viruses.

The social networks surrounding intimate couples provide them with bonding and bridging social capital and have been theorized to be associated with their well-being and relationship quality. These networks are multidimensional, featuring compositional (e.g., the proportion of family members vs. friends) and structural characteristics (e.g., density, degree of overlap between spouses’ networks). Most previous studies of couple networks are based on partners’ global ratings of their network characteristics or network data collected from one member of the dyad. This study presents the analysis of “duocentric networks" or the combined personal networks of both members of a couple, collected from 207 mixed-sex newlywed couples living in low-income neighborhoods of Harris County, TX. We conducted a pattern-centric analysis of compositional and structural features to identify distinct types of couple networks. We identified five qualitatively distinct network types (wife family-focused, husband family-focused, shared friends, wife friend-focused, and extremely disconnected). Couples’ network types were associated with the quality of the relationships between couples and their network contacts (e.g., emotional support) but not with the quality of the couples’ relationship with each other. We argue that duocentric networks provide appropriate data for measuring bonding and bridging capital in couple networks.

For many deaths associated with influenza and Omicron infections, those viruses are not detected. We applied previously developed methodology to estimate the contribution of influenza and Omicron infections to all-cause mortality in France for the 2014–2015 through the 2018–2019 influenza seasons, and the period between week 33, 2022 and week 12, 2023. For the 2014–2015 through the 2018–2019 seasons, influenza was associated with annual average of 15,654 (95% CI (13,013, 18,340)) deaths, while between week 33, 2022 and week 12, 2023, we estimated 7,851 (5,213, 10,463) influenza-associated deaths and 32,607 (20,794, 44,496) SARS-CoV-2 associated deaths. For many Omicron-associated deaths for cardiac disease, mental&behavioural disorders, and other causes, Omicron infections are not characterised as a contributing cause of death – for example, between weeks 33–52 in 2022, we estimated 23,983 (15,307, 32,620) SARS-CoV-2-associated deaths in France, compared with 12,811 deaths with COVID-19 listed on death certificate. Our results suggest the need for boosting influenza vaccination coverage in different population groups in France, and for wider detection of influenza infections in respiratory illness episodes (including pneumonia) in combination with the use of antiviral medications. For Omicron epidemics, wider detection of Omicron infections in persons with underlying health conditions is needed.

Traditional techniques for calculating outstanding claim liabilities such as the chain-ladder are notoriously at risk of being distorted by outliers in past claims data. Unfortunately, the literature in robust methods of reserving is scant, with notable exceptions such as Verdonck & Debruyne (2011, Insurance: Mathematics and Economics, 48, 85–98) and Verdonck & Van Wouwe (2011, Insurance: Mathematics and Economics, 49, 188–193). In this paper, we put forward two alternative robust bivariate chain-ladder techniques to extend the approach of Verdonck & Van Wouwe (2011, Insurance: Mathematics and Economics, 49, 188–193). The first technique is based on Adjusted Outlyingness (Hubert & Van der Veeken, 2008. Journal of Chemometrics, 22, 235–246) and explicitly incorporates skewness into the analysis while providing a unique measure of outlyingness for each observation. The second technique is based on bagdistance (Hubert et al., 2016. Statistics: Methodology, 1–23) which is derived from the bagplot; however; it is able to provide a unique measure of outlyingness and a means to adjust outlying observations based on this measure.

Furthermore, we extend our robust bivariate chain-ladder approach to an N-dimensional framework. The implementation of the methods, especially beyond bivariate, is not trivial. This is illustrated on a trivariate data set from Australian general insurers and results under the different outlier detection and treatment mechanisms are compared.

We consider a simple random walk on $\mathbb{Z}^d$ started at the origin and stopped on its first exit time from $({-}L,L)^d \cap \mathbb{Z}^d$. Write L in the form $L = m N$ with $m = m(N)$ and N an integer going to infinity in such a way that $L^2 \sim A N^d$ for some real constant $A \gt 0$. Our main result is that for $d \ge 3$, the projection of the stopped trajectory to the N-torus locally converges, away from the origin, to an interlacement process at level $A d \sigma_1$, where $\sigma_1$ is the exit time of a Brownian motion from the unit cube $({-}1,1)^d$ that is independent of the interlacement process. The above problem is a variation on results of Windisch (2008) and Sznitman (2009).

Copulas provide a powerful and flexible tool for modeling the dependence structure of random vectors, and they have many applications in finance, insurance, engineering, hydrology, and other fields. One well-known class of copulas in two dimensions is the Farlie–Gumbel–Morgenstern (FGM) copula, since its simple analytic shape enables closed-form solutions to many problems in applied probability. However, the classical definition of the high-dimensional FGM copula does not enable a straightforward understanding of the effect of the copula parameters on the dependence, nor a geometric understanding of their admissible range. We circumvent this issue by analyzing the FGM copula from a probabilistic approach based on multivariate Bernoulli distributions. This paper examines high-dimensional exchangeable FGM copulas, a subclass of FGM copulas. We show that the dependence parameters of exchangeable FGM copulas can be expressed as a convex hull of a finite number of extreme points. We also leverage the probabilistic interpretation to develop efficient sampling and estimating procedures and provide a simulation study. Throughout, we discover geometric interpretations of the copula parameters that assist one in decoding the dependence of high-dimensional exchangeable FGM copulas.

Extreme value theory plays an important role in providing approximation results for the extremes of a sequence of independent random variables when their distribution is unknown. An important one is given by the generalised Pareto distribution $H_\gamma(x)$ as an approximation of the distribution $F_t(s(t)x)$ of the excesses over a threshold t, where s(t) is a suitable norming function. We study the rate of convergence of $F_t(s(t)\cdot)$ to $H_\gamma$ in variational and Hellinger distances and translate it into that regarding the Kullback–Leibler divergence between the respective densities.

