Unobserved heterogeneous treatment effects have been emphasized in the recent policy evaluation literature (see, e.g., Heckman and Vytlacil (2005, Econometrica 73, 669–738)). This paper proposes a nonparametric test for unobserved heterogeneous treatment effects in a treatment effect model with a binary treatment assignment, allowing for individuals’ self-selection to the treatment. Under the standard local average treatment effects assumptions, i.e., the no defiers condition, we derive testable model restrictions for the hypothesis of unobserved heterogeneous treatment effects. Furthermore, we show that if the treatment outcomes satisfy a monotonicity assumption, these model restrictions are also sufficient. Then, we propose a modified Kolmogorov–Smirnov-type test which is consistent and simple to implement. Monte Carlo simulations show that our test performs well in finite samples. For illustration, we apply our test to study heterogeneous treatment effects of the Job Training Partnership Act on earnings and the impacts of fertility on family income, where the null hypothesis of homogeneous treatment effects gets rejected in the second case but fails to be rejected in the first application.

Influenza virus infections can lead to a number of secondary complications, including sepsis. We applied linear regression models to mortality and hospital admission data coded for septicaemia from 1998 to 2019 in Hong Kong, and estimated that septicaemia was associated with an annual average excess mortality rate of 0.23 (95% CI 0.04–0.40) per 100 000 persons per year and an excess septicaemia hospitalisation rate of 1.73 (95% CI 0.94–2.50) per 100 000 persons per year. The highest excess morbidity and mortality was found in older adults and young children, and during influenza A(H3N2) epidemics.

In Cameroon, >90% of cattle are considered exposed to African animal trypanosomiasis (AAT) infection, with the presence of tsetse rendering cattle husbandry as a very difficult proposition. A systematic review of data on AAT and tsetse from 1990 to 2021 was conducted to develop a national atlas. The review identified 74 relevant scientific documents, with three pathogenic Trypanosoma species (Trypanosoma vivax, T. congolense and T. brucei s.l.) most frequently identified as causing AAT. Trypanosoma grayi, T. theileri, T. simiae and the human African trypanosomiasis causative agent T. brucei gambiense were also identified in a wide range of hosts. The tsetse fly fauna of Cameroon comprises nine species, with Glossina palpalis palpalis and G. fuscipes fuscipes the most widely distributed following their identification in seven and five of the 10 regions, respectively. Two species, Glossina nigrofusca and G. pallicera pallicera appeared to be rare and were restricted to both forest and protected areas. The presence of AAT is associated with the presence of tsetse in the livestock–human–wildlife interface of Cameroon. AAT occurs beyond the tsetse belts of the country where mechanical vectors are abundant. This study provides AAT and tsetse maps to support ongoing interventions in Cameroon.

The molecular properties of the circulating causative agents of hand, foot and mouth disease (HFMD) in Wuxi remain unclear, posing diagnostic and prevention challenges. Additionally, in several regions of mainland China, the EV71 immunisation drastically reduced related cases and altered the HFMD pathogen spectrum, while the precise situation in Wuxi remained unknown. To address these issues, paediatric HFMD cases diagnosed in the clinic were enrolled and anal swabs were acquired in the spring of 2019. The 5′-UTR and VP1 genes were interpreted using RT-nPCR with degenerate primers to confirm their genotypes. Following that, the entire genome sequences of each viral type were recovered, allowing for the interpretation of several molecular properties. A total of 249 clinically confirmed HFMD cases had their anal swabs taken for viral identification, from which the genome sequences of seven genotypes were recovered. Coxsackievirus A16 is the most prevalent type, followed by Coxsackievirus A6, A10, A2, A4, A5 and Echovirus 11, all of which were genetically determined for the first time in Wuxi. Phylogenetic and recombination analyses were used to evaluate their evolutionary relationships with other strains found in other regions. Noticeably, a CVA16 subtype, responsible for a large proportion of the observed cases, phylogenetically clustered within clade B1a along with some strains from other countries, was the first one to be reported in China. Furthermore, some recombination events were inferred from strains detected in sporadic cases, particularly the recombination between CVA2 and CVA5 strains. Our investigation elucidated the multiple molecular characteristics of the HFMD causal enterovirus strains in Wuxi, underlining the potential hazards associated with these circulating viral types in the population and aiding in future surveillance and prevention of this disease.

We develop theoretical finite-sample results concerning the size of wild bootstrap-based heteroskedasticity robust tests in linear regression models. In particular, these results provide an efficient diagnostic check, which can be used to weed out tests that are unreliable for a given testing problem in the sense that they overreject substantially. This allows us to assess the reliability of a large variety of wild bootstrap-based tests in an extensive numerical study.

