Coronavirus disease-2019 precipitated the rapid deployment of novel therapeutics, which led to operational and logistical challenges for healthcare organizations. Four health systems participated in a qualitative study to abstract lessons learned, challenges, and promising practices from implementing neutralizing monoclonal antibody (nMAb) treatment programs. Lessons are summarized under three themes that serve as critical building blocks for health systems to rapidly deploy novel therapeutics during a pandemic: (1) clinical workflows, (2) data infrastructure and platforms, and (3) governance and policy. Health systems must be sufficiently agile to quickly scale programs and resources in times of uncertainty. Real-time monitoring of programs, policies, and processes can help support better planning and improve program effectiveness. The lessons and promising practices shared in this study can be applied by health systems for distribution of novel therapeutics beyond nMAbs and toward future pandemics and public health emergencies.

This study investigated cases of pregnancy-related listeriosis in British Columbia (BC), Canada, from 2005 to 2014. We described all diagnosed cases in pregnant women (n = 15) and neonates (n = 7), estimated the excess healthcare costs associated with listeriosis, and calculated the fraction of stillbirths attributable to listeriosis, and mask cell sizes 1–5 due to data requirements. Pregnant women had a median gestational age of 31 weeks at listeriosis onset (range: 20–39) and on average delivered at a median of 37 weeks gestation (range: 20–40). Neonates experienced complications but no fatalities. Stillbirths occurred in 1–5 of 15 pregnant women with listeriosis, and very few (0.05–0.24%) of the 2,088 stillbirths in BC in the 10 years were attributed to listeriosis (exact numbers masked). Pregnant women and neonates with listeriosis had significantly more hospital visits, days in the hospital and physician visits than those without listeriosis. Pregnant women with listeriosis had 2.59 times higher mean total healthcare costs during their pregnancy, and neonates with listeriosis had 9.85 times higher mean total healthcare costs during their neonatal period, adjusting for various factors. Despite small case numbers and no reported deaths, these results highlight the substantial additional health service use and costs associated with individual cases of pregnancy-related listeriosis in BC.

In this short note, we show that every convex, order-bounded above functional on a Fréchet lattice is automatically continuous. This improves a result in Ruszczyński and Shapiro ((2006) Mathematics of Operations Research 31(3), 433–452.) and applies to many deviation and variability measures. We also show that an order-continuous, law-invariant functional on an Orlicz space is strongly consistent everywhere, extending a result in Krätschmer et al. ((2017) Finance and Stochastics 18(2), 271–295.).

Buildings employ an ensemble of technical systems like those for heating and ventilation. Ontologies such as Brick, IFC, SSN/SOSA, and SAREF have been created to describe such technical systems in a machine-understandable manner. However, these focus on describing system topology, whereas several relevant use cases (e.g., automated fault detection and diagnostics (AFDD)) also need knowledge about the physical processes. While mathematical simulation can be used to model physical processes, these are practically expensive to run and are not integrated with mainstream technical systems ontologies today. We propose to describe the effect of component actuation on underlying physical mechanisms within component stereotypes. These stereotypes are linked to actual component instances in the technical system description, thereby accomplishing an integration of knowledge about system structure and physical processes. We contribute an ontology for such stereotypes and show that it covers 100% of Brick heating, ventilation, and air-conditioning (HVAC) components. We further show that the ontology enables automatically inferring relationships between components in a real-world building in most cases, except in two situations where component dependencies are underreported. This is due to missing component models for passive parts like splits and join in ducts, and hence points at concrete future extensions of the Brick ontology. Finally, we demonstrate how AFDD applications can utilize the resulting knowledge graph to find expected consequences of an action, or conversely, to identify components that may be responsible for an observed state of the process.

This study explored the effect of SARS-CoV-2 infection and COVID-19 vaccination during pregnancy on neonatal outcomes among women from the general Dutch population. VASCO is an ongoing prospective cohort study aimed at assessing vaccine effectiveness of COVID-19 vaccination. Pregnancy status was reported at baseline and through regular follow-up questionnaires. As an extension to the main study, all female participants who reported to have been pregnant between enrolment (May–December 2021) and January 2023 were requested to complete an additional questionnaire on neonatal outcomes. Multivariable linear and logistic regression analyses were used to determine the associations between self-reported SARS-CoV-2 infection or COVID-19 vaccination during pregnancy and neonatal outcomes, adjusted for age, educational level, and presence of a medical risk condition. Infection analyses were additionally adjusted for COVID-19 vaccination before and during pregnancy, and vaccination analyses for SARS-CoV-2 infection before and during pregnancy. Of 312 eligible participants, 232 (74%) completed the questionnaire. In total, 196 COVID-19 vaccinations and 115 SARS-CoV-2 infections during pregnancy were reported. Infections were mostly first infections (86; 75%), caused by the Omicron variant (95; 83%), in women who had received ≥1 vaccination prior to infection (101; 88%). SARS-CoV-2 infection during pregnancy was not significantly associated with gestational age (β = 1.7; 95%CI: −1.6–5.0), birth weight (β = 82; −59 to 223), Apgar score <9 (odds ratio (OR): 1.3; 0.6–2.9), postpartum hospital stay (OR: 1.0; 0.6–1.8), or neonatal intensive care unit admission (OR: 0.8; 0.2–3.2). COVID-19 vaccination during pregnancy was not significantly associated with gestational age (β = −0.4; −4.0 to 3.2), birth weight (β = 88; −64 to 240), Apgar score <9 (OR: 0.9; 0.4–2.3), postpartum hospital stay (OR: 0.9; 0.5–1.7), or neonatal intensive care unit admission (OR: 1.6; 0.4–8.6). In conclusion, this study did not find an effect of SARS-CoV-2 infection or COVID-19 vaccination during pregnancy on any of the studied neonatal outcomes among a general Dutch, largely vaccinated, population. Together with data from other studies, this supports the safety of COVID-19 vaccination during pregnancy.

