Is it always beneficial to create a new relationship (have a new follower/friend) in a social network? This question can be formally stated as a property of the centrality measure that defines the importance of the actors of the network. Score monotonicity means that adding an arc increases the centrality score of the target of the arc; rank monotonicity means that adding an arc improves the importance of the target of the arc relatively to the remaining nodes. It is known that most centralities are both score and rank monotone on directed, strongly connected graphs. In this paper, we study the problem of score and rank monotonicity for classical centrality measures in the case of undirected networks: in this case, we require that score, or relative importance, improves at both endpoints of the new edge. We show that, surprisingly, the situation in the undirected case is very different, and in particular that closeness, harmonic centrality, betweenness, eigenvector centrality, Seeley’s index, Katz’s index, and PageRank are not rank monotone; betweenness and PageRank are not even score monotone. In other words, while it is always a good thing to get a new follower, it is not always beneficial to get a new friend.

Genital human papillomavirus (HPV) infections are caused by a broad diversity of genotypes. As available vaccines target a subgroup of these genotypes, monitoring transmission dynamics of nonvaccine genotypes is essential. After reviewing the epidemiological literature on study designs aiming to monitor those dynamics, we evaluated their abilities to detect HPV-prevalence changes following vaccine introduction. We developed an agent-based model to simulate HPV transmission in a heterosexual population under various scenarios of vaccine coverage and genotypic interaction, and reproduced two study designs: post-vs.-prevaccine and vaccinated-vs.-unvaccinated comparisons. We calculated the total sample size required to detect statistically significant prevalence differences at the 5% significance level and 80% power. Although a decrease in vaccine-genotype prevalence was detectable as early as 1 year after vaccine introduction, simulations indicated that the indirect impact on nonvaccine-genotype prevalence (a decrease under synergistic interaction or an increase under competitive interaction) would only be measurable after >10 years whatever the vaccine coverage. Sample sizes required for nonvaccine genotypes were >5 times greater than for vaccine genotypes and tended to be smaller in the post-vs.-prevaccine than in the vaccinated-vs.-unvaccinated design. These results highlight that previously published epidemiological studies were not powerful enough to efficiently detect changes in nonvaccine-genotype prevalence.

There is limited research on whether inequalities exist among individuals from different ethnicities and deprivation status among enteric fever cases. The aim of the study was to investigate the association between the enteric fever incidence rates, ethnicity and deprivation for enteric fever cases in England. Additionally, it was assessed if ethnicity and deprivation were associated with symptom severity, hospital admission and absence from school/work using logistic regression models. Incidence rates were higher in the two most deprived index of multiple deprivation quintiles and those of Pakistani ethnicity (9.89, 95% CI 9.08–10.75) followed by Indian (7.81, 95% CI 7.18–8.49) and Bangladeshi (5.68, 95% CI 4.74–6.76) groups: the incidence rate in the White group was 0.07 (95% CI 0.06–0.08). Individuals representing Pakistani (3.00, 95% CI 1.66–5.43), Indian (2.05, 95% CI 1.18–3.54) and Other/Other Asian (3.51, 95% CI 1.52–8.14) ethnicities had significantly higher odds of hospital admission than individuals representing White (British/Other) ethnicity, although all three groups had statistically significantly lower symptom severity scores. Our results show that there are significant ethnic and socioeconomic inequalities in enteric fever incidence that should inform prevention and treatment strategies. Targeted, community-specific public health interventions are needed to impact on overall burden.

Wastewater surveillance and quantitative analysis of SARS-CoV-2 RNA are increasingly used to monitor the spread of COVID-19 in the community. We studied the feasibility of applying the surveillance data for early detection of local outbreaks. A Monte Carlo simulation model was constructed, applying data on reported variation in RNA gene copy concentration in faeces and faecal masses shed. It showed that, even with a constant number of SARS-CoV-2 RNA shedders, the variation in concentrations found in wastewater samples will be large, and that it will be challenging to translate viral concentrations into incidence estimates, especially when the number of shedders is low. Potential signals for early detection of hypothetical outbreaks were analysed for their performance in terms of sensitivity and specificity of the signals. The results suggest that a sudden increase in incidence is not easily identified on the basis of wastewater surveillance data, especially in small sampling areas and in low-incidence situations. However, with a high number of shedders and when combining data from multiple consecutive tests, the performance of wastewater sampling is expected to improve considerably. The developed modelling approach can increase our understanding of the results from wastewater surveillance of SARS-CoV-2.

Data on coronavirus disease 2019 (COVID-19) prevalence in the Democratic Republic of Congo are scarce. We conducted a cross-sectional study to determine the seroprevalence of antibodies against anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the slum of Kadutu, city of Bukavu, between June and September 2021. The survey participants were all unvaccinated against SARS-CoV-2. The crude seroprevalence rate was adjusted to the known characteristics of the assay. Participants aged 15–49 years old made up 80% of the population enrolled in the study (n = 507; 319 women and 188 men). The overall crude and adjusted seroprevalence rates of antibodies for COVID-19 were 59.7% (95% CI 55.4–63.9%) and 84.0% (95% CI 76.2–92.4%), respectively. This seroprevalence rate indicates widespread dissemination of SARS-CoV-2 in these communities. COVID-19 symptoms were either absent or mild in more than half of the participants with antibodies for COVID-19 and none of the participants with antibodies for COVID-19 required hospitalisation. These results suggest that SARS-CoV-2 spread did not appear to be associated with severe symptoms in the population of these settlements and that many cases went unreported in these densely populated locations. The relevance of vaccination in these communities should be thoroughly investigated.

