Epidemic forecasting provides an opportunity to predict geographic disease spread and counts when an outbreak occurs and plays a key role in preventing or controlling their adverse impact. However, conventional prediction models based on complex mathematical modelling rely on the estimation of model parameters, which yields unreliable and unsustainable results. Herein, we proposed a simple model for predicting the epidemic transmission dynamics based on nonlinear regression of the epidemic growth rate and iterative methods, which is applicable to the progression of the COVID-19 outbreak under the strict control measures of the Chinese government. Our model yields reliable and accurate results as confirmed by the available data: we predicted that the total number of infections in mainland China would be 91 253, and the maximum number of beds required for hospitalised patients would be 62 794. We inferred that the inflection point (when the growth rate turns from positive to negative) of the epidemic across China would be mid-February, and the end of the epidemic would be in late March. This model is expected to contribute to resource allocation and planning in the health sector while providing a theoretical basis for governments to respond to future global health crises or epidemics.

To understand the characteristics and influencing factors related to cluster infections in Jiangsu Province, China, we investigated case reports to explore transmission dynamics and influencing factors of scales of cluster infection. The effectiveness of interventions was assessed by changes in the time-dependent reproductive number (Rt). From 25th January to 29th February, Jiangsu Province reported a total of 134 clusters involving 617 cases. Household clusters accounted for 79.85% of the total. The time interval from onset to report of index cases was 8 days, which was longer than that of secondary cases (4 days) (χ2 = 22.763, P < 0.001) and had a relationship with the number of secondary cases (the correlation coefficient (r) = 0.193, P = 0.040). The average interval from onset to report was different between family cluster cases (4 days) and community cluster cases (7 days) (χ2 = 28.072, P < 0.001). The average time interval from onset to isolation of patients with secondary infection (5 days) was longer than that of patients without secondary infection (3 days) (F = 9.761, P = 0.002). Asymptomatic patients and non-familial clusters had impacts on the size of the clusters. The average reduction in the Rt value in family clusters (26.00%, 0.26 ± 0.22) was lower than that in other clusters (37.00%, 0.37 ± 0.26) (F = 4.400, P = 0.039). Early detection of asymptomatic patients and early reports of non-family clusters can effectively weaken cluster infections.

Healthcare staff have been at the centre of the fight against the COVID-19 pandemic, facing diverse work-related stressors. Building upon studies from various countries, we aimed to investigate (1) the prevalence of various work-related stressors among healthcare professionals in Germany specific to the COVID-19 pandemic, (2) the psychological effects of these stressors in terms of clinical symptoms, and (3) the healthcare professionals' help-seeking behaviour. To this end, N = 300 healthcare professionals completed an online survey including the ICD-10 Symptom Rating checklist (ISR), event-sampling questions on pandemic-related stressors and self-formulated questions on help-seeking behaviour. Participants were recruited between 22 May and 22 July 2020. Findings were analysed using t tests, regressions and comparisons to large clinical and non-clinical samples assessed before and during the pandemic. Results show that healthcare professionals were most affected by protective measures at their workplace and changes in work procedures. Psychological symptoms, particularly anxiety and depression, were significantly more severe than in a non-clinical pre-pandemic sample and in the general population during the pandemic. At the same time, most professionals indicated that they would not seek help for psychological concerns. These findings indicate that healthcare employers need to pay greater attention to the mental health of their staff.

During a disease outbreak, healthcare workers (HCWs) are essential to treat infected individuals. However, these HCWs are themselves susceptible to contracting the disease. As more HCWs get infected, fewer are available to provide care for others, and the overall quality of care available to infected individuals declines. This depletion of HCWs may contribute to the epidemic's severity. To examine this issue, we explicitly model declining quality of care in four differential equation-based susceptible, infected and recovered-type models with vaccination. We assume that vaccination, recovery and survival rates are affected by quality of care delivered. We show that explicitly modelling HCWs and accounting for declining quality of care significantly alters model-predicted disease outcomes, specifically case counts and mortality. Models neglecting the decline of quality of care resulting from infection of HCWs may significantly under-estimate cases and mortality. These models may be useful to inform health policy that may differ for HCWs and the general population. Models accounting for declining quality of care may therefore improve the management interventions considered to mitigate the effects of a future outbreak.

Time variation is a fundamental problem in statistical and econometric analysis of macroeconomic and financial data. Recently, there has been considerable focus on developing econometric modelling that enables stochastic structural change in model parameters and on model estimation by Bayesian or nonparametric kernel methods. In the context of the estimation of covariance matrices of large dimensional panels, such data requires taking into account time variation, possible dependence and heavy-tailed distributions. In this paper, we introduce a nonparametric version of regularization techniques for sparse large covariance matrices, developed by Bickel and Levina (2008) and others. We focus on the robustness of such a procedure to time variation, dependence and heavy-tailedness of distributions. The paper includes a set of results on Bernstein type inequalities for dependent unbounded variables which are expected to be applicable in econometric analysis beyond estimation of large covariance matrices. We discuss the utility of the robust thresholding method, comparing it with other estimators in simulations and an empirical application on the design of minimum variance portfolios.

Laboratory data increasingly suggest that Salmonella infection may contribute to colon cancer (CC) development. Here, we examined epidemiologically the potential risk of CC associated with salmonellosis in the human population. We conducted a population-based cohort study using four health registries in Denmark. Person-level demographic data of all residents were linked to laboratory-confirmed non-typhoidal salmonellosis and to CC diagnoses in 1994–2016. Hazard ratios (HRs) for CC (overall/proximal/distal) associated with reported salmonellosis were estimated using Cox proportional hazard models. Potential effects of serovar, age, sex, inflammatory bowel disease and follow-up time post-infection were also assessed. We found no increased risk of CC ≥1 year post-infection (HR 0.99; 95% confidence interval (CI) 0.88–1.13). When stratifying by serovar, there was a significantly increased risk of proximal CC ≥1 year post-infection with serovars other than Enteritidis and Typhimurium (HR 1.40; 95% CI 1.03–1.90). CC risk was significantly increased in the first year post-infection (HR 2.08; 95% CI 1.48–2.93). The association between salmonellosis and CC in the first year post-infection can be explained by increased stool testing around the time of CC diagnosis. The association between proximal CC and non-Enteritidis/non-Typhimurium serovars is unclear and warrants further investigation. Overall, this study provides epidemiological evidence that notified Salmonella infections do not contribute significantly to CC risk in the studied population.

Reliability properties associated with the classic models of systems with age replacement have been a usual topic of research. Most previous works have checked the aging properties of the lifetime of the working units using stochastic comparisons among the systems with age replacement at different times. However, from a practical point of view, it would also be interesting to deduce to which aging classes the lifetime of the system belongs, making use of the aging properties of the lifetime of its working units. The first part of this article deals with this problem. Further along, stochastic orderings are established between the systems with replacement at the same time using several stochastic comparisons among the lifetimes of their working units. In addition, the lifetimes of two systems with age replacement are compared as well. This is performed assuming stochastic orderings between the number of replacement until failure, and the lifetimes of their working units conditioned to be less or equal than the replacement time. Similar comparisons are accomplished considering two systems with age replacement where the replacements occur at a random time. Illustrative examples are presented throughout the paper.

Independent data stewardship remains a core component of good data governance practice. Yet, there is a need for more robust independent data stewardship models that are able to oversee data-driven, multi-party data sharing, usage and re-usage, which can better incorporate citizen representation, especially in relation to personal data. We propose that data foundations—inspired by Channel Islands’ foundations laws—provide a workable model for good data governance not only in the Channel Islands, but also elsewhere. A key advantage of this model—in addition to leveraging existing legislation and building on established precedent—is the statutory role of the guardian that is a unique requirement in the Channel Islands, and when interpreted in a data governance model provides the independent data steward. The principal purpose for this paper, therefore, is to demonstrate why data foundations are well suited to the needs of data sharing initiatives. We further examine how data foundations could be established in practice—and provide key design principles that should be used to guide the design and development of any data foundation.

Vaccination remains the best strategy to reduce invasive meningococcal disease. This study evaluated an investigational tetanus toxoid-conjugate quadrivalent meningococcal vaccine (MenACYW-TT) vs. a licensed tetanus toxoid-conjugate quadrivalent meningococcal vaccine (MCV4-TT) (NCT02955797). Healthy toddlers aged 12–23 months were included if they were either meningococcal vaccine-naïve or MenC conjugate (MCC) vaccine-primed (≥1 dose of MCC prior to 12 months of age). Vaccine-naïve participants were randomised 1:1 to either MenACYW-TT (n = 306) or MCV4-TT (n = 306). MCC-primed participants were randomised 2:1 to MenACYW-TT (n = 203) or MCV4-TT (n = 103). Antibody titres against each of the four meningococcal serogroups were measured by serum bactericidal antibody assay using the human complement. The co-primary objectives of this study were to demonstrate the non-inferiority of MenACYW-TT to MCV4-TT in terms of seroprotection (titres ≥1:8) at Day 30 in both vaccine-naïve and all participants (vaccine-naïve and MCC-primed groups pooled). The immune response for all four serogroups to MenACYW-TT was non-inferior to MCV4-TT in vaccine-naïve participants (seroprotection: range 83.6–99.3% and 81.4–91.6%, respectively) and all participants (seroprotection: range 83.6–99.3% and 81.4–98.0%, respectively). The safety profiles of both vaccines were comparable. MenACYW-TT was well-tolerated and demonstrated non-inferior immunogenicity when administered to MCC vaccine-primed and vaccine-naïve toddlers.

Seeds of Abrus precatorius L. (Fabaceae) were used as weight measure by Indigenous people. Where, the seeds were referred as Ratti; a traditional Indian unit of mass measurement. Seed weight fluctuates depending upon age, moisture, storage-period/conditions. Therefore, use of seeds as a weighing unit become dubious and need to be validated. For this purpose, seeds of A. precatorious were subjected to different moisture conditions and periodically monitored. Surprisingly, there was no change in seed weight was observed, indicating the impermeability of seed coat. The later was confirmed by scarification of seed coat which resulted in 53% increase in seed weight against 0% in control. Further, presence of a potent toxin (abrin) in the seed coat protects it from pests and microbes, and contributes to the maintenance of impermeability for longer period of time. The data validates the use of A. precatorious seeds as a weighing unit (ratti) by the indigenous people and discussed.

We consider conditions for strict stationarity and ergodicity of a class of multivariate BEKK processes $(X_t : t=1,2,\ldots )$ and study the tail behavior of the associated stationary distributions. Specifically, we consider a class of BEKK-ARCH processes where the innovations are assumed to be Gaussian and a finite number of lagged $X_t$’s may load into the conditional covariance matrix of $X_t$. By exploiting that the processes have multivariate stochastic recurrence equation representations, we show the existence of strictly stationary solutions under mild conditions, where only a fractional moment of $X_t$ may be finite. Moreover, we show that each component of the BEKK processes is regularly varying with some tail index. In general, the tail index differs along the components, which contrasts with most of the existing literature on the tail behavior of multivariate GARCH processes. Lastly, in an empirical illustration of our theoretical results, we quantify the model-implied tail index of the daily returns on two cryptocurrencies.

An increase in oyster aquaculture as a sustainable method of shellfish production is one response to overharvest and degradation of natural oyster reefs over the past century. Successful aquaculture production requires determining the environmental conditions optimal for oyster growth. In this study, the salinity, temperature, chlorophyll a concentration and the growth of Crassostrea virginica were monitored at four locations within the Mission-Aransas Estuary, Texas (USA), a shallow subtropical estuary influenced by relatively low freshwater inflow. Mean growth of the oyster shell (0.205 mm d–1 and 0.203 g d–1) and soft tissues (3.447 mg d–1) was highest when salinity was low (mean = 15.5) and chlorophyll a concentration was high (8.4 μg l–1). Oyster growth also varied temporally with periods of spawning. In low-inflow estuaries such as the Mission-Aransas Estuary, oyster farms should be sited close to river mouths so that oysters can benefit from freshwater inflows and lower salinities.

People living in urban slums or informal settlements are among the most vulnerable communities, highly susceptible to coronavirus disease 2019 (COVID-19) infection and vulnerable to the consequences of the measures taken to control the spread of the virus. Fear and stigma related to infection, mistrust between officials and the population, the often-asymptomatic nature of the disease is likely to lead to under-reporting. We conducted a cross-sectional study to determine the seroprevalence of COVID-19 infection in a large slum in South India 3 months after the index case and recruited 499 adults (age >18 years). The majority (74.3%) were females and about one-third of the population reported comorbidities. The overall seroprevalence of IgG antibody for COVID-19 was 57.9% (95% CI 53.4–62.3). Age, education, occupation and the presence of reported comorbidities were not associated with seroprevalence (P-value >0.05). Case-to-undetected-infections ratio was 1:195 and infection fatality rate was calculated as 2.94 per 10 000 infections. We estimated seroprevalence of COVID-19 was very high in our study population. The focus in this slum should shift from infection prevention to managing the indirect consequences of the pandemic. We recommend seroprevalence studies in such settings before vaccination to identify the vulnerability of COVID-19 infection to optimise the use of insufficient resources. It is a wake-up call to societies and nations, to dedicate paramount attention to slums into recovery and beyond – to build, restore and maintain health equity for the ‘Health and wellbeing of all’.

We discuss the existence and uniqueness of stationary and ergodic nonlinear autoregressive processes when exogenous regressors are incorporated into the dynamic. To this end, we consider the convergence of the backward iterations of dependent random maps. In particular, we give a new result when the classical condition of contraction on average is replaced with a contraction in conditional expectation. Under some conditions, we also discuss the dependence properties of these processes using the functional dependence measure of Wu (2005, Proceedings of the National Academy of Sciences 102, 14150–14154) that delivers a central limit theorem giving a wide range of applications. Our results are illustrated with conditional heteroscedastic autoregressive nonlinear models, Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) processes, count time series, binary choice models, and categorical time series for which we provide many extensions of existing results.

Environmental gains of electric cars can be optimized with the use of lightweight and recyclable magnesium in the vehicle’s structural components. Ductility improvement of low-density Mg-Al alloys will extend their use in automotive body applications. The authors achieved 63% ductility improvement in Mg-6wt%Al with trace Y (1.5 ppm) due to the β-phase refinement and predicted that higher levels would not perform as well. As predicted, 0.3wt% of Y addition investigated in this study led to lower mechanical performance and β-phase refinement than those obtained with trace additions. The tensile ductility and yield strength increased by ~13% and 16%, respectively, and the compression strain to fracture by ~22%. Scanning electron and optical microscopy, X-Rays diffraction, mechanical testing and thermodynamic calculations were used to investigate the effect of 0.3wt% Y on the microstructure of Mg-6wt%Al. The matrix dissolution revealed the close association of the Al2Y and the β-Mg17Al12 phases.

Ever since the World Health Organization (WHO) declared the new coronavirus disease 2019 (COVID-19) as a pandemic, there has been a public health debate concerning medical resources and supplies including hospital beds, intensive care units (ICU), ventilators and protective personal equipment (PPE). Forecasting COVID-19 dissemination has played a key role in informing healthcare professionals and governments on how to manage overburdened healthcare systems. However, forecasting during the pandemic remained challenging and sometimes highly controversial. Here, we highlight this challenge by performing a comparative evaluation for the estimations obtained from three COVID-19 surge calculators under different social distancing approaches, taking Lebanon as a case study. Despite discrepancies in estimations, the three surge calculators used herein agree that there will be a relative shortage in the capacity of medical resources and a significant surge in PPE demand if the social distancing policy is removed. Our results underscore the importance of implementing containment interventions including social distancing in alleviating the demand for medical care during the COVID-19 pandemic in the absence of any medication or vaccine. The paper also highlights the value of employing several models in surge planning.

Gene methylation is one means of controlling tissue gene expression, but it is unknown what pathways influencing Alzheimer’s disease (AD) are controlled this way. We compared normal and AD brain tissue data for gene expression (mRNAs) and gene methylation profiling. We identified methylated differentially expressed genes (MDEGs). Protein-protein interaction (PPI) of the MDEGs showed 18 hypermethylated low-expressed genes (Hyper-LGs) involved in cell signaling and metabolism; also 10 hypomethylated highly expressed (Hypo-HGs) were involved in regulation of transcription and development. Molecular pathways enriched in Hyper-LGs included neuroactive ligand-receptor interaction pathways. Hypo-HGs were notably enriched in pathways including hippo signaling. PPI analysis also identified both Hyper-LGs and Hypo-HGs, as hub proteins. Our analysis of AD datasets identified Hyper-LGs, Hypo-HGs, and transcription factors linked to these genes. These pathways, which may participate in Alzheimer’s disease development, may be affected by treatments that influence gene methylation patterns.