This paper investigates risk aggregation and capital allocation problems for an insurance portfolio consisting of several lines of business. The class of multivariate INAR(1) processes is proposed to model different sources of dependence between the number of claims of the portfolio. The total capital required for the whole portfolio is evaluated under the TVaR risk measure, and the contribution of each line of business is derived under the TVaR-based allocation rule. We provide the risk aggregation and capital allocation formulas in the general case of continuous and strictly positive claim sizes and then in the case of mixed Erlang claim sizes. The impact of both time dependence and cross-dependence on the behavior of risk aggregation and capital allocation is numerically illustrated.

Data mining and knowledge discovery (DMKD) focuses on extracting useful information from data. In the chemical process industry, tasks such as process monitoring, fault detection, process control, optimization, etc., can be achieved using DMKD. However, the selection of the appropriate method for each step in the DMKD process, namely data cleaning, sampling, scaling, dimensionality reduction (DR), clustering, clustering analysis and data visualization to obtain meaningful insights is far from trivial. In this contribution, a computational environment (FastMan) is introduced and used to illustrate how method selection affects DMKD in chemical process data. Two case studies, using data from a simulated natural gas liquid plant and real data from an industrial pyrolysis unit, were conducted to demonstrate the applicability of these methodologies in real-life scenarios. Sampling and normalization methods were found to have a great impact on the quality of the DMKD results. Also, a neighbor graphs method for DR, t-distributed stochastic neighbor embedding, outperformed principal component analysis, a matrix factorization method frequently used in the chemical process industry for identifying both local and global changes.

We study two models of an age-biased graph process: the $\delta$-version of the preferential attachment graph model (PAM) and the uniform attachment graph model (UAM), with m attachments for each of the incoming vertices. We show that almost surely the scaled size of a breadth-first (descendant) tree rooted at a fixed vertex converges, for $m=1$, to a limit whose distribution is a mixture of two beta distributions and a single beta distribution respectively, and that for $m>1$ the limit is 1. We also analyze the likely performance of two greedy (online) algorithms, for a large matching set and a large independent set, and determine – for each model and each greedy algorithm – both a limiting fraction of vertices involved and an almost sure convergence rate.

Predicting the occurrence of thermoacoustic instabilities is of major interest in a variety of engineering applications such as aircraft propulsion, power generation, and industrial heating. Predictive methodologies based on a physical approach have been developed in the past decades, but have a moderate-to-high computational cost when exploring a large number of designs. In this study, the stability prediction capabilities and computational cost of four well-established classification algorithms—the K-Nearest Neighbors, Decision Tree (DT), Random Forest (RF), and Multilayer Perceptron (MLP) algorithms—are investigated. These algorithms are trained using an in-house physics-based low-order network model tool called OSCILOS. All four algorithms are able to predict which configurations are thermoacoustically unstable with a very high accuracy and a very low runtime. Furthermore, the frequency intervals containing unstable modes for a given configuration are also accurately predicted using multilabel classification. The RF algorithm correctly predicts the overall stability and finds all frequency intervals containing unstable modes for 99.6 and 98.3% of all configurations, respectively, which makes it the most accurate algorithm when a large number of training examples is available. For smaller training sets, the MLP algorithm becomes the most accurate algorithm. The DT algorithm is found to be slightly less accurate, but can be trained extremely quickly and runs about a million times faster than a traditional physics-based low-order network model tool. These findings could be used to devise a new generation of combustor optimization tools that would run much faster than existing codes while retaining a similar accuracy.

We study the Wiener disorder detection problem where each observation is associated with a positive cost. In this setting, a strategy is a pair consisting of a sequence of observation times and a stopping time corresponding to the declaration of disorder. We characterize the minimal cost of the disorder problem with costly observations as the unique fixed point of a certain jump operator, and we determine the optimal strategy.

In many nonlinear panel data models with fixed effects maximum likelihood estimators suffer from the incidental parameters problem, which often entails that point estimates are markedly biased. While the recent literature has mostly generated methods that yield a first-order bias reduction relative to maximum likelihood, we derive a first- and second-order bias correction of the profile likelihood based on “expected quantities” which differs from the corresponding correction based on “sample averages” derived in Dhaene and Sun (2021, Journal of Econometrics 220, 227–252). While consistency and asymptotic normality of our estimator are derived in a setting where both the number of individuals and the number of time periods grow to infinity, we illustrate in a simulation study that our second-order bias reduction indeed yields an estimator with substantially improved small sample properties relative to its first-order unbiased counterpart, especially when less than 10 time periods are available.