This article examines the impact of the largest claims reinsurance treaties on loss reserve of the ceding company. The largest claims reinsurance, known as LCR, and ECOMOR reinsurance treaties are considered to be the two most appropriate reinsurance treaties for large or catastrophe claims. Then, it studies the impact of such treaties on loss reserves. Through a simulation study, it shown that, under a more general situation, the LCR treaty can be a more efficient (in some sense, see below) treaty than the ECOMOR treaty for the ceding company.

We clarify and refine the definition of a reciprocal random field on an undirected graph, with the reciprocal chain as a special case, by introducing four new properties: the factorizing, global, local, and pairwise reciprocal properties, in decreasing order of strength, with respect to a set of nodes $\delta$. They reduce to the better-known Markov properties if $\delta$ is the empty set, or, with the exception of the local property, if $\delta$ is a complete set. Conditions for each reciprocal property to imply the next stronger property are derived, and it is shown that, conditionally on the values at a set of nodes $\delta_0$, all four properties are preserved for the subgraph induced by the remaining nodes, with respect to the node set $\delta\setminus\delta_0$. We note that many of the above results are new even for reciprocal chains.

The Ending the HIV Epidemic initiative aims to decrease new HIV infections and promote test-and-treat strategies. Our aims were to establish a baseline of HIV outcomes among newly diagnosed PWH in Washington, DC (DC), a ‘hotspot’ for the HIV epidemic. We also examined sociodemographic and clinical factors associated with retention in care (RIC), antiretroviral therapy (ART) initiation and viral suppression (VS) among newly diagnosed PWH in the DC Cohort from 2011–2016. Among 455 newly diagnosed participants, 92% were RIC at 12 months, ART was initiated in 65% at 3 months and 91% at 12 months, VS in at least 17% at 3 months and 82% at 12 months and 55% of those with VS at 12 months had sustained VS for an additional 12 months. AIDS diagnosis was associated with RIC (aOR 2.99; 1.13–2.28), ART initiation by 3 months (aOR 2.58; 1.61–4.12) and VS by 12 months (aOR4.87; 1.69–14.03). This analysis contributes to our understanding of the HIV treatment dynamics of persons with recently diagnosed HIV infection in a city with a severe HIV epidemic.

In this study, we consider a class of multiple-drawing opposite-reinforcing urns with time-dependent replacement rules. The class has the symmetric property of a Friedman-type urn. We divide the class into a small-increment regime and a large-increment regime. For small-increment schemes, we prove almost-sure convergence and a central limit theorem for the proportion of white balls by stochastic approximation. For large-increment schemes, by assuming the affinity condition, we show almost-sure convergence of the proportion of white balls by martingale theory and present a way to identify the limit distribution of the proportion of white balls.

The introduction of pneumococcal conjugate vaccines (PCV) into the childhood vaccination programme has reduced invasive pneumococcal disease (IPD). Although anticipated from data elsewhere, surveillance in Ireland has confirmed reductions in IPD amongst those ⩾65 years of age due to a decline of PCV serotypes in this age group. Currently, direct protection against IPD in the elderly is focused on immunisation with the 23-valent pneumococcal polysaccharide vaccine (PPV23). However, immunity may not be as effective as with PCV and, furthermore, PPV23 uptake is poor in Ireland. Hence, consideration should be given to providing a PCV to this age group.

Data assimilation of flow measurements is an essential tool for extracting information in fluid dynamics problems. Recent works have shown that the physics-informed neural networks (PINNs) enable the reconstruction of unsteady fluid flows, governed by the Navier–Stokes equations, if the network is given enough flow measurements that are appropriately distributed in time and space. In many practical applications, however, experimental measurements involve only time-averaged quantities or their higher order statistics which are governed by the under-determined Reynolds-averaged Navier–Stokes (RANS) equations. In this study, we perform PINN-based reconstruction of time-averaged quantities of an unsteady flow from sparse velocity data. The applied technique leverages the time-averaged velocity data to infer unknown closure quantities (curl of unsteady RANS forcing), as well as to interpolate the fields from sparse measurements. Furthermore, the method’s capabilities are extended further to the assimilation of Reynolds stresses where PINNs successfully interpolate the data to complete the velocity as well as the stresses fields and gain insight into the pressure field of the investigated flow.