In Beijing, the capital of China, routine measles mass vaccination has been in place for decades with high coverage; and since the 2000s, catch-up vaccination programmes have been implemented for migrant workers coming to the city. However, measles epidemics in Beijing persisted. Here, we explored the contributing factors of persistent measles transmission in Beijing using an epidemic model in conjunction with a particle filter. Model inputs included data on birth, death, migration, and vaccination. We formulated a series of hypotheses covering the impact of migrant influx, early waning of maternal immunity, and increased mixing among infants; we compared the plausibility of the hypotheses based on model fit to age-grouped, weekly measles incidence data from January 2005 to December 2014, and out-of-fit prediction during 2015–2019. Our best models showed close agreement with the data, and the out-of-fit prediction generally captured the trend of measles incidence from 2015 to 2019. We found that large influx of migrants with considerably higher susceptibility likely contributed to the persistent measles transmission in Beijing. Our findings suggest that stronger catch-up vaccination programmes for migrants may help eliminate measles transmission in Beijing.

Motivated by applications to COVID dynamics, we describe a model of a branching process in a random environment $\{Z_n\}$ whose characteristics change when crossing upper and lower thresholds. This introduces a cyclical path behavior involving periods of increase and decrease leading to supercritical and subcritical regimes. Even though the process is not Markov, we identify subsequences at random time points $\{(\tau_j, \nu_j)\}$—specifically the values of the process at crossing times, viz. $\{(Z_{\tau_j}, Z_{\nu_j})\}$—along which the process retains the Markov structure. Under mild moment and regularity conditions, we establish that the subsequences possess a regenerative structure and prove that the limiting normal distributions of the growth rates of the process in supercritical and subcritical regimes decouple. For this reason, we establish limit theorems concerning the length of supercritical and subcritical regimes and the proportion of time the process spends in these regimes. As a byproduct of our analysis, we explicitly identify the limiting variances in terms of the functionals of the offspring distribution, threshold distribution, and environmental sequences.

Systemic risk measures have been shown to be predictive of financial crises and declines in real activity. Thus, forecasting them is of major importance in finance and economics. In this paper, we propose a new forecasting method for systemic risk as measured by the marginal expected shortfall (MES). It is based on first de-volatilizing the observations and, then, calculating systemic risk for the residuals using an estimator based on extreme value theory. We show the validity of the method by establishing the asymptotic normality of the MES forecasts. The good finite-sample coverage of the implied MES forecast intervals is confirmed in simulations. An empirical application to major U.S. banks illustrates the significant time variation in the precision of MES forecasts, and explores the implications of this fact from a regulatory perspective.

Dog vaccination is the key to controlling rabies in human populations. However, in countries like India, with large free-roaming dog populations, vaccination strategies that rely only on parenteral vaccines are unlikely to be either feasible or successful. Oral rabies vaccines could be used to reach these dogs. We use cost estimates for an Indian city and linear optimisation to find the most cost-effective vaccination strategies. We show that an oral bait handout method for dogs that are never confined can reduce the per dog costs of vaccination and increase vaccine coverage. This finding holds even when baits cost up to 10x the price of parenteral vaccines, if there is a large dog population or proportion of dogs that are never confined. We suggest that oral rabies vaccine baits will be part of the most cost-effective strategies to eliminate human deaths from dog-mediated rabies by 2030.

We consider Poisson hail models and characterize up to boundaries the collection of critical moments which guarantee stability. In particular, we treat the case of infinite speed of propagation.

Real networks often exhibit clustering, the tendency to form relatively small groups of nodes with high edge densities. This clustering property can cause large numbers of small and dense subgraphs to emerge in otherwise sparse networks. Subgraph counts are an important and commonly used source of information about the network structure and function. We study probability distributions of subgraph counts in a community affiliation graph. This is a random graph generated as an overlay of m partly overlapping independent Bernoulli random graphs (layers) $G_1,\dots,G_m$ with variable sizes and densities. The model is parameterised by a joint distribution of layer sizes and densities. When m grows linearly in the number of nodes n, the model generates sparse random graphs with a rich statistical structure, admitting a nonvanishing clustering coefficient and a power-law limiting degree distribution. In this paper we establish the normal and $\alpha$-stable approximations to the numbers of small cliques, cycles, and more general 2-connected subgraphs of a community affiliation graph.

Post COVID-19 condition (PCC) refers to persistent or recurring symptoms (>8 weeks) occurring ≤12 weeks following acute COVID-19. The objective of this systematic review was to assess the evidence on the risk of PCC with vaccination before or after COVID-19 or after developing PCC, and the safety of vaccination among those already experiencing PCC. A search was conducted up to 13 December 2022 and standard systematic review methodology was followed. Thirty-one observational studies were included. There is moderate confidence that two doses of vaccine given pre-infection reduced the odds of PCC (pooled OR (pOR) 0.67, 95% CI 0.60–0.74, I2 = 59.9%), but low confidence that one dose may not reduce the odds (pOR 0.64, 95% CI 0.31#x2013;1.31, I2 = 99.2%), and the evidence is very uncertain about the effect of three doses (pOR 0.45, 95% CI 0.10#x2013;1.99, I2 = 30.9%). One of three studies suggested vaccination shortly after COVID-19 may offer additional protection from developing PCC compared to unvaccinated individuals, but this evidence was very uncertain. For those with PCC, vaccination was not associated with worsening PCC symptoms (10 studies) and appears safe (3 studies), but it is unclear if vaccination may change established PCC symptoms.