Foodborne pathogen Listeria monocytogenes may cause serious, life-threatening disease in susceptible persons. We combined data from Finnish national listeriosis surveillance, patient interview responses, and laboratory data of patient samples and compared them to listeria findings from food and food production plants collected as part of outbreak investigations during 2011–2021. The incidence of invasive listeriosis in Finland (1.3/100000 in 2021) is higher than the EU average (0.5/100000 in 2021), and most cases are observed in the elderly with a predisposing condition. Many cases reported consuming high-risk foods as well as improper food storage. Since ongoing patient interviews and whole genome sequencing were introduced, several listeriosis outbreaks were detected and food sources identified. Recommendations about high-risk foods for listeriosis and proper food storage should be better communicated to susceptible people. In Finland, patient interviews and typing and comparing listeria isolates in foods and patient samples are crucial in solving outbreaks and determining measures to control invasive listeriosis.

Let X be a d-dimensional diffusion and M the running supremum of its first component. In this paper, we show that for any $t>0,$ the density (with respect to the $(d+1)$-dimensional Lebesgue measure) of the pair $\big(M_t,X_t\big)$ is a weak solution of a Fokker–Planck partial differential equation on the closed set $\big\{(m,x)\in \mathbb{R}^{d+1},\,{m\geq x^1}\big\},$ using an integral expansion of this density.

Homeless shelter residents and staff may be at higher risk of SARS-CoV-2 infection. However, SARS-CoV-2 infection estimates in this population have been reliant on cross-sectional or outbreak investigation data. We conducted routine surveillance and outbreak testing in 23 homeless shelters in King County, Washington, to estimate the occurrence of laboratory-confirmed SARS-CoV-2 infection and risk factors during 1 January 2020–31 May 2021. Symptom surveys and nasal swabs were collected for SARS-CoV-2 testing by RT-PCR for residents aged ≥3 months and staff. We collected 12,915 specimens from 2,930 unique participants. We identified 4.74 (95% CI 4.00–5.58) SARS-CoV-2 infections per 100 individuals (residents: 4.96, 95% CI 4.12–5.91; staff: 3.86, 95% CI 2.43–5.79). Most infections were asymptomatic at the time of detection (74%) and detected during routine surveillance (73%). Outbreak testing yielded higher test positivity than routine surveillance (2.7% versus 0.9%). Among those infected, residents were less likely to report symptoms than staff. Participants who were vaccinated against seasonal influenza and were current smokers had lower odds of having an infection detected. Active surveillance that includes SARS-CoV-2 testing of all persons is essential in ascertaining the true burden of SARS-CoV-2 infections among residents and staff of congregate settings.

Although compliance scales have been used to assess compliance with health guidelines to reduce the spread of COVID-19, no scale known to us has shown content validity regarding global guidelines and reliability across an international sample. We assessed the validity and reliability of a Compliance Scale developed by a group of over 150 international researchers. Exploratory factor analysis determined reliable items on the English version. Confirmatory factor analysis confirmed the reliability of the six-item scale and convergent validity was found. After invariance testing and alignment, we employed a novel R code to run a Monte Carlo simulation for alignment validation. This scale can be employed to measure compliance across multiple languages, and our alignment validation method can be conducted with future cross-language surveys.

This study aimed to analyse the seroprevalence of SARS-CoV-2 in Kazakhstan. This is a cross-sectional study of adult population in Kazakhstan for the period from October 2021 to May 2022. For the study, 6 720 people aged 18 to 69 were recruited (from 17 regions). The demographic data were collected and analysed. Gender was evenly distributed (males 49.9%, females 50.1%). Women exhibited a higher seroprevalence than men (IgM 20.7% vs 17.9% and IgG 46.1% vs 41.5%). The highest prevalence of IgM was found in the age group of 30–39. However, the highest prevalence of IgG was detected in the age group of 60–69. The seroprevalence of IgG increased across all groups (from 39.7% in 18–29 age groups to 53.1% in 60–69 age groups). The odds for a positive test were significantly increased in older age groups 50–59 (p < 0.0001) and 60–69 (p < 0.0001). The odds of a positive test were 1.12 times higher in females compared to males (p = 0.0294). The odds for a positive test were significantly higher in eight regions (Astana, Akmola, Atyrau, Western Kazakhstan region, Kostanai, Turkestan, Eastern Kazakhstan region, and Shymkent) compared to Almaty city. The odds of a positive test were three times higher in Astana and the Western Kazakhstan region than in Almaty city. In urban areas, the odds of a positive test were 0.75 times lower than in rural areas (p < 0.0001). The study’s results showed an adequate level of seroprevalence (63%) that exceeds the essential minimum of herd immunity indicators in the country. There was significant geographic variability with a higher prevalence of IgG/IgM antibodies to SARS-CoV-2 in rural areas.