We prove a new lower bound for the almost 20-year-old problem of determining the smallest possible size of an essential cover of the $n$-dimensional hypercube $\{\pm 1\}^n$, that is, the smallest possible size of a collection of hyperplanes that forms a minimal cover of $\{\pm 1\}^n$ and such that, furthermore, every variable appears with a non-zero coefficient in at least one of the hyperplane equations. We show that such an essential cover must consist of at least $10^{-2}\cdot n^{2/3}/(\log n)^{2/3}$ hyperplanes, improving previous lower bounds of Linial–Radhakrishnan, of Yehuda–Yehudayoff, and of Araujo–Balogh–Mattos.

We provide explicit small-time formulae for the at-the-money implied volatility, skew, and curvature in a large class of models, including rough volatility models and their multi-factor versions. Our general setup encompasses both European options on a stock and VIX options, thereby providing new insights on their joint calibration. The tools used are essentially based on Malliavin calculus for Gaussian processes. We develop a detailed theoretical and numerical analysis of the two-factor rough Bergomi model and provide insights on the interplay between the different parameters for joint SPX–VIX smile calibration.

The performance and confidence in fault detection and diagnostic systems can be undermined by data pipelines that feature multiple compounding sources of uncertainty. These issues further inhibit the deployment of data-based analytics in industry, where variable data quality and lack of confidence in model outputs are already barriers to their adoption. The methodology proposed in this paper supports trustworthy data pipeline design and leverages knowledge gained from one fully-observed data pipeline to a similar, under-observed case. The transfer of uncertainties provides insight into uncertainty drivers without repeating the computational or cost overhead of fully redesigning the pipeline. A SHAP-based human-readable explainable AI (XAI) framework was used to rank and explain the impact of each choice in a data pipeline, allowing the decoupling of positive and negative performance drivers to facilitate the successful selection of highly-performing pipelines. This empirical approach is demonstrated in bearing fault classification case studies using well-understood open-source data.

Our study aim was to identify high-risk areas of neonatal mortality associated with bacterial sepsis in the state of São Paulo, Southeast Brazil. We used a population-based study applying retrospective spatial scan statistics with data extracted from birth certificates linked to death certificates. All live births from mothers residing in São Paulo State from 2004 to 2020 were included. Spatial analysis using the Poisson model was adopted to scan high-rate clusters of neonatal mortality associated with bacterial sepsis (WHO-ICD10 A32.7, A40, A41, P36, P37.2 in any line of the death certificate). We found a prevalence of neonatal death associated with bacterial sepsis of 2.3/1000 live births. Clusters of high neonatal mortality associated with bacterial sepsis were identified mainly in the southeast region of the state, with four of them appearing as cluster areas for all birth weight categories (<1500 g, 1500 to <2500 g and ≥ 2500 g). The spatial analysis according to the birth weight showed some overlapping in the detected clusters, suggesting shared risk factors that need to be explored. Our study highlights the ongoing challenge of neonatal sepsis in the most developed state of a middle-income country and the importance of employing statistical techniques, including spatial methods, for enhancing surveillance and intervention strategies.

In our digitalized modern society where cyber-physical systems and internet-of-things (IoT) devices are increasingly commonplace, it is paramount that we are able to assure the cybersecurity of the systems that we rely on. As a fundamental policy, we join the advocates of multilayered cybersecurity measures, where resilience is built into IoT systems by relying on multiple defensive techniques. While existing legislation such as the General Data Protection Regulation (GDPR) also takes this stance, the technical implementation of these measures is left open. This invites research into the landscape of multilayered defensive measures, and within this problem space, we focus on two defensive measures: obfuscation and diversification. In this study, through a literature review, we situate these measures within the broader IoT cybersecurity landscape and show how they operate with other security measures built on the network and within IoT devices themselves. Our findings highlight that obfuscation and diversification show promise in contributing to a cost-effective robust cybersecurity ecosystem in today’s diverse cyber threat landscape.