Health care workers (HCWs) are in a higher risk of acquiring the disease owing to their regular contact with the patients. The aim of this study is to evaluate the seroprevalence among HCWs pre- and post-vaccination. The serological assessment of anti-SARS-CoV-2 antibody was conducted in pre- and post-vaccination of first or both doses of the ChAdOx1 nCoV-19 vaccine and followed up to 8 months for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and antibody titre. The neutralising antibody was positively correlated with IgG and total antibody. IgG was significantly decreased after 4–6 months post-infection. Almost all HCWs developed IgG after 2 doses of vaccine with comparable IgG to that of the infected HCWs. A follow-up of 6 to 8 months post vaccination showed a significant drop in antibody titre, while 56% of them didn't show a detectable level of IgG, suggesting the need for a booster dose. Around 21% of the vaccinated HCWs with significantly low antibody titre were infected with the SARS-CoV-2, but a majority of them showed mild symptoms and recovered in home isolation without any O2 support. We noticed the effectiveness of the ChAdOx1 nCoV-19 vaccine as evident from the low rate of breakthrough infection with any severe symptoms.

Using nested case–control data from the Lifelines COVID-19 cohort, we undertook a validation study of a clinical and genetic model to predict the risk of severe COVID-19 in people with confirmed COVID-19 and in people with confirmed or self-reported COVID-19. The model performed well in terms of discrimination of cases and controls for all ages (area under the receiver operating characteristic curve (AUC) = 0.680 for confirmed COVID-19 and AUC = 0.689 for confirmed and self-reported COVID-19) and in the age group in which the model was developed (50 years and older; AUC = 0.658 for confirmed COVID-19 and AUC = 0.651 for confirmed and self-reported COVID-19). There was no evidence of over- or under-dispersion of risk scores but there was evidence of overall over-estimation of risk in all analyses (all P < 0.0001). In the light of large numbers of people worldwide remaining unvaccinated and continuing uncertainty regarding vaccine efficacy over time and against variants of concern, identification of people at high risk of severe COVID-19 may encourage the uptake of vaccinations (including boosters) and the use of non-pharmaceutical inventions.

We welcome our new editors and provide background on an unusual duo of articles in this issue.

Male sex is associated with higher risk of both colonisation and infection with Staphylococcus aureus (S. aureus). However, the role of sex-steroids in colonisation among men is largely unknown. Thus, the aim of this study was to investigate possible associations between circulating sex-steroids and nasal carriage of S. aureus in a general male population. The population-based Tromsø6 study (2007–2008) included 752 males aged 31–87 years with serum sex-steroids measured by liquid chromatography tandem mass spectrometry and two nasal swab samples for the assessment of S. aureus carriage. Multivariable logistic regression models were used to study the association between sex-steroid concentrations and S. aureus persistent nasal carriage (two positive swabs vs. others), while adjusting for potential confounding factors.

S. aureus persistent nasal carriage prevalence was 32%. Among men aged 55 years and above (median age 65 years), there was an inverse dose-response relationship between serum concentration of testosterone and persistent nasal carriage, and carriers had significantly lower mean levels of testosterone (P = 0.028, OR = 0.94 per nmol/l change in testosterone; 95% CI = 0.90–0.98). This association was attenuated when adjusting for body mass index and age (OR = 0.96 per nmol/l change in testosterone; 95% CI = 0.91–1.01). There was no association in the total population. This large population-based study suggests that testosterone levels may be inversely related to S. aureus persistent nasal carriage in older men. Future studies addressing biological mechanisms underlying the male predisposition to S. aureus colonisation and infection may foster preventive interventions that take sex-differences into account.

In urban systems, there is an interdependency between neighborhood roles and transportation patterns between neighborhoods. In this paper, we classify docking stations in bicycle-sharing networks to gain insight into the human mobility patterns of three major cities in the United States. We propose novel time-dependent stochastic block models, with degree-heterogeneous blocks and either mixed or discrete block membership, which classify nodes based on their time-dependent activity patterns. We apply these models to (1) detect the roles of bicycle-sharing stations and (2) describe the traffic within and between blocks of stations over the course of a day. Our models successfully uncover work blocks, home blocks, and other blocks; they also reveal activity patterns that are specific to each city. Our work gives insights for the design and maintenance of bicycle-sharing systems, and it contributes new methodology for community detection in temporal and multilayer networks with heterogeneous degrees.

The coronavirus disease 2019 (COVID-19) pandemic had an uneven development in different countries. In Argentina, the pandemic began in March 2020 and, during the first 3 months, the vast majority of cases were concentrated in a densely populated region that includes the city of Buenos Aires (country capital) and the Greater Buenos Aires (GBA) area that surrounds it. This work focuses on the spread of COVID-19 between June and November 2020 in GBA. Within this period of time there was no vaccine, basically only the early wild strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was present, and the official restriction and distancing measures in this region remained more or less constant. Under these particular conditions, the incidences show a sharp rise from June 2020 and begin to decrease towards the end of August until the end of November 2020. In this work we study, through mathematical modelling and available epidemiological information, the spread of COVID-19 in this region and period of time. We show that a coherent explanation of the evolution of incidences can be obtained assuming that only a minority fraction of the population got involved in the spread process, so that the incidences decreased as this group of people was becoming immune. The observed evolution of the incidences could then be a consequence at the population level of lasting immunity conferred by SARS-CoV-2.