Given $\alpha \gt 0$ and an integer $\ell \geq 5$, we prove that every sufficiently large $3$-uniform hypergraph $H$ on $n$ vertices in which every two vertices are contained in at least $\alpha n$ edges contains a copy of $C_\ell ^{-}$, a tight cycle on $\ell$ vertices minus one edge. This improves a previous result by Balogh, Clemen, and Lidický.

Bovine tuberculosis (bTB) is a chronic, zoonotic infection of domestic and wild animals caused mainly by Mycobacterium bovis. The Test and Vaccinate or Remove (TVR) project was a 5-year intervention (2014–2018) applied to Eurasian badgers (Meles meles) in a 100 km2 area of County Down, Northern Ireland. This observational study used routine bTB surveillance data of cattle to determine if the TVR intervention had any effect in reducing the infection at a herd level. The study design included the TVR treatment area (Banbridge) compared to the three adjacent 100 km2 areas (Dromore, Ballynahinch, and Castlewellan) which did not receive any badger intervention. Results showed that there were statistically lower bTB herd incidence rate ratios in the Banbridge TVR area compared to two of the other three comparison areas, but with bTB herd history and number of bTB infected cattle being the main explanatory variables along with Year. This finding is consistent with other study results conducted as part of the TVR project that suggested that the main transmission route for bTB in the area was cattle-to-cattle spread. This potentially makes any wildlife intervention in the TVR area of less relevance to bTB levels in cattle. It must also be noted that the scientific power of the TVR study (76%) was below the recommended 80%, meaning that results must be interpreted with caution. Even though statistical significance was achieved in two cattle-related risk factors, other potential risk factors may have also demonstrated significance in a larger study.

We prove existence and uniqueness for the inverse-first-passage time problem for soft-killed Brownian motion using rather elementary methods relying on basic results from probability theory only. We completely avoid the relation to a suitable partial differential equation via a suitable Feynman–Kac representation, which was previously one of the main tools.

The aim of this study is to analyse the changing patterns in the transmission of COVID-19 in relation to changes in Vietnamese governmental policies, based on epidemiological data and policy actions in a large Vietnamese province, Bac Ninh, in 2021. Data on confirmed cases from January to December 2021 were collected, together with policy documents. There were three distinct periods of the COVID-19 pandemic in Bac Ninh province during 2021. During the first period, referred to as the ‘Zero-COVID’ period (01/04–07/04/2021), there was a low population vaccination rate, with less than 25% of the population receiving its first vaccine dose. Measures implemented during this period focused on domestic movement restrictions, mask mandates, and screening efforts to control the spread of the virus. The subsequent period, referred to as the ‘Transition’ period (07/05–10/22/2021), witnessed a significant increase in population vaccination coverage, with 80% of the population receiving their first vaccine dose. During this period, several days passed without any reported COVID-19 cases in the community. The local government implemented measures to manage domestic actions and reduce the time spent in quarantine, and encouraged home quarantining for the close contacts of cases with COVID-19. Finally, the ‘New-normal’ stage (10/23–12/31/2021), during which the population vaccination coverage with a second vaccine dose increased to 70%, and most of the mandates for the prevention and control of COVID-19 were reduced. In conclusion, this study highlights the importance of governmental policies in managing and controlling the transmission of COVID-19 and provides insights for developing realistic and context-specific strategies in similar settings.

This paper investigates an operation mechanism for mutual aid platforms to develop more sustainably and profitably. A mutual aid platform is an online risk-sharing platform for risk-heterogeneous participants, and the platform extracts revenues by charging participants commission and subscription fees. A modeling framework is proposed to identify the optimal commissions and subscriptions for mutual aid platforms. Participants are divided into different types based on their loss probabilities and values derived from the platform. We present how these commissions and subscriptions should be set in a mutual aid plan to maximize the platform’s revenues. Our analysis emphasized the importance of accounting for risk heterogeneity in mutual aid platforms. Specifically, different types of participants should be charged different commissions/subscriptions depending on their loss probabilities and values on the platform. Participants’ shared costs should be determined based on their loss probabilities. Adverse selection occurs on the platform if participants with different risks pay the same shared costs. Our results also show that the platform’s maximum revenue will be lower if the platform charges the same fee to all participants. The numerical results of a practical example illustrate that the optimal commission/subscription scheme and risk-sharing rule result in considerable improvements in platform revenue over the current scheme implemented by the platform.

An outbreak of SARS-CoV-2 was confirmed after an academic party in Helsinki, Finland, in 2022. All 70 guests were requested to fill in follow-up questionnaires; serologic analyses and whole-genome sequencing (WGS) were conducted when possible.

Of those participating – all but one with ≥3 vaccine doses – 21/53 (40%) had test-confirmed symptomatic COVID-19: 7% of those with earlier episodes and 76% of those without. Half (11/21) were febrile, but none needed hospitalisation. WGS revealed subvariant BA.2.23.

Compared to vaccination alone, our data suggest remarkable protection by hybrid immunity against symptomatic infection, particularly in instances of recent infections with homologous variants.

