We report a foodborne outbreak of the previously undetected Cryptosporidium parvum gp60 subtype IIγA11. In December 2023, notifications of cryptosporidiosis cases increased in Sweden, prompting the initiation of a national outbreak investigation, and a case–control study was performed to identify the source. We identified 60 cases between 15 December 2023 and 1 January 2024. The median age was 44 years (range: 16–81), and 73% were women. Controls were recruited from a national random pool; frequency was matched by age group and sex. Compared to controls, cases were more likely to have consumed items from salad bars in grocery stores (8% vs. 85%; adjusted odds ratios [aOR]: 58; 95% confidence interval [CI]: 22–186). In regards to food items from the salad bars, cases were more likely to have consumed kale mix salad compared to controls (62% vs. 32%; aOR: 3.6; 95%CI: 1.2–12). Trace-back investigations identified kale producers from Sweden, Belgium, and Spain, but no particular grower was identified, and no food samples were available for microbiological analysis. Our investigation indicates that leafy greens such as kale may contain Cryptosporidium spp. and cause outbreaks and it is important to understand how the contamination occurs to prevent future outbreaks and apply adequate preventive measures.

Predicting epidemic trends of coronavirus disease 2019 (COVID-19) remains a key public health concern globally today. However, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection rate in previous studies of the transmission dynamics model was mostly a fixed value. Therefore, we proposed a meta-Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) model by adding a time-varying SARS-CoV-2 reinfection rate to the transmission dynamics model to more accurately characterize the changes in the number of infected persons. The time-varying reinfection rate was estimated using random-effect multivariate meta-regression based on published literature reports of SARS-CoV-2 reinfection rates. The meta-SEIRS model was constructed to predict the epidemic trend of COVID-19 from February to December 2023 in Sichuan province. Finally, according to the online questionnaire survey, the SARS-CoV-2 infection rate at the end of December 2022 in Sichuan province was 82.45%. The time-varying effective reproduction number in Sichuan province had two peaks from July to December 2022, with a maximum peak value of about 15. The prediction results based on the meta-SEIRS model showed that the highest peak of the second wave of COVID-19 in Sichuan province would be in late May 2023. The number of new infections per day at the peak would be up to 2.6 million. We constructed a meta-SEIRS model to predict the epidemic trend of COVID-19 in Sichuan province, which was consistent with the trend of SARS-CoV-2 positivity in China. Therefore, a meta-SEIRS model parameterized based on evidence-based data can be more relevant to the actual situation and thus more accurately predict future trends in the number of infections.

Residual blood specimens provide a sample repository that could be analyzed to estimate and track changes in seroprevalence with fewer resources than household-based surveys. We conducted parallel facility and community-based cross-sectional serological surveys in two districts in India, Kanpur Nagar District, Uttar Pradesh, and Palghar District, Maharashtra, before and after a measles-rubella supplemental immunization activity (MR-SIA) from 2018 to 2019. Anonymized residual specimens from children 9 months to younger than 15 years of age were collected from public and private diagnostic laboratories and public hospitals and tested for IgG antibodies to measles and rubella viruses. Significant increases in seroprevalence were observed following the MR SIA using the facility-based specimens. Younger children whose specimens were tested at a public facility in Kanpur Nagar District had significantly lower rubella seroprevalence prior to the SIA compared to those attending a private hospital, but this difference was not observed following the SIA. Similar increases in rubella seroprevalence were observed in facility-based and community-based serosurveys following the MR SIA, but trends in measles seroprevalence were inconsistent between the two specimen sources. Despite challenges with representativeness and limited metadata, residual specimens can be useful in estimating seroprevalence and assessing trends through facility-based sentinel surveillance.

Since the beginning of mass vaccination campaign for COVID-19 in Italy (December 2020) and following the rapidly increasing vaccine administration, sex differences have been emphasized. Nevertheless, incomplete and frequently incoherent sex-disaggregated data for COVID-19 vaccinations are currently available, and vaccines clinical studies generally do not include sex-specific analyses for safety and efficacy. We looked at sex variations in the COVID-19 vaccine’s effectiveness against infection and severe disease outcomes. We conducted a nationwide retrospective cohort study on Italian population, linking information on COVID-19 vaccine administrations obtained through the Italian National Vaccination Registry, with the COVID-19 integrated surveillance system, held by the Istituto Superiore di Sanità. The results showed that, in all age groups, vaccine effectiveness (VE) was higher in the time-interval ≤120 days post-vaccination. In terms of the sex difference in vaccination effectiveness, men and women were protected against serious illness by vaccination in a comparable way, while men were protected against infection to a somewhat greater extent than women. To fully understand the mechanisms underlying the sex difference in vaccine response and its consequences for vaccine effectiveness and development, further research is required. The sex-related analysis of vaccine response may contribute to adjust vaccination strategies, improving overall public health programmes.

By coupling long-range polymerase chain reaction, wastewater-based epidemiology, and pathogen sequencing, we show that adenovirus type 41 hexon-sequence lineages, described in children with hepatitis of unknown origin in the United States in 2021, were already circulating within the country in 2019. We also observed other lineages in the wastewater, whose complete genomes have yet to be documented from clinical samples.

The design of gas turbine combustors for optimal operation at different power ratings is a multifaceted engineering task, as it requires the consideration of several objectives that must be evaluated under different test conditions. We address this challenge by presenting a data-driven approach that uses multiple probabilistic surrogate models derived from Gaussian process regression to automatically select optimal combustor designs from a large parameter space, requiring only a few experimental data points. We present two strategies for surrogate model training that differ in terms of required experimental and computational efforts. Depending on the measurement time and cost for a target, one of the strategies may be preferred. We apply the methodology to train three surrogate models under operating conditions where the corresponding design objectives are critical: reduction of NOx emissions, prevention of lean flame extinction, and mitigation of thermoacoustic oscillations. Once trained, the models can be flexibly used for different forms of a posteriori design optimization, as we demonstrate in this study.

We consider the problem of parameter estimation for the superposition of square-root diffusions. We first derive the explicit formulas for the moments and auto-covariances based on which we develop our moment estimators. We then establish a central limit theorem for the estimators with the explicit formulas for the asymptotic covariance matrix. Finally, we conduct numerical experiments to validate our method.

Motivated by the impact of emerging technologies on (toll) parks, this paper studies a problem of customers’ strategic behavior, social optimization, and revenue maximization for infinite-server queues. More specifically, we assume that a customer’s utility consists of a positive reward for receiving service minus a cost caused by the other customers in the system. In the observable setting, we show the existence, uniqueness, and expressions of the individual equilibrium threshold, the socially optimal threshold, and the optimal revenue threshold, respectively. Then, we prove that the optimal revenue threshold is smaller than the socially optimal threshold, which is smaller than the individual one. Furthermore, we also extend the cost functions to any finite polynomial function with nonnegative coefficients. In the unobservable setting, we derive the joining probabilities of individual equilibrium and optimal revenue. Finally, using numerical experiments, we complement our results and compare the social welfare and the revenue under these two information levels.

In this paper, we define weighted failure rate and their means from the stand point of an application. We begin by emphasizing that the formation of n independent component series system having weighted failure rates with sum of weight functions being unity is same as a mixture of n distributions. We derive some parametric and non-parametric characterization results. We discuss on the form invariance property of baseline failure rate for a specific choice of weight function. Some bounds on means of aging functions are obtained. Here, we establish that weighted increasing failure rate average (IFRA) class is not closed under formation of coherent systems unlike the IFRA class. An interesting application of the present work is credited to the fact that the quantile version of means of failure rate is obtained as a special case of weighted means of failure rate.

This paper investigates the precise large deviations of the net loss process in a two-dimensional risk model with consistently varying tails and dependence structures, and gives some asymptotic formulas which hold uniformly for all x varying in t-intervals. The study is among the initial efforts to analyze potential risk via large deviation results for the net loss process of the two-dimensional risk model, and can provide a novel insight to assess the operation risk in a long run by fully considering the premium income factors of the insurance company.

In this paper, a new multivariate counting process model (called Multivariate Poisson Generalized Gamma Process) is developed and its main properties are studied. Some basic stochastic properties of the number of events in the new multivariate counting process are initially derived. It is shown that this new multivariate counting process model includes the multivariate generalized Pólya process as a special case. The dependence structure of the multivariate counting process model is discussed. Some results on multivariate stochastic comparisons are also obtained.

Aggregate implements an efficient fast Fourier transform (FFT)-based algorithm to approximate compound probability distributions. Leveraging FFT-based methods offers advantages over recursion and simulation-based approaches, providing speed and accuracy to otherwise time-consuming calculations. Combining user-friendly features and an expressive domain-specific language called DecL, Aggregate enables practitioners and nonprogrammers to work with complex distributions effortlessly. The software verifies the accuracy of its FFT-based numerical approximations by comparing their first three moments to those calculated analytically from the specified frequency and severity. This moment-based validation, combined with carefully chosen default parameters, allows users without in-depth knowledge of the underlying algorithm to be confident in the results. Aggregate supports a wide range of frequency and severity distributions, policy limits and deductibles, and reinsurance structures and has applications in pricing, reserving, risk management, teaching, and research. It is written in Python.