David Gibson’s (2008) examination of research on conversational interaction highlighted methodological and theoretical gaps in current understanding – particularly around the localized construction of interaction and the reproduction of social structures. This paper extends extant formal models used by group process researchers to explain how exogenous status structures shape local interaction by incorporating insights from qualitative work examining the local production of conversational interaction. Relational events serve as a bridge between conversation analytic understandings of the deep structure of conversation and expectation states formal models of permeation. We propose a theoretical integration of the status organizing process (permeation) and local turn-taking rules (deep structure) as a more complete model of conversational behavior in task groups. We test a formalized construction of this preliminary theory by examining turn-taking using data from 55 task groups whose members vary in gender, authority, and legitimacy of that authority. This integrated model offers substantial improvements in prediction accuracy over using status information alone. We then propose ways to expand the integrated theoretical framework to advance current understandings of action and events in conversation. Finally, we offer suggestions for insights from group processes theories that could be incorporated into network models of interaction outside of this theoretical framework.

The COVID‑19 pandemic has increased the popularity of online shopping, and companies are looking for ways to provide consumers with experiences that online shopping cannot provide, such as touching products and imagining them in use. In this context, the importance of haptic imagery of products showcased online is increasing. This study replicated and extended Peck et al.’s (2013, Journal of Consumer Psychology, 23, 189–196) finding that physical control and psychological ownership mediate the influence of haptic imagery on purchase intention. This study showed that imagining touching a product increased purchase intention through the mediation of physical control and psychological ownership compared with not imagining, conceptually replicating Peck et al.’s study. This study also examined the moderating effect of product involvement and showed that there was no moderator role of product involvement. The findings would have a practical application in marketing, such as encouraging consumers to imagine touching the product.

Numerous works have been proposed to generate random graphs preserving the same properties as real-life large-scale networks. However, many real networks are better represented by hypergraphs. Few models for generating random hypergraphs exist, and also, just a few models allow to both preserve a power-law degree distribution and a high modularity indicating the presence of communities. We present a dynamic preferential attachment hypergraph model which features partition into communities. We prove that its degree distribution follows a power-law, and we give theoretical lower bounds for its modularity. We compare its characteristics with a real-life co-authorship network and show that our model achieves good performances. We believe that our hypergraph model will be an interesting tool that may be used in many research domains in order to reflect better real-life phenomena.

In this paper, we examine the contribution of Network Science journal to the network science discipline. We do so from two perspectives. First, expanding the existing taxonomy of article contribution, we examine trends in theory testing, theory building, and new method development within the journal’s articles. We find that the journal demands a high level of theoretical contribution and methodological rigor. High levels of theoretical and methodological contribution become significant predictors of article citation rates. Second, we look at the composition of the studies in Network Science and determine that the journal has already established a solid “hard core” for the new discipline.

We consider a two-stage service system with two types of servers, namely subordinates who perform the first-stage service and supervisors who have their own responsibilities in addition to collaborating with the subordinates on the second-stage service. Rewards are earned when first- or second-stage service is completed and when supervisors finish one of their own responsibilities. Costs are incurred when impatient customers abandon without completing the second-stage service. Our problem is to determine how the supervisors should distribute their time between their joint work with the subordinates and their own responsibilities. Under the assumptions that service times at both stages are exponentially distributed and that the customers waiting for second-stage service abandon after an exponential amount of time, we prove that one of two policies will maximize the long-run average profit. Namely, it is optimal for supervisors to start collaborating with subordinates either when subordinates can no longer serve new customers or as soon as there is a customer ready for second-stage service. Furthermore, we show that the optimality condition is a simple threshold on the system parameters. We conclude by proving that pooling supervisors (and their associated subordinates) improves system performance, but with limited returns as more supervisors are pooled.

A stream of research on co-authorship, used as a proxy of scholars’ collaborative behavior, focuses on members of a given scientific community defined at discipline and/or national basis for which co-authorship data have to be retrieved. Recent literature pointed out that international digital libraries provide partial coverage of the entire scholar scientific production as well as under-coverage of the scholars in the community. Bias in retrieving co-authorship data of the community of interest can affect network construction and network measures in several ways, providing a partial picture of the real collaboration in writing papers among scholars. In this contribution, we collected bibliographic records of Italian academic statisticians from an online platform (IRIS) available at most universities. Even if it guarantees a high coverage rate of our population and its scientific production, it is necessary to deal with some data quality issues. Thus, a web scraping procedure based on a semi-automatic tool to retrieve publication metadata, as well as data management tools to detect duplicate records and to reconcile authors, is proposed. As a result of our procedure, it emerged that collaboration is an active and increasing practice for Italian academic statisticians with some differences according to the gender, the academic ranking, and the university location of scholars. The heuristic procedure to accomplish data quality issues in the IRIS platform can represent a working case report to adapt to other bibliographic archives with similar characteristics.

For a quadratic Markov branching process (QMBP), we show that the decay parameter is equal to the first eigenvalue of a Sturm–Liouville operator associated with the partial differential equation that the generating function of the transition probability satisfies. The proof is based on the spectral properties of the Sturm–Liouville operator. Both the upper and lower bounds of the decay parameter are given explicitly by means of a version of Hardy’s inequality. Two examples are provided to illustrate our results. The important quantity, the Hardy index, which is closely linked to the decay parameter of the QMBP, is deeply investigated and estimated.