Consider a Brownian motion on the circumference of the unit circle, which jumps to the opposite point of the circumference at incident times of an independent Poisson process of rate $\lambda$. We examine the problem of coupling two copies of this ‘jumpy Brownian motion’ started from different locations, so as to optimise certain functions of the coupling time. We describe two intuitive co-adapted couplings (‘Mirror’ and ‘Synchronous’) which differ only when the two processes are directly opposite one another, and show that the question of which strategy is best depends upon the jump rate $\lambda$ in a non-trivial way. We also provide an explicit description of a (non-co-adapted) maximal coupling for any jump rate in the case that the two jumpy Brownian motions begin at antipodal points of the circle.

Undirected, binary network data consist of indicators of symmetric relations between pairs of actors. Regression models of such data allow for the estimation of effects of exogenous covariates on the network and for prediction of unobserved data. Ideally, estimators of the regression parameters should account for the inherent dependencies among relations in the network that involve the same actor. To account for such dependencies, researchers have developed a host of latent variable network models; however, estimation of many latent variable network models is computationally onerous and which model is best to base inference upon may not be clear. We propose the probit exchangeable (PX) model for undirected binary network data that is based on an assumption of exchangeability, which is common to many of the latent variable network models in the literature. The PX model can represent the first two moments of any exchangeable network model. We leverage the EM algorithm to obtain an approximate maximum likelihood estimator of the PX model that is extremely computationally efficient. Using simulation studies, we demonstrate the improvement in estimation of regression coefficients of the proposed model over existing latent variable network models. In an analysis of purchases of politically aligned books, we demonstrate political polarization in purchase behavior and show that the proposed estimator significantly reduces runtime relative to estimators of latent variable network models, while maintaining predictive performance.

We use Stein’s method to establish the rates of normal approximation in terms of the total variation distance for a large class of sums of score functions of samples arising from random events driven by a marked Poisson point process on $\mathbb{R}^d$. As in the study under the weaker Kolmogorov distance, the score functions are assumed to satisfy stabilisation and moment conditions. At the cost of an additional non-singularity condition, we show that the rates are in line with those under the Kolmogorov distance. We demonstrate the use of the theorems in four applications: Voronoi tessellations, k-nearest-neighbours graphs, timber volume, and maximal layers.

We consider infinitely wide multi-layer perceptrons (MLPs) which are limits of standard deep feed-forward neural networks. We assume that, for each layer, the weights of an MLP are initialized with independent and identically distributed (i.i.d.) samples from either a light-tailed (finite-variance) or a heavy-tailed distribution in the domain of attraction of a symmetric $\alpha$-stable distribution, where $\alpha\in(0,2]$ may depend on the layer. For the bias terms of the layer, we assume i.i.d. initializations with a symmetric $\alpha$-stable distribution having the same $\alpha$ parameter as that layer. Non-stable heavy-tailed weight distributions are important since they have been empirically seen to emerge in trained deep neural nets such as the ResNet and VGG series, and proven to naturally arise via stochastic gradient descent. The introduction of heavy-tailed weights broadens the class of priors in Bayesian neural networks. In this work we extend a recent result of Favaro, Fortini, and Peluchetti (2020) to show that the vector of pre-activation values at all nodes of a given hidden layer converges in the limit, under a suitable scaling, to a vector of i.i.d. random variables with symmetric $\alpha$-stable distributions, $\alpha\in(0,2]$.

We consider a class of weakly interacting particle systems of mean-field type. The interactions between the particles are encoded in a graph sequence, i.e. two particles are interacting if and only if they are connected in the underlying graph. We establish a law of large numbers for the empirical measure of the system that holds whenever the graph sequence is convergent to a graphon. The limit is the solution of a non-linear Fokker–Planck equation weighted by the (possibly random) graphon limit. In contrast with the existing literature, our analysis focuses on both deterministic and random graphons: no regularity assumptions are made on the graph limit and we are able to include general graph sequences such as exchangeable random graphs. Finally, we identify the sequences of graphs, both random and deterministic, for which the associated empirical measure converges to the classical McKean–Vlasov mean-field limit.

We develop a novel Monte Carlo algorithm for the vector consisting of the supremum, the time at which the supremum is attained, and the position at a given (constant) time of an exponentially tempered Lévy process. The algorithm, based on the increments of the process without tempering, converges geometrically fast (as a function of the computational cost) for discontinuous and locally Lipschitz functions of the vector. We prove that the corresponding multilevel Monte Carlo estimator has optimal computational complexity (i.e. of order $\varepsilon^{-2}$ if the mean squared error is at most $\varepsilon^2$) and provide its central limit theorem (CLT). Using the CLT we construct confidence intervals for barrier option prices and various risk measures based on drawdown under the tempered stable (CGMY) model calibrated/estimated on real-world data. We provide non-asymptotic and asymptotic comparisons of our algorithm with existing approximations, leading to rule-of-thumb principles guiding users to the best method for a given set of parameters. We illustrate the performance of the algorithm with numerical examples.