This study investigated an outbreak in a kindergarten in Wuyi County of acute gastroenteritis concerning a large number of students and teachers. We performed a case-control study, and collected information on the layout of the school, symptoms, onset time of all cases and vomiting sites. A total of 62 individuals fit the definition of probable cases; among these, there were 19 cases of laboratory-confirmed norovirus infection. Nausea and vomiting were the most common symptoms in the outbreak. Seven student norovirus patients vomited in the school. The odds ratio (OR) of norovirus illness was 15.75 times higher among teachers who handled or interacted with student vomitus without respiratory protection than compared to those without this type of exposure (OR 15.75, 95% CI 1.75–141.40). Nine samples were successfully genotyped; eight samples were norovirus GII.2[P16], one sample was norovirus GII.4 Sydney[P16]. This study revealed that improper handling of vomitus is a risk factor of norovirus infection. Therefore, more attention should be given to train school staff in knowledge of disinfection.

This study is performed to figure out how the presence of diabetes affects the infection, progression and prognosis of 2019 novel coronavirus disease (COVID-19), and the effective therapy that can treat the diabetes-complicated patients with COVID-19. A multicentre study was performed in four hospitals. COVID-19 patients with diabetes mellitus (DM) or hyperglycaemia were compared with those without these conditions and matched by propensity score matching for their clinical progress and outcome. Totally, 2444 confirmed COVID-19 patients were recruited, from whom 336 had DM. Compared to 1344 non-DM patients with age and sex matched, DM-COVID-19 patients had significantly higher rates of intensive care unit entrance (12.43% vs. 6.58%, P = 0.014), kidney failure (9.20% vs. 4.05%, P = 0.027) and mortality (25.00% vs. 18.15%, P < 0.001). Age and sex-stratified comparison revealed increased susceptibility to COVID-19 only from females with DM. For either non-DM or DM group, hyperglycaemia was associated with adverse outcomes, featured by higher rates of severe pneumonia and mortality, in comparison with non-hyperglycaemia. This was accompanied by significantly altered laboratory indicators including lymphocyte and neutrophil percentage, C-reactive protein and urea nitrogen level, all with correlation coefficients >0.35. Both diabetes and hyperglycaemia were independently associated with adverse prognosis of COVID-19, with hazard ratios of 10.41 and 3.58, respectively.

Lockdowns have been a core infection control measure in many countries during the coronavirus disease 2019 (COVID-19) pandemic. In England's first lockdown, children of single parent households (SPHs) were permitted to move between parental homes. By the second lockdown, SPH support bubbles between households were also permitted, enabling larger within-household networks. We investigated the combined impact of these approaches on household transmission dynamics, to inform policymaking for control and support mechanisms in a respiratory pandemic context. This network modelling study applied percolation theory to a base model of SPHs constructed using population survey estimates of SPH family size. To explore putative impact, varying estimates were applied regarding extent of bubbling and proportion of different-parentage within SPHs (DSPHs) (in which children do not share both the same parents). Results indicate that the formation of giant components (in which COVID-19 household transmission accelerates) are more contingent on DSPHs than on formation of bubbles between SPHs, and that bubbling with another SPH will accelerate giant component formation where one or both are DSPHs. Public health guidance should include supportive measures that mitigate the increased transmission risk afforded by support bubbling among DSPHs. Future network, mathematical and epidemiological studies should examine both independent and combined impact of policies.

Corrosion is an important problem that engineers and scientists must overcome to avoid the collapse of structures, chemical processing plants, and metallic objects, which can lead to not only economic loss but also environmental and human losses. One of the simplest and most widely used methods to quantify corrosion rates (CRs) is the immersion test. The usual approach that has been used to date, to quantify the CR by this method, is to assume that the initial surface area of the corroding object remains constant over time. It is shown that such approximations underestimate the true CR and that they may lead to significant errors. A formula to calculate the CR considering changes in the area is presented in this work. The formula herein can be used to accurately quantify the CR by the immersion test and improve the quality of experimental data and the analysis and modeling of corrosion phenomena.

Reduced-order models (ROMs) are computationally inexpensive simplifications of high-fidelity complex ones. Such models can be found in computational fluid dynamics where they can be used to predict the characteristics of multiphase flows. In previous work, we presented a ROM analysis framework that coupled compression techniques, such as autoencoders, with Gaussian process regression in the latent space. This pairing has significant advantages over the standard encoding–decoding routine, such as the ability to interpolate or extrapolate in the initial conditions’ space, which can provide predictions even when simulation data are not available. In this work, we focus on this major advantage and show its effectiveness by performing the pipeline on three multiphase flow applications. We also extend the methodology by using deep Gaussian processes as the interpolation algorithm and compare the performance of our two variations, as well as another variation from the literature that uses long short-term memory networks, for the interpolation.

We show that the diameter of a uniformly drawn spanning tree of a simple connected graph on n vertices with minimal degree linear in n is typically of order $\sqrt{n}$. A byproduct of our proof, which is of independent interest, is that on such graphs the Cheeger constant and the spectral gap are comparable.

We investigated risk factors associated with COVID-19 by conducting a retrospective, frequency-matched case-control study, with three sampling periods (August–October 2020). We compared cases completing routine contact tracing to asymptomatic population controls. Multivariable analyses estimated adjusted odds ratios (aORs) for non-household community settings. Meta-analyses using random effects provided pooled odds ratios (pORs). Working in healthcare (pOR 2.87; aORs 2.72, 2.81, 3.08, for study periods 1–3 respectively), social care (pOR 4.15; aORs 2.46, 5.06, 5.41, for study periods 1–3 respectively) or hospitality (pOR 2.36; aORs 2.01, 2.54, 2.63, for study periods 1–3 respectively) were associated with increased odds of being a COVID-19 case. Additionally, working in bars, pubs and restaurants, warehouse settings, construction, educational settings were significantly associated. While definitively determining where transmission occurs is impossible, we provide evidence that in certain sectors, the impact of mitigation measures may only be partial and reinforcement of measures should be considered in these settings.

This paper presents a method used to rapidly assess the incursion and the establishment of community transmission of suspected SARS-CoV-2 variant of concern Delta (lineage B.1.617.2) into the UK in April and May 2021. The method described is independent of any genetically sequenced data, and so avoids the inherent lag times involved in sequencing of cases. We show that, between 1 April and 12 May 2021, there was a strong correlation between local authorities with high numbers of imported positive cases from India and high COVID-19 case rates, and that this relationship holds as we look at finer geographic detail. Further, we also show that Bolton was an outlier in the relationship, having the highest COVID-19 case rates despite relatively few importations. We use an artificial neural network trained on demographic data, to show that observed importations in Bolton were consistent with similar areas. Finally, using an SEIR transmission model, we show that imported positive cases were a contributing factor to persistent transmission in a number of local authorities, however they could not account for increased case rates observed in Bolton. As such, the outbreak of Delta variant in Bolton was likely not a result of direct importation from overseas, but rather secondary transmission from other regions